Abstract-Generative Artificial Intelligence (GenAI) has evolved rapidly, creating transformative opportunities across sectors, particularly in healthcare and marketing. Despite the promise of improved patient care, streamlined medical workflows, and enhanced customer engagement, GenAI faces significant challenges. Key obstacles include high computational costs, data-privacy concerns, and ethical accountability in content generation. Moreover, the open-source initiatives by leading firms like Meta have intensified competition, pushing GenAI models toward commoditization, impacting revenue structures and sparking a “race to the bottom” in pricing. The market is further complicated by monopolistic dependencies on critical hardware providers, particularly Nvidia, which dominate GPU supplies essential for AI training. With a rapidly growing market projected to reach trillions by 2030, the industry must navigate these barriers to realize the full potential of GenAI. This study explores GenAI’s current applications, fiscal and ethical challenges, and the strategic imperatives needed to foster sustainable, profitable growth within an increasingly crowded and commoditized industry landscape.

Abstract-In the era of digital transformation, organizations are increasingly leveraging data analytics to optimize marketing strategies and enhance customer engagement. Predicting customer performance is critical for businesses aiming to tailor marketing efforts, improve customer retention, and maximize revenue. This study presents a comprehensive data mining framework utilizing the Naive Bayes classifier to forecast customer performance based on historical behavior and interaction data. Employing Google Analytics as the primary data collection tool, we evaluate the model’s effectiveness by analyzing metrics such as accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and the area under the Receiver Operating Characteristic (ROC) curve. The results illustrate the framework’s potential to provide actionable insights into customer behavior, thereby facilitating more informed marketing strategies and decision-making processes.

Abstract-IIn the current times, conventional farming which is the most widely used type of farming has been affected by several problems such as decrease in the availability of space due to the increasing population, wastage of water, destruction of crops due to insects, rains, etc. Furthermore, in the future where the population is expected to grow further, these problems in farming can be disastrous as it can decrease the availability of food and can lead to the starvation of a big part of the population. Hydroponics which is another method of farming can be a solution to most of the problems associated with conventional farming. In this type of farming, crops are grown without the requirement of soil, instead it utilizes a growing medium and water is directly supplied to the roots of the plants. Further fertilizers are dissolved in the water itself. This type of farming can save a lot of space as the plants are grown in vertical slots and they can be stacked upon each other and water requirement is also very low for this type of farming as most of the water is recycled. In this paper, we are going to discuss the various factors which affect the growth rate of the plants in vertical farming. The plants we have taken are jalapeno plants. The trail period is of 7 weeks where we have compared different factors affecting the growth rate of the plants.

Abstract-Chatbots function as software that allows users to ask questions and receive assistance through appropriate responses. This paper explores an AI-based chatbot designed specifically for students experiencing suicidal thoughts or at risk of suicide. The aim of this chatbot is to help reduce the number of suicides among students by providing them with timely support and guidance. Leveraging the expansive and rapidly evolving field of AI, this technology can contribute positively to addressing societal challenges and promoting well-being.

Abstract-As data demands increase with the evolution toward beyond-5G and 6G communication systems, achieving efficient network backhaul is crucial to support high data rates, minimized latency, and broad geographic coverage. Traditional backhaul networks, reliant on radio frequency (RF) communications, face limitations in scalability and bandwidth, particularly in dense urban and rural remote areas. This paper explores a hybrid RF-Free-Space Optical (FSO) communication model, integrating Low Earth Orbit (LEO) satellites with High Altitude Platform Stations (HAPS) to enhance backhaul network efficiency. The proposed HAPS-LEO cooperative model mitigates atmospheric disruptions and offers scalable, high-bandwidth solutions. We further examine Contact Graph Routing (CGR) as a protocol for optimized data routing in variable connectivity conditions, presenting simulated performance results that demonstrate the advantages of this architecture.

Abstract-Cardiovascular diseases (CVDs) are a leading cause of death worldwide, posing a significant health threat not only in India but across the globe. This highlights the critical need for a dependable, precise, and accessible system to diagnose such conditions promptly, enabling timely treatment. Machine learning algorithms have become invaluable tools in healthcare, automating the analysis of extensive and complex datasets. Recent studies demonstrate that various machine learning techniques can aid healthcare professionals in diagnosing heart-related conditions. The heart, second only to the brain in importance, plays a vital role in circulating blood throughout the body. Predicting heart disease occurrence is thus essential in the medical field. Data analytics enhances the prediction accuracy by analysing large volumes of patient data, often maintained on a monthly basis, which could be utilized to anticipate potential future diseases. Techniques such as Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM) are widely applied to predict heart conditions. Diagnosing and predicting heart diseases remain a considerable challenge for both doctors and hospitals globally. To mitigate the high mortality rate associated with these diseases, efficient and rapid detection methods are essential. Machine learning and data mining techniques hold a crucial role in this context. Researchers are accelerating efforts to develop machine learning-based software that can assist doctors in both predicting and diagnosing heart diseases. This research project aims to leverage machine learning algorithms to predict the likelihood of heart disease in patients.

Abstract-This project focuses on traffic safety analysis, aiming to enhance road user safety through a comprehensive evaluation of various factors that influence accident rates and driving conditions. By assessing parameters such as skid resistance, surface texture, visibility, and roadway geometry, the study identifies critical factors that contribute to traffic incidents and offers insights into effective safety measures. Field data was gathered from selected road sections, and laboratory tests were conducted to analyze surface characteristics. Statistical analysis was applied to understand the correlation between these factors and accident frequency, enabling the development of targeted recommendations to improve safety standards. The project underscores the importance of proactive road maintenance and design improvements in reducing accidents and enhancing the overall safety and efficiency of transportation infrastructure. This project aims to enhance road safety by conducting an in-depth analysis of factors contributing to traffic accidents and assessing the effectiveness of potential interventions. Through examining elements such as pavement skid resistance, surface texture, road geometry, and visibility, the study explores their influence on accident frequency and severity. Field data collected from high-risk road sections, along with laboratory testing of pavement properties, provide a foundation for evaluating existing conditions. Using statistical and spatial analysis, the study identifies patterns in accident data, highlighting critical areas for improvement. Recommendations are developed based on these insights to propose cost-effective strategies that prioritize safety, such as optimized pavement materials, better signage, and improved road design. This research underscores the role of systematic traffic safety analysis in advancing safer, more resilient transportation systems. This project undertakes a comprehensive traffic safety analysis aimed at reducing accidents and improving road safety through a detailed examination of key factors affecting driving conditions. By focusing on parameters such as skid resistance, pavement surface texture, visibility, road geometry, and traffic flow, the study seeks to identify elements that significantly impact accident rates and driving safety.

Abstract-This project focuses on traffic safety analysis, aiming to enhance road user safety through a comprehensive evaluation of various factors that influence accident rates and driving conditions. By assessing parameters such as skid resistance, surface texture, visibility, and roadway geometry, the study identifies critical factors that contribute to traffic incidents and offers insights into effective safety measures. Field data was gathered from selected road sections, and laboratory tests were conducted to analyze surface characteristics. Statistical analysis was applied to understand the correlation between these factors and accident frequency, enabling the development of targeted recommendations to improve safety standards. The project underscores the importance of proactive road maintenance and design improvements in reducing accidents and enhancing the overall safety and efficiency of transportation infrastructure. This project aims to enhance road safety by conducting an in-depth analysis of factors contributing to traffic accidents and assessing the effectiveness of potential interventions. Through examining elements such as pavement skid resistance, surface texture, road geometry, and visibility, the study explores their influence on accident frequency and severity. Field data collected from high-risk road sections, along with laboratory testing of pavement properties, provide a foundation for evaluating existing conditions. Using statistical and spatial analysis, the study identifies patterns in accident data, highlighting critical areas for improvement. Recommendations are developed based on these insights to propose cost-effective strategies that prioritize safety, such as optimized pavement materials, better signage, and improved road design. This research underscores the role of systematic traffic safety analysis in advancing safer, more resilient transportation systems. This project undertakes a comprehensive traffic safety analysis aimed at reducing accidents and improving road safety through a detailed examination of key factors affecting driving conditions. By focusing on parameters such as skid resistance, pavement surface texture, visibility, road geometry, and traffic flow, the study seeks to identify elements that significantly impact accident rates and driving safety.

Abstract-This study explores the potential of Kraft lignin and wood-based additives to mitigate rutting in porous asphalt concrete (PAC), a material widely used for its water permeability and noise-reducing properties. PAC, however, suffers from rutting, a type of pavement distress that leads to deformations and reduced performance under traffic loads. The research evaluates the impact of incorporating Kraft lignin and wood-based modifiers into PAC to enhance its rutting resistance. Experimental investigations, including wheel-tracking and Marshall stability tests, were conducted on asphalt samples with varying concentrations of these modifiers. Results indicated that both Kraft lignin and wood-based additives significantly improved rutting resistance, with lignin contributing to greater binder stiffness and wood additives enhancing aggregate bonding. These findings suggest that bio-based modifiers could offer a sustainable solution to improving the durability of porous asphalt pavements, reducing maintenance costs and environmental impact.

Abstract-Lifting bridges, also known as movable bridges, are crucial for enabling both road and maritime traffic, especially in regions where waterways intersect with busy transportation corridors. These bridges, including types such as bascule, swing, and vertical lift bridges, allow for efficient passage of vessels while maintaining road connectivity. Research into lifting bridges spans a range of disciplines, from structural engineering and materials science to automation and environmental impact studies. One primary focus is on the design and mechanics of movable bridges, with emphasis on the structural integrity, materials, and load-bearing capacities of these complex systems. Innovations in materials science have led to the exploration of corrosion-resistant alloys and high-performance composites, improving the durability and lifespan of lifting bridge components. Additionally, advanced automated control systems are becoming increasingly important, with research on robotic mechanisms and smart sensors aiming to streamline bridge operations and enhance safety. These innovations are complemented by studies into the impact of lifting bridges on traffic flow, which examine the operational challenges and disruptions posed by the periodic lifting and lowering of bridges. Another key area of research involves the environmental impact of lifting bridges. Studies have been conducted on the ecological effects of bridge operations on aquatic ecosystems, particularly in relation to waterway traffic and habitat disruption. Moreover, with the rise of sustainable infrastructure, researchers are exploring ways to reduce energy consumption and carbon footprints associated with the mechanical lifting process. Further, lifting bridges present unique challenges in extreme environments, such as those found in cold and hot climates, where materials and mechanisms face additional stresses due to thermal expansion, corrosion, or ice formation.

Abstract-The machine is a double-purpose unit proposed to chop and crush forage crops in an efficient way, to cut down on waste and inefficiency in agricultural practices. It discusses evaluation related to the performance of the machine, with emphasis on its productivity in trimming different forages. The study discusses the advantages the use of this machine would bring about, such as minimum labor costs and efficient crop management. Testing results show that the machine achieves the basic standards of operation for agricultural purposes. The main objective of the project was to develop a machine that efficiently performs chopping and crushing work simultaneously with the ability to overcome the weaknesses of machines that can only perform the two functions separately. This multi-purpose functionality aims at increased productivity and saving on operational costs. An increased need for environmentally friendly economical machines capable of delivering agricultural needs effectively, therefore, is essential to achieve economic sustainability.

Abstract-Chatbots function as software that enables users to ask questions and receive assistance through appropriate responses. This paper explores an AI-based chatbot designed to serve as an online ticketing system, streamlining the process of issue reporting, resolution, and user assistance across various domain. It also includes features like customer support, IT helpdesks, and event management. Natural language processing (NLP) is used by this proposed chatbot to understand user queries, categorize tickets, and provide instant responses. The aim of this chatbot is to enhance efficiency, reduce response times, and improve user satisfaction.

Abstract-Artificial Intelligence (AI) has become a pivotal technology for automating complex processes, while Soft Computing provides innovative ways to manage imprecise and uncertain data. By combining the two, hybrid systems leverage the strengths of AI’s precision and Soft Computing’s adaptability. This paper delves into the principles behind these hybrid models, emphasizing their use in healthcare, autonomous systems, finance, and smart cities. It also highlights the challenges of scalability and interpretability and outlines potential research directions, including integrating quantum computing and promoting explainable models.

Abstract-Low productivity of workers is the most significant factor behind delivery slippages in manufacturing industries. As manufacturing is a laborer predominant industrial sector, this paper focuses on worker output and their efficiency in the manufacturing sector. It covers the definitions of productivity, efficiency of the workers, its perspectives and the factors influencing the productivity. Proposed ANOVA method optimize performance of productivity and worker production parameters. Also observed more sensible case to increase production productivity.

Abstract-The challenges of urban areas with ever-increasing traffic congestion, emergency response, and maintaining road safety are the basis of this paper. The ITMS proposed in this paper treats optimization of timings at the traffic signals based on real-time vehicle counts, along with the detection of emergency vehicles and accidents, as its prime mandate. To achieve these objectives of optimal traffic management, advanced technologies, such as sensor detectors, algorithms for processing data, and communicating networks, were adopted. With simulations and evaluations, the ITMS holds great promise in enhancing traffic flow efficiency as well as reducing congestion while shortening emergency vehicle response times vis-a-vis fixed-time signal control. The research performed here addresses the development of more sustainable and resilient urban transportation systems.

Abstract-This study focuses on the design and analysis of a hollow shaft for an EV go-kart, optimizing weight reduction and structural integrity. Using SolidWorks for design and ANSYS for Finite Element Analysis (FEA), the shaft’s performance under mechanical stresses and cyclic loads was evaluated. Results demonstrated significant weight savings while maintaining strength, rigidity, and durability, enhancing the go-kart’s efficiency and reliability. This work highlights the potential of hollow shafts in improving EV performance through lightweight design.

Abstract-Employing generative adversarial networks, specifically with regard to cycle consistency loss and mask vectors, mainly concentrates on the colorization of Synthetic Aperture Radar (SAR). Most SAR imagery is devoid of chromatic information. Contemporary deep learning techniques are the predominant approach for SAR colorization. The methodology proposed herein employs a multidomain cycle-consistency generative adversarial network (MC-GAN). It enhances performance through the integration of a mask vector and cycle-consistency loss. The approach does not necessitate the availability of paired SAR-optical imagery. The multidomain classification loss contributes to the precision of the color output. The methodology has been evaluated using the SEN1-2 dataset for urban and terrain areas.

Abstract-The cost of commuting to and from school is a burden for many people, especially in urban areas. While ride-hailing services are popular worldwide, most students face issues with accessibility and convenience. The aim of this work is to create and use fairShare. A web platform that allows students to connect and share rides, thereby reducing transportation costs and reducing the environment around them. Users can register, post trips,and compete with other students using the same route. Early tests of the platform have shown that it reduces student travel costs and provides a good user experience. The platform also promotes sustainable practices for students. fairShare demonstrates the potential of student-friendly carsharing to reduce transportation costs and improve social interaction. The platform has the ability to measure a broader and more effective way for students to take action.

Abstract-With the advent of the “e-” revolution starting in 2000, the issue of cyber security, cyber-attacks and cyber threats which included domains, but not e-business, e-government, e-; commerce etc. only occurred because for the issue of cybersecurity in e- learning is under-explored, the aim of this paper is to present methods that focus on monitoring cybersecurity issues related to e- learning processes on. In addition, this article aims to present some good examples of cybersecurity management strategies in e- learning and cybersecurity trends in this area.[2] This paper will present possibilities for increasing information security and cyber- security awareness in education and e-learning that will inspire future cybersecurity professionals to navigate their career path.[3].

Abstract-The growing dependence on technology for a wide range of activities has dramatically increased computational demands, driving significant growth in computer network usage over the past few decades. This surge in demand for processing and storage capabilities has opened up business opportunities for companies but has also drawn the attention of cybercriminals. In response to these threats, researchers have developed various attack detection and prevention models. This paper introduces a new intrusion detection model that operates in two phases. The first phase involves building a feature ontology to train a convolutional neural network (CNN), and the second phase tests the trained model. For feature selection, the model uses an Elephant Herd Optimization-based genetic algorithm, which efficiently identifies a strong feature set for classifying network sessions. Experiments on a real-world dataset show that the proposed model can detect various types of attacks within normal sessions. Results demonstrate improved accuracy and performance metrics compared to existing models.

Abstract-IoT device-based communication boosts monitoring, business operations, and daily activities but also increases the load on servers and clouds. To handle this, edge computing acts as an intermediary layer. Efficient job management is critical for large-scale IoT networks, but existing models often fail to adapt based on past job sequences. This work introduces a model using a modified wolf Optimization algorithm to dynamically balance loads without prior training. It also incorporates a Random Forest model to generate initial job sequences. Experiments show that the proposed approach reduces job makespan time and enhances edge resource utilization compared to other models.

Abstract-Virtual meetings have become an essential mode of communication in contemporary professional environments. However, the fast-paced nature of virtual meetings undermines the ability to remember critical information accurately as even making notes is an imperfect mundane task, manual note-taking is both time- consuming and error-prone, often resulting in overlooked decisions and action items. SummrAIze is an AI-powered meeting assistant designed to address these challenges by automating the transcription, [1]summarization, and extraction of actionable insights during virtual meetings on platforms like Google Meet and Microsoft Teams. Using advanced machine learning algorithms, SummrAIze produces real-time summaries, highlights key points, and identifies action items, enabling participants to engage fully in discussions without sacrificing documentation accuracy. Integrated with productivity tools, SummrAIze not only reduces manual effort but also ensures that all essential information is recorded and accessible, enhancing team collaboration and workflow continuity. This paper presents the design, methodology, and potential impact of SummrAIze, a tool that redefines productivity in the context of virtual meetings.

Abstract-Non-destructive spectroscopic techniques represent the top-choice for any kind of process monitoring . Among all of the available techniques, Raman spectroscopy is one of the most solid and versatile tools to analyze several materials, both in lab and on-field conditions . Raman analysis has grown, reaching several industrial sectors such the food and textiles sectors .Raman spectroscopy displays several advantageous features over other techniques like infrared spectroscopy. For example, the quality of the signal collected is barely affected by the presence of water, allowing for use in plenty of applications where infrared analyses are not reliable . A representative case study is the in-situ monitoring of a fermentative process where Raman techniques outperformed any other spectroscopic approach .Molecular-level tissue characterization is highly potent for cancer diagnosis. As a tissue starts becoming cancerous, specific biomolecules are overexpressed or aberrantly expressed, which can be used as cancer molecular markers. If we can detect these molecular markers spectroscopically, it would lead to a new molecular-level cancer diagnosis with high objectivity.

Abstract-The plight of homeless children remains one of the most urgent global challenges, with millions of vulnerable children deprived of basic human rights such as shelter, healthcare, and education. Despite the rapid advancement of technology, child welfare systems in many developing countries still face significant hurdles, marked by inefficiencies and fragmented services. This paper proposes an innovative AI-driven system for adoption and rehabilitation that aims to address these systemic challenges holistically. By harnessing cutting-edge artificial intelligence (AI) algorithms, the system streamlines the adoption process, delivers personalized healthcare recommendations, and optimizes resource allocation for child welfare organizations. Through the integration of predictive analytics, data-driven decision-making, and a robust ethical framework, the system ensures transparency, fairness, and scalability. Early simulations and case studies highlight the transformative potential of AI in enhancing adoption success rates and improving healthcare outcomes for homeless children. The findings emphasize the system’s ability to drive meaningful improvements in global child welfare efforts, offering a scalable, ethical solution that can have a lasting impact on vulnerable children worldwide.

Abstract-Phishing attacks remain a prevalent cybersecurity threat, exploiting vulnerabilities in digital platforms to compromise sensitive user data. This paper introduces a novel machine learning-based framework for phishing URL detection, combining advanced feature engineering techniques and classification algorithms. By integrating lexical attributes, WHOIS data, and ranking metrics like PageRank and Alexa Rank, our approach enhances detection accuracy and minimizes false positives. Experimental results demonstrate superior performance across classifiers, achieving an accuracy of 99.8% using Support Vector Machines. The framework’s modular design ensures adaptability to evolving phishing tactics and scalability for enterprise deployment. This research lays the foundation for future advancements in AI-driven cybersecurity solutions.

Abstract-The demand for robust authentication systems has risen significantly as cyberattacks become increasingly sophisticated. Current authentication mechanisms, such as textual passwords, biometrics, and graphical systems, each have unique vulnerabilities. This research explores the concept of a 3D password system, which integrates various authentication schemes into a virtual 3D environment to enhance security. The system allows users to interact with objects in a 3D space, forming unique and complex passwords based on sequences of interactions. This paper elaborates on the system’s design, implementation, and potential applications in critical and non-critical systems. Detailed analyses reveal that the 3D password provides superior resistance to timing attacks, brute force attempts, and well-studied schemes, while maintaining user-friendliness. Future research avenues include the incorporation of AR/VR and IoT technologies to further expand the utility of the 3D password system.

Abstract-This paper proposes a flower recognition system using transfer learning with the ResNet-50 architecture. By utilizing pre-trained weights from ResNet-50, the system classifies ten species of flowers, drawing on an extended dataset with over 8,000 labelled images. The study addresses challenges in deep convolutional neural networks, such as overfitting and local optimality, by fine-tuning the ResNet-50 model. Initially, only the final layers of the model are retrained on the flower dataset, while the pre-trained layers remain frozen. After achieving initial convergence, all layers are unfrozen for full model fine-tuning. The dataset is divided into training, validation, and test sets to evaluate the model’s performance, which is measured using accuracy, and F1-score. The experimental results demonstrate that the transfer learning approach significantly improves classification accuracy and generalization, outperforming traditional methods. This approach proves especially effective in handling visually similar flower species and diverse environmental conditions. The study highlights the potential of transfer learning in enhancing the efficiency and robustness of flower recognition systems, contributing to broader applications in image classification tasks.

Abstract-The concept of Shoe Theory emphasizes that everyone is comfortable in their own shoes and should not be forced to wear someone else’s shoes. This theory posits that individual differences, including the effects of various elements such as time and generational perspectives, significantly impact workplace dynamics and organizational effectiveness. The theory highlights the importance of recognizing the unique experiences and backgrounds of team members to foster an inclusive and productive environment. Keeping creative destruction in mind, everything has its loophole to be breached. Although the answer may be yes or no, there always exists a condition of if/situation and but/exception.

Abstract-The k-Nearest Neighbors (k-NN) algorithm is a widely used non-parametric method for classification tasks, where the selection of the optimal value of k (the number of neighbors) plays a critical role in model performance. Traditional methods for selecting k, such as cross-validation or heuristic approaches, can be time-consuming and computationally expensive. This paper proposes an alternative approach to determining the optimal k for k-NN using polynomial regression. By treating the relationship between the value of k and the performance metric (such as classification accuracy) as a continuous function, we use polynomial regression to model this relationship and identify the k that results in the best performance. The polynomial regression model is trained on a set of performance data for different values of k, allowing for a smooth and accurate estimation of the optimal k across various datasets. Our experimental results demonstrate that the polynomial regression-based approach provides an efficient and effective method for selecting k, outperforming traditional techniques and reducing the computational cost associated with hyperparameter tuning. The proposed method also offers several advantages over traditional hyperparameter optimization techniques. By modelling the performance of k-NN as a continuous function of k, polynomial regression avoids the need for exhaustive grid search or cross-validation, making it particularly suitable for scenarios where computational resources are limited or time is constrained. Furthermore, the flexibility of polynomial regression allows for capturing complex, non-linear relationships between k and model performance, which can lead to more accurate predictions of the optimal value. Our approach is demonstrated one dataset, where it not only achieves higher accuracy but also reduces the overall time spent on model selection, making it a practical and scalable solution for hyperparameter tuning in machine learning applications.

Abstract-This paper reviews current research on Alumni Portal, the connections between alumnus and students, college interaction between alumnus, past records, event updates and records. The review covers 30 research papers, investigating database of Alumnus, students, past and present events held, interaction of alumnus in college events, interaction of alumnus and students. For improving the previous Alumni portals and projects related to Alumni.

Abstract-This paper explores the transformative potential of an Augmented Reality (AR) storytelling application designed to enhance traditional storytelling methods by integrating interactive digital animations, text, and audio into physical environments. The app offers a dynamic and immersive experience, particularly for children, by enabling real-time interaction with animated characters, voice narration, and engaging, interactive scenes. Unlike static books or conventional digital content, this app allows users to actively participate in the narrative, creating a more engaging and educational experience. By overlaying digital elements onto the real world, the app fosters increased interactivity and encourages deeper emotional and cognitive engagement with the story. Children can interact with animated characters, explore rich 3D environments, and receive instant feedback through audio cues and animations that respond to their actions. The app also supports educational growth by offering interactive learning modules, promoting reading comprehension, and allowing customization of story elements to accommodate multiple learning styles. The application leverages cutting-edge AR technologies to transform traditional narratives into immersive experiences, providing both entertainment and educational value. By integrating AI-driven components for voice recognition and dynamic content generation, the app can offer personalized experiences and adaptable content based on user preferences and interactions. This survey examines the underlying technologies and design choices that contribute to the app’s ability to engage users, as well as the broader implications of AR in storytelling for enhancing educational tools and creative learning platforms.

Abstract-This paper dives into how robotics is transforming manufacturing today. It looks at how robots are making processes faster, safer, and more efficient while also tackling some challenges like high costs and technical complexity. By exploring industries like automotive and consumer goods, and through examples from companies like Toyota and Unilever, the paper highlights both the advantages and limitations of using robots. It also touches on important issues like job impacts and cybersecurity risks, suggesting that thoughtful planning is essential for making the most of robotics in manufacturing.

Abstract-This paper examines the pivotal role of mechanical engineering in advancing transportation through innovations like electric vehicles, lightweight materials, and dual-fuel systems. It highlights their impact on sustainability, efficiency, and safety while addressing challenges such as costs, regulations, and public acceptance. Emerging technologies like Hyperloop and hydrogen propulsion are also explored, emphasizing their potential to redefine global mobility.

Abstract-Diabetes is one of the most frequent diseases worldwide where yet no remedy is discovered for it. Every year a great deal of money has to be spent for caring for patients with diabetes. Therefore, it is crucial that prediction should be very accurate and a very dependable method must be adopted for doing so. One of these methods is the use of artificial intelligence systems, and in particular, the use of Artificial Neural Networks, or ANN. So, in this paper, we used artificial neural networks in order to predict whether or not a person has diabetes. The criterion was to minimize the error function in neural network training with the help of a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 70%

Abstract-The enhancement in web technologies has contributed to the evolution of web applications that are very dynamic and engaging. This research work focuses on the creation of an online image editing application that is based on cloud infrastructure and modern web layouts/development tools such as Next.js, TailwindCSS, and Cloudinary’s APIs, among other resources, to deliver advanced image editing features. The application incorporates Clerk to allow users to create login accounts and easily register, while data is managed using MongoDB to facilitate the security of users and edited pictures across several devices. Necessary and basic features such as object removal, editing backgrounds, recoloring pictures, restoring, and changing the size of images are handled within the cloud and therefore benefit the functionality of the application and users as well. In addition, a contact form utilizing EmailJS has been integrated to enable communication with users. This research work highlights the legitimacy of cloud-based solutions as well as their expanded geographic reach in catering to an advanced user experience within image editing applications, thus supporting the growth of cloud computing and web technology.

Abstract-Due to the large amount of information generated every day, automatic writing is an important part of knowledge management. The discipline has made great progress, especially with the emergence of abstraction, abstraction and hybrid content models. In the extraction method, the main idea is preserved by selecting the main sentence or phrase from the text, while in the abstraction method, all the information is repeated to create new sentences. As the name suggests, hybrid models include the features of both extraction and abstraction systems to get the best of both approaches. However, issues remain, particularly in how to address the authenticity, coherence, and length of the text. This article examines the current state of writing concepts and topics in practice and future research.

Abstract-This study introduces the Car Surveillance System (Driver Negligence and Dissuader System), integrating advanced lane detection, drowsiness detection, pedestrian detection, and object detection technologies to boost road safety. Much like the luggage storage website, it presents a user-friendly interface and real-time alerts to avert accidents. Intelligent functionalities ensure efficacy and security, simplifying driving experiences and encouraging hassle-free travel. Tailored settings and transparent pricing cater to individual driver requirements, tackling prevalent challenges and nurturing safer roads for all users.

Abstract-In light of the increasing gun violence incidents worldwide, there is a pressing need for automated visual surveillance systems capable of detecting handguns. This paper presents a method for real-time handgun detection in video streams using the YOLO algorithm, comparing its performance in terms of false positives and false negatives against the Faster CNN algorithm. To enhance detection accuracy, we compiled a custom dataset featuring handguns from various angles and merged it with the Roboflow dataset. The YOLO model was trained on this combined dataset and validated using four different videos. The results indicate that YOLO effectively detects handguns across diverse scenes, demonstrating superior speed and comparable accuracy to Faster CNN, making it suitable for real-time applications.

Abstract-The field of healthcare is turning more towards tools to improve access, to services and make the experience better for patients and providers alike. A specific example is “Appointify,” a web platform for booking doctor appointments that was created using the MERN technology stack— MongoDB, Express.js, React and Node.js—with a goal of simplifying the appointment process and connecting patients, with healthcare professionals seamlessly. This document provides an outline of “Appointify ” a system created to tackle the issues encountered in appointment handling like extended waiting periods and disorganized scheduling well as the absence of efficient communication, between patients and healthcare providers.”Appointify” allows patients to search for doctors based on their expertise area request appointments access their history and update their profiles. It also equips doctors with functions to control their availability, schedule appointments. Engage with patients effectively. The platform includes functions such, as role based access control for security measures and encryption to safeguard data privacy It also features responsive design for user friendly interaction, on various devices

Abstract-In this research paper we have proposed a device that helps visually impaired people recognise currency denomination in order to detect the denomination of Indian currency. The members of this community have challenges particular to them when it comes to dealing with money, and as such there is an ever-growing need for quick and accurate identification tools appropriate for their scenario. We describe the process we have followed to develop the device, offering a blend of image processing and machine learning to allow currency identification in real time. Surveys of potential users revealed important preferences and needs for accessibility and ease of use, guiding the design of a new device system. According to test results, the device achieves high accuracy in denominations recognition and effective user satisfaction, demonstrating a potential device providing financially independent life for visually impaired users. These findings underscore the value of blending cutting-edge technology with user-centered design to create impactful solutions for underserved communities. The paper hence concludes with recommendations for the further enhancements and future research to expand the device’s features and accessibility.

Abstract-This system is designed to solve a common problem: running out of gas without knowing when it’s about to happen. The system keeps track of how much gas is left in the container by continuously checking its weight. If the gas is running low, it can automatically place a new gas order using the Internet of Things (IoT) technology. A device called a load cell is used to measure the weight of the gas container, and this data is sent to an Arduino Uno (a small computer) to compare with a standard weight. If the gas is low, the system sends a message to the user via SMS, using a GSM modem. For safety, the system also has sensors to detect gas leaks (MQ-2 sensor) and monitor the surrounding temperature (LM35 sensor). If any unusual changes are detected by these sensors, such as a gas leak or a sudden change in temperature, a siren will sound to alert the user.

Abstract-Liver diseases like cirrhosis and hepatitis are major causes of global morbidity and mortality, highlighting the need for early detection. Traditional diagnostic methods often identify liver damage at later stages, limiting preventive interventions. This study develops a machine learning model to predict liver damage earlier using clinical features and lab results. By analyzing a data-set with patient demographics and biochemical markers, we apply machine learning algorithms, including Random Forest, Decision Tree, and Logistic Regression, and evaluate their performance using metrics like accuracy, precision, recall, F1 score, and ROC-AUC. The Random Forest model outperformed others, showing high accuracy and robustness. Feature importance analysis revealed critical clinical factors, such as serum bilirubin and liver enzymes, in predicting liver damage. These results suggest that machine learning, especially Random Forest, could aid in the early detection of liver disease, improving patient outcomes. Future work will focus on using larger, more diverse data-sets and advanced models to improve predictive accuracy.

Abstract-Financial systems have become more complicated than ever before due to their fast growth, which calls for creative methods of managing, analyzing, and forecasting system behavior. In order to solve problems in intricate financial systems, this study investigates the use of intelligent computing and trustworthy machine learning models. The goal of the project is to improve decision-making, risk assessment, and anomaly detection in dynamic financial contexts by fusing cutting-edge computational techniques with reliable AI frameworks. The dependability and interpretability of machine learning models are given special attention in order to make sure they satisfy the exacting standards of accuracy and transparency that are necessary for financial stakeholders. The implications of these technologies for reducing systemic risks and enhancing operational effectiveness are also covered in the study. This study demonstrates the revolutionary potential of intelligent computing and reliable machine learning in creating robust and flexible financial ecosystems via case studies and experimental validations. The results highlight how important they are in determining how finance and economic stability develop in the future.

Abstract-For more effective treatment, early diagnosis of liver disease is crucial. Detecting liver disease in its early stages is challenging due to its subtle symptoms, often becoming apparent only in advanced stages. This research leverages machine learning techniques to address this issue by enhancing liver disease detection. The primary objective is to differentiate between liver patients and healthy individuals using classification algorithms. Liver disease has seen a global increase in prevalence in the 21st century, with nearly 2 million annual deaths attributed to it according to recent surveys. It accounts for 3.5% of global deaths [1]. Early diagnosis and treatment can significantly improve outcomes for patients with chronic liver disease, which is among the most fatal illnesses. The advancement of artificial intelligence, including various machine learning algorithms like Regression, Support vector machine, KNN, and Random Forest, offers the potential to extend the lifespan of individuals with Chronic Liver Disease (CLD).

Abstract-Concurrency and Synchronization in digital electronics where algorithms are use to comprehend the all the calculations for work. Digital machines ranging from Embedded Systems, IOT, Computers, Smartphones, Servers and Networking systems. Synchronization has became a very crucial part of basic programs running in the background of any operating system, that is the “Kernel”. These algorithms are the basic part of the OS for its smooth working in multi-programming, load balancing, time synchronization, data I/O ops within and out of the system, parallel computing with GPUs, I/O ops with IOT and cloud systems, Network and data security, mathematical calculations, etc. Synchronization programs are used to prevent conditions such as data races, deadlock, network latency, data corruption, manipulation and many more. Conditions created by these bugs can be visible or invisible in the user space. This Research paper is a comprehensive analysis on Concurrency and Synchronization. Source code examples of such conditions are given below from the original source code of some of the common linux distros. Applications of solutions to some of these issues in programs and systems to help progress for development of the performance and results.

Abstract-Dynamic Ride Pricing is a vital feature in the ridesharing industry that allows companies to adjust ride fares based on shifts in supply, demand, weather conditions, and other relevant factors. This study details the development of a machine learning-driven dynamic pricing model designed to optimize fare adjustments in real time. By analyzing key variables such as trip distance, weather, and historical patterns of supply and demand, the algorithm can deliver pricing that is both contextually relevant and responsive. The model aims to achieve a balance between profitability and customer satisfaction by swiftly adapting to fluctuating market conditions. Leveraging advanced machine learning techniques, it ensures pricing that is not only accurate but also fair and responsive. By integrating these factors into a unified pricing strategy, the model provides an optimized solution that enhances operational efficiency and meets consumer needs, ultimately contributing to a more equitable and efficient pricing system in the ridesharing sector.

Abstract-The use of wind power and conversion into energy, methodology regarding implementation of the idea, Advantages and Disadvantages and the scope for future.

Abstract-Augmented Reality, also known as AR technology, is a tool that employs computer graphics to superimpose a different layer of information onto the real world. Traditionally, virtual reality provided more interactive experiences when compared with other methods. In this paper, we explore the current state and future prospects of AR with a focus on its application in sectors such as medicine, education and retail among others. The functioning mechanisms of AR systems; sensors involved, processing algorithms required, rendering techniques for visual output and user interaction are discussed along with recent innovations like improved AR hardware or mobile applications. A literature review has been done to illustrate how AR enhances engagement in education, assists surgeons enhance precision during operations, changes customer experience in retail shops and provides entertainment through immersiveness. Moreover, AR technologies are also being explored for use in sectors such as tourism, automotive, and manufacturing, where they have the potential to revolutionize customer service, design processes, and workflow management.But there are obstacles that still hinders growth of AR such as technical barriers, privacy issues and expensiveness . Additionally, it discusses ways to overcome these challenges while pointing out things to research on so that maximum utility of AR can achieve. In conclusion, we find out that AR has great potential to alter different industries since it leads to more practical applications and encourages ongoing innovation.

Abstract-Chronic kidney disease (CKD) is a global health challenge, affecting millions of individuals and often leading to kidney failure when not detected early. The application of machine learning (ML) for CKD prediction has gained significant attention, enabling timely diagnosis using clinical data. This paper explores various ML techniques used for CKD prediction, focusing on preprocessing challenges such as missing data, data imbalance, and feature selection. Additionally, the paper discusses the emerging role of Federated Learning (FL), a decentralised approach to ML that allows for privacy-preserving collaborative model training across institutions.

Abstract-Machine learning techniques are doing wonders in every sphere of life but using predictive analysis in healthcare is a challenging task. However, if implemented properly these techniques help in making timely judgements about the health and treatment of patients. Globally, diseases including diabetes, heart disease, and breast cancer are major causes of death; yet, the majority of these deaths are due to failure to have regular checkups for these conditions. Low doctor-to-population ratios and a lack of medical infrastructure are the root causes of the above-mentioned issue. Thus, early detection and treatment of these diseases can save many lives. Machine Learning, Deep Learning and Streamlit is an effort concentrated on the development of healthcare using in-depth engines to forecast several sicknesses. Streamli Cloud and Streamlit Library facilitate deployment of prediction models like a breeze for developers. This has made accessing and using prediction capabilities of the system easily done by any layman. The paper focuses on forecasting three major diseases namely diabetes, heart failure and Parkinson’s disease by using an advanced ensemble of deep learning models as well as traditional machine learning techniques. Then again, merging Support Vector Machine (SVM) algorithm together with Logistic Regression models will form one such integration scheme.

Abstract-The goal of the web-based search engine “Intelli Search” is to give users accurate and pertinent content by combining personalized video recommendations with sophisticated AI-driven response production. The platform imitates Gemini’s capabilities by leveraging the YouTube API to suggest pertinent films arranged by comment engagement and the Gemini API to produce theoretical answers based on user inquiries. By using MongoDB to store and show user search history in a sidebar, the project allows users to view past queries after entering their login information. Auth0 securely manages authentication, guaranteeing a quick and secure user login. Through the integration of these technologies, Intelli Search provides a dynamic and customized user experience, enhancing search relevance by fusing multimedia resources with theoretical knowledge. The architecture is examined in this work.

Abstract-Medical image analysis is a critical component in modern healthcare, enabling more accurate and timely diagnoses. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown impressive capabilities in automating medical image interpretation. This paper reviews the latest advancements in deep learning methods for medical image analysis, covering key applications such as image classification, segmentation, and object detection. We discuss the challenges in applying deep learning models to medical imaging, such as the need for large annotated datasets, generalization to diverse datasets, and model interpretability. Additionally, we provide an overview of state-of-the-art architectures and their performance in different medical imaging tasks. Finally, we address the future directions and potential clinical applications of these techniques.

Abstract-Deep reinforcement learning has emerged as a transformative technology in AI and robotics, finding new answers to challenging problems in space exploration missions. This review details the latest developments within the DRL framework with applications in space robotics, exploring aspects such as autonomous navigation and resource optimization as well as mission planning. In this study, we do some case studies on strategies like AlphaNavNet, AstroPlannerNet, and open-source SpaceRL framework. We review how the DRL-based system addresses some key issues such as unpredictable terrain, delay in communication and exploration versus exploitation. In addition, this paper covers the embedding of simulation-to-reality translation in robotics and astrophysical modeling and the application of deep learning techniques such as Double Deep Q- Networks (DDQN) and Reinforced Deep Markov Models (RDMM) in augmenting the decision- making power of space missions. Although DRL has proved to outperform other approaches in simulaions and prototype testing, the review also emphasizes experimentation for added robustness and reliability within extraterrestrial condition. Through this analysis, we gain insight into the potential and limitations of DRL in advancing space exploration, using new architectures and real-world validation.

Abstract-AI has deeply penetrated even the most critical domains, including healthcare, finance, and governance, making it possible with its transformative potential to reach unprecedented efficiency and innovation. Still, this widespread diffusion poses ever more urgent challenges related to ethics and accountability that should not be ignored. Synthesizing insights from five seminal studies on “Ethical Approaches in Designing Autonomous and Intelligent Systems,” “Accountability of AI Under the Law: The Role of Explanation,” “Explainable AI as a Tool for Accountability,” “AI Accountability in Financial Decision-Making,” and “Ethical Implications of Artificial Intelligence (AI) Adoption in Financial Decision-Making,” this paper explores the interplay between accountability frameworks and explainable AI (XAI), regulatory compliance, and societal impacts by combining theoretical and practical perspectives. This paper explores the necessity of explainable models in terms of handling ethical dilemmas, such as bias mitigation, fairness, and transparency, through technical methodologies like sensitivity analysis, counterfactual reasoning, and Shapley values for feature importance. Case studies in health care, finance, and governance -AI-driven diagnostics, credit risk assessments, and algorithmic decision-making in welfare systems- will be explored to illustrate consequences of opacity and betterment facilitated by accountability-driven approaches. In terms of these elements, this paper discusses emerging regulatory landscapes, including the AI Act in the European Union and global data protection laws, as importance factors forming the ethical practices of AI. Public trust erosion due to biased or opaque AI systems is a further societal impact, and inclusive design and multi-stakeholder accountability are put forward as important aspects in this context. A balanced framework of ethical considerations to guide AI innovation should encompass both technical and normative dimensions. Various practical recommendations are laid out, such as standardized practices of XAI, robust accountability mechanisms, and proactive approaches to compliance and regulatory matters. The research brings the technological advancement closer to the imperatives of ethics in AI, toward trust, equity, and justice in its use.

Abstract-The use of DL algorithms revolutionizes the approach towards the detection of plant disease, making this most critical agricultural technology develop towards accuracy and efficiency that were not possible even with earlier methods. Apart from the benefits that an automated system may have over a manual intervention one, such as quicker identification of disease and less manual efforts, DL techniques, and CNNs in particular, allow the diagnosis of the diseases on plants with precision. The potential of AI-powered systems for plant disease detection is the ability to automatically analyze a plant image to recognize the symptoms and classify diseases with high accuracy. These systems also have the potential to provide real-time support by analyzing complex images and suggesting management recommendations for diseases. Thus, with DL algorithms, the system can identify diseases in plants, detect slight changes in texture and color, and recommend the corrective action to optimize crop health. Further, with the recent advancement in optimized models like YOLOv5 and hybrid techniques by integrating CNN with traditional classifiers such as Support Vector Machines (SVMs), the accuracy in detection has increased. Although the approaches present promising outcomes, challenges abound, especially in dealing with complex image backgrounds, low-quality datasets, and computational efficiency. This paper discusses approaches designed to overcome these hurdles, thus indicating the future direction of plant disease detection systems. This work will, therefore contribute towards the advancement of AI-driven agricultural solutions in terms of the accuracy and speed of plant disease detection and enable better crop management practices around the world.

Abstract-With the increasing prevalence of limited labelled data in many real-world applications, few-shot learning (FSL) has become an essential approach to enable effective learning from minimal examples. However, scalability, domain generalization, and adaptability to new tasks remain significant challenges. This paper introduces “Unified Adaptive Few-Shot Learning”, a novel framework that combines the strengths of metric learning, graph neural networks (GNNs), and meta-learning. By extending Prototypical Networks with GNN- based prototype refinement, our approach improves the quality of class representations and captures complex inter-class relationships. Meta-learning further enhances task-specific adaptation, while self-supervised pretraining boosts feature robustness. Additionally, integrating class metadata facilitates seamless transitions between few-shot and zero-shot tasks. Experimental evaluations on benchmark datasets like Mini-ImageNet and Meta-Dataset demonstrate that our framework outperforms existing methods in accuracy, scalability, and cross-domain generalization, offering a promising solution for real-world FSL applications.

Abstract-The manufacture of raw materials to deliver the product to the consumer in a traditional supply chain system is a manual process with insufficient data and transaction security. It also takes a significant amount of time, making the entire procedure lengthy. Overall, the undivided process is ineffective and untrustworthy for consumers. If blockchain and smart contract technologies are integrated into traditional supply chain management systems, data security, authenticity, time management, and transaction processes will all be significantly improved. Blockchain is a revolutionary, decentralized technology that protects data from unauthorized access. The entire supply chain management (SCM) will be satisfied with the consumer once smart contracts are implemented. The plan becomes more trustworthy when the mediator is contracted, which is doable in these ways. The tags employed in the conventional SCM process are costly and have limited possibilities. As a result, it is difficult to maintain product secrecy and accountability in the SCM scheme. It is also a common target for wireless attacks (reply to attacks, eavesdropping, etc.). In SCM, the phrase “product confidentiality” is very significant. It means that only those who have been validated have acc ess to the information. This paper emphasizes reducing the involvement of third parties in the supply chain system and improving data security. Traditional supply chain management systems have a number of significant flaws. Lack of traceability, difficulty maintaining product safety and quality, failure to monitor and control inventory in warehouses and shops, rising supply chain expenses, and so on, are some of them. The focus of this paper is on minimizing third-party participation in the supply chain system and enhancing data security. This improves accessibility, efficiency, and timeliness throughout the whole process. The primary advantage is that individuals will feel safer throughout the payment process. However, in this study, a peer-to-peer encrypted system was utilized in conjunction with a smart contract. Additionally, there are a few other features. Because this document makes use of an immutable ledger, the hacker will be unable to get access to it. Even if they get access to the system, they will be unable to modify any data. If the goods are defective, the transaction will be halted, and the customer will be reimbursed, with the seller receiving the merchandise. By using cryptographic methods, transaction security will be a feasible alternative for recasting these issues. Finally, this paper will demonstrate how to maintain the method with the maximum level of safety, transparency, and efficiency.

Abstract-Cross-site scripting is one of the severe problems in Web Applications. With more connected devices which uses different Web Applications for every job, the risk of XSS attacks is increasing. In Web applications, hacker steals victims session details or other important information by exploiting XSS vulnerabilities. We studied 412 research papers on cross-site scripting, which are published in between 2002 to 2019. Most of the existing XSS prevention methods are Dynamic analysis, Static analysis, Proxy based method, Filter based method etc. We categorized existing methods and discussed solutions presented on papers and discussed impact of XSS attacks, different defensive methods and research trends in XSS attacks.

Abstract-The Focus Pro Anti-Distraction Application is a productivity-enhancing tool designed to help users maintain focus by reducing distractions from digital platforms like social media, videos, and other time-wasting activities. With the increasing prevalence of digital distractions, this app provides a structured, customizable solution to improve concentration and task completion for students, professionals, and anyone seeking better focus. The app offers multiple focus modes, each tailored for specific tasks: Learning Mode, Assignment Mode, and Notes Mode. These modes feature task management tools, reminders, progress tracking, and a calendar to organize tasks and goals effectively. Users can customize their experience based on their specific needs, whether they are studying, working on assignments, or taking notes. A standout feature is the app’s blocking functionality, which allows users to create a customized list of websites and apps to block during use. This helps users avoid distractions and stay on task by preventing access to non-productive content on both mobile and desktop devices. In addition, the app integrates an AI-powered Filtering system that intelligently analyzes content on platforms like YouTube and Google. It uses keyword and hashtag analysis to allow access only to study-related content, ensuring users remain focused on educational materials. The app also includes performance analytics, which tracks user productivity and provides insights into task completion. Users earn points for completing tasks on time, and these points contribute to earning badges. This gamification approach encourages users to stay motivated and improve their focus. In addition, the app offers a streamlined profile section that allows users to monitor their achievements, track badges earned. The interface is designed to be user-friendly and visually engaging, making it easy for users to navigate modes.

Abstract-Within the face of world populace increase, sustainable and efficient crop production has come to be important. the mixing of emerging technologies consisting of the net of things (IoT), cloud computing, and machine mastering is revolutionizing agriculture through permitting actual-time soil tracking, crop selection, and predictive analytics for more desirable choice- making. This paper offers a comprehensive framework for IoT-enabled precision agriculture, which employs numerous sensors to reveal soil parameters—including moisture, pH, and temperature—and leverages advanced machine learning algorithms for crop advice and soil nutrient management. The proposed structures now not best optimize irrigation and fertilization but additionally provide a low-value, electricity-efficient method to information collection via wi-fi sensor networks. additionally, cloud-primarily based structures and cell programs provide farmers with far flung get entry to real-time data, permitting well timed interventions. by way of combining reinforcement learning fashions, multi-sensor information fusion, and modular hardware setups, this machine supports sustainable farming practices and will increase crop productiveness. The consequences show sizeable upgrades in prediction accuracy, decreased environmental effect, and more advantageous selection-making skills for farmers, contributing to the modernization of agriculture.

Abstract-The rapid advancements in deep learning (DL) techniques have transformed the healthcare sector, leading to notable improvements in diagnostic accuracy, personalized treatment, and ongoing patient monitoring. One particularly promising application of deep learning in healthcare is Human Activity Recognition (HAR), which uses wearable and mobile sensors to track and categorize individuals’ daily activities. HAR, especially within the framework of the Internet of Healthcare Things (IoHT), has demonstrated significant potential in enhancing elder care, rehabilitation processes, and chronic disease management. However, despite these advancements, several challenges persist in fully leveraging deep learning for healthcare applications. A major challenge is the dependence on large, labeled datasets for training models. In real-world scenarios, obtaining labeled data for HAR tasks can be time-consuming, costly, and often impractical, leading to a reliance on weakly labeled or unlabeled data. To tackle this issue, recent strategies in deep learning, particularly semi-supervised and reinforcement learning techniques, have been introduced to make efficient use of the vast amounts of unlabeled data available. These methods, such as Deep Q-Networks (DQN) and auto-labeling schemes, significantly lessen the manual labeling burden while preserving high model accuracy. Additionally, deep learning’s capability to integrate multi-modal data from various sensors (like accelerometers, gyroscopes, and context sensors) is vital for HAR tasks. This integration of sensor data offers a more thorough understanding of human activity and improves the accuracy of activity classification models. Among the most promising deep learning models for HAR are Long Short-Term Memory (LSTM) networks, which excel at processing sequential data typical in human activity monitoring. LSTMs effectively capture temporal dependencies in sensor data, making them well-suited for identifying complex motion patterns and contextual changes.

Abstract-The COVID-19 pandemic has had an unprecedented impact on health systems in most countries, and in particular, on the mental health and well-being of health workers on the frontlines of pandemic response efforts. The purpose of this study is to provide an evidence-based overview of the adverse mental health impacts on healthcare workers during times of crisis and other challenging working conditions and to highlight the importance of prioritizing and protecting the mental health and well-being of the healthcare workforce, particularly in the context of the emotional intelligence.

Abstract-Phishing is a category of cyberattack that includes the theft of credit card numbers, passwords, and other private data. We have employed machine learning algorithms to identify phishing websites in order to prevent phishing fraud. The availability of several services, including social networking, software downloads, online banking, entertainment, and education, has sped up the development of the Web in recent years. Consequently, enormous volumes of data are downloaded and uploaded to the Internet on a regular basis. Attackers can now obtain private information, including social security numbers, account numbers, passwords, and usernames, as well as financial information. This is one of the most important problems with web security and is referred to as a “phishing” attack on the internet. To identify these malicious websites, we employ a variety of machine learning methods, including KNN, Naive Bayes, Gradient Boosting, and Decision Trees. The study is broken down into the following sections. The introduction outlines the tools, methods, and concentrated zones that are employed. The process of gathering the data needed to proceed is described in depth in the preliminary section. Subsequently, the paper highlights the thorough examination of the information sources.

Abstract-Emerging topics such as environmental protection and energy utilization have pushed research and development of electric vehicles. In the last few decades, numerous technologies have been developed for EV importance. In this article, key research topics in the area of EVs, namely electric machines, electrochemical energy sources, wireless charging infrastructure, and latest EV/HEV models are covered. This Review paper aims to consolidate the key emerging technologies in this field and provide the readers a blueprint to begin their own journeys.

Abstract-This project introduces the YouTube Video Transcribe Summarizer, a tool designed to automatically extract transcripts and generate concise summaries from YouTube videos. By leveraging the YouTube Transcript API, the system retrieves accurate video transcripts and utilizes Google Gemini Pro’s advanced text-based model to create coherent summaries.
Users can input a YouTube video URL, which displays the video thumbnail for context. The application features a customizable prompt template to tailor the summary generation process, ensuring relevance to individual needs. Built on a user-friendly Streamlit interface, this tool aims to enhance content accessibility and engagement. Additionally, the project explores the possibility of executing local models for improved performance and user control. By streamlining the summarization of video content, the YouTube Video Transcribe Summarizer facilitates more efficient information consumption, empowering users to navigate the vast landscape of online video more effectively.

Abstract-If we attempt to measure the need for the proliferation of so many programming languages, we will get an answer but it is a serious question in itself: why do we need so many programming languages?! Albeit there are existing so many dominant programming languages which can perform almost every task specifically, we are developing and depending upon a variety of them. Through this paper, the rationale behind developing diverse programming languages will be explored and the other factors like performance optimization, ease of use, specification and demand of the evolution of the era of technology will be discussed. It will also examine the distinguished categorisation of computer languages.

Abstract-This research paper explores the concept of man-made islands as a potential solution to address habitat loss and environmental degradation. By creating artificial islands, we can provide new habitats for wildlife, protect existing ecosystems, and mitigate the impacts of human activities on the environment. The paper will delve into the design principles, construction techniques, and ecological considerations involved in creating sustainable man-made islands. It will also examine the potential benefits of these islands, such as increased biodiversity, improved water quality, and coastal protection. Additionally, the research will discuss the challenges and limitations associated with man-made islands, including their environmental impact, economic feasibility, and potential conflicts with other land uses. Ultimately, this paper aims to contribute to the ongoing dialogue on innovative solutions for conservation and environmental sustainability.

Abstract-Nanorobotics is an exciting field that combines nanotechnology and robotics to revolutionize medicine. These tiny robots, smaller than a speck of dust can navigate through our bodies to deliver targeted treatments perform precise surgeries and even repair damaged cells . With their ability to access hard to reach areas and perform tasks at the molecular level nanorobotics hold immense potential in improving outcomes healthcare and transforming the future of medicines.

Abstract-Nanomaterials display unique features such as Excellent physical and chemical stability, lower density and high surface area. This chapter focus on nanomaterials such as graphene and carbon Nanotubes, how it is electrically and optically sensored with Nanomaterials. Multiple complex biosensors has been focused and even the application of Nanaomaterials also. In past few years a major disease has been affected throughout the world that is COVID-19, how nanomaterials has been used in curing the disease.

Abstract-DNA data storage is revolutionizing technology to fill up the voids in existing data storage systems with higher density and durability. The paper deals with DNA comput- ing, especially with the concept of using DNA sequences for data storage with emphasis on encoding digital data in DNA sequences and discussion on the latest developments in DNA storage technologies, challenges facing it, such as scalability and cost, and also the problem of error correction. The paper also highlights the advantages of DNA as a storage medium, including high information capacity and stability in the long term but discusses existing challenges. As a conclusion, we enumerate some directions for further research needed to make DNA data storage more practical. Another key challenge explored in the paper is error correction. DNA sequences, while robust, are prone to errors during synthesis, amplification, and sequencing processes. These errors can compromise the integrity of the stored data, necessitating the development of advanced error correction mechanisms. The paper examines current strategies for mitigating these errors, including the use of redundancy, coding theory, and error-tolerant storage architectures, while also identifying gaps that require further exploration.

Abstract-Conserving energy of power grid within wireless power grid nodes network (power grid) is crucial in different applications including wearable devices. To this end, proposed work uses sleep and wakeup protocol for conserving energy of power grid nodes. The protocol first of all examines the nodes that are not used for transmission of packets for longer period of times. After that detected node will be put to sleep. The nodes energy will play a crucial role to make it a cluster head. Euclidean distance will be used to elect node as cluster head. The experimental setup involves random node distribution, initial energy allocation, and the formation of clusters based on Euclidean distance. The proposed sleep and wakeup mechanisms strategically put nodes to sleep after periods of inactivity, conserving energy resources. A comprehensive evaluation, comparing the protocol’s performance with the widely used low energy aggregate cluster head (LEACH) selection protocol, stable election protocol (SEP), time based stable election protocol (TSEP) and distributed energy efficient clustering protocol (DEEC), reveals superior results in terms of fewer dead nodes, prolonged network lifetime, and efficient packet transmissions. The proposed method showcases a controlled and sustained pattern in communication to cluster heads and base stations, outperforming LEACH, DEEC, SEP and TSEP. Remaining energy analysis indicates a more gradual and sustainable reduction in energy levels, highlighting the protocol’s effectiveness in maintaining operational nodes over prolonged network. The study concludes with insights into future research directions, emphasizing parameter optimization, scalability considerations, integration of energy harvesting methods, and enhanced security measures.

Abstract-This paper presents a brief idea on load flow in power system, bus classification, improving stability of power system, flexible ac system, various controllers of FACTs and advantages of using TCSC in series compensation. It presents the modelling scheme of TCSC and the advantages of using it in power flow network. The plots obtained after simulation of network using MATLAB both with and without TCSC gives fair idea of advantages on use of reactive power compensators. load flow studies are fundamental in power system analysis for ensuring efficient and stable operation of electrical networks. This thesis investigates the application of the swing equation and the Newton-Raphson method in performing load flow analysis, aiming to enhance the accuracy and efficiency of power system evaluations. The swing equation, representing the dynamic response of a generator’s rotor to changes in system conditions, is used to model the transient behaviour of generators in power systems. This dynamic model is crucial for understanding how generators respond to load variations and network disturbances. However, for steady-state analysis, which is essential for system planning and operation, the swing equation’s role is more implicit, focusing on power balance and network equilibrium. In this study, we integrate the swing equation into a comprehensive load flow analysis framework, combining it with the Newton-Raphson method—a robust iterative technique for solving nonlinear algebraic equations. The Newton-Raphson method is employed to solve the power flow equations, which describe the relationship between generator outputs, load demands, and network configurations. The thesis details the formulation of the power flow equations and the application of the Newton-Raphson method to solve these equations efficiently. The integration of the swing equation helps refine the analysis by incorporating generator dynamics into the power flow study. The effectiveness of this approach is demonstrated through various case studies on different network configurations, showing improvements in both accuracy and convergence speed compared to traditional methods.

Abstract-As the rate of traffic violations is on the rise, there arises the need for automated enforcement systems. This project is about the implementation of an automated system of e-challan generation based on the license plate detection system. Cameras positioned at the intersections take images of the vehicles violating traffic rules; using computer vision techniques, the number plates are identified and read. The system now fetches the registered mobile number of the violator and sends out an e-challan by itself, thus although removing the manual efforts with more precision [1] and effective enforcement. By using tools like OpenCV and YOLO in major towns, the project can make the roads safer and traffic flow manageable.

Abstract-Robotics in neurosurgery has completely changed the game, and now there is much greater accuracy, higher efficiency levels, and greater safety of the patient. Robotic systems such as ROSA, NeuroMate, and Stealth Autoguide have taken minimally invasive approaches within surgery to an entirely different level, allowing for complex sutures to be performed with great ease. This paper discusses the history of development of robotic systems, the specifics of their application in different neurosurgical procedures, and their advantages related to the lesser invasiveness, better results for the patients, and shorter periods of the recovery. Limitations such as costs, the need for training, and ethical issues are in the analyses, and also expected advances such as autonomous operations driven by AI and tele-robotics. There is great potential with the use of robotics in the development of neurosurgical practice towards more accurate and patient-centered clinical activities.

Abstract-High-Frequency Trading (HFT) is a critical component of modern financial markets, characterized by the execution of large volumes of orders within fractions of a second. The integration of machine learning (ML) techniques has revolutionized HFT by enhancing decision-making, optimizing trading strategies, and mitigating risks. This study explores the transformative impact of ML on HFT, focusing on methodologies such as Support Vector Machines (SVM), Random Forests (RF), Deep Learning architectures like Convolutional Neural Networks (CNNs), and advanced techniques including Reinforcement Learning and hybrid models. The research examines these methods in terms of their effectiveness in predictive modeling, pattern recognition, and real-time analytics. Additionally, a comparative analysis of these ML models highlights their advantages, limitations, and adaptability to the dynamic nature of financial markets. By addressing the challenges and opportunities of integrating ML into HFT, this paper provides insights into the future potential of automated trading systems and their implications for market efficiency and stability.

Abstract- Traffic control strategy plays a significant role in obtaining sustainable objectives because it not only improves traffic mobility but also enhances traffic management systems. It has been developed and applied by the research community in recent years and still offers various challenges and issues that may require the attention of researchers and engineers. Recent technological developments toward connected and automated vehicles are beneficial for improving traffic safety and achieving sustainable goals. There is a need to develop a survey on traffic control techniques, which could provide the recent developments in the traffic control strategy and could be useful in obtaining sustainable goals. This survey presents a comprehensive investigation of traffic control techniques by carefully reviewing existing methods from a new perspective and reviews various traffic control strategies that play an important role in achieving sustainable objectives. First, we present traffic control modeling techniques that provide a robust solution to obtain reasonable traffic and sustainable mobilities. These techniques could be helpful for enhancing the traffic flow in a freeway traffic environment. Then, we discuss traffic control strategies that could be helpful for researchers and practitioners to design a robust freeway traffic controller. Second, we present a comprehensive review of recent state-of-the-art methods on the vehicle design control strategy, which is followed by the traffic control design strategy. They aim to reduce traffic emissions and energy consumption by a vehicle. Finally, we present the open research challenges and outline some recommendations which could be beneficial for obtaining sustainable goals in traffic systems and help researchers understand various technical aspects in the deployment of traffic control systems.

Abstract- The ‘Budget Beacon’ is an unadorned web application designed to make it easy for people to manage their finances and monitor their expenses. It provides users with the facilities to make financial decisions and strategies. Incorporating advanced features makes it easier for users to maintain their finances with precision and make more financial decision with precision. The web application gives users the ability to keep track of their daily expenses and break down their spending by category [1].It helps users keep their financial information digitally eliminating the traditional book keeping system.

Abstract- Managing data for diverse types of home service providers can be challenging for users due to communication gaps between providers and recipients. This often leads to unexpected inconveniences for service recipients and missed opportunities for providers to showcase their skills effectively. ServiHub, an on-demand home services platform, bridges this gap by facilitating seamless two-way communication between service providers and recipients. The platform simplifies the process of finding the right service provider and ensures efficient job scheduling for providers. Additionally, a feedback-based rating system enhances the skills of service providers and ensures users receive improved and reliable services over time.

Abstract- The main aim of this project is to design the system by using the micro-controller (ATmega328) and temperature sensor for sensing the room temperature with a small DC fan. The system was designed to maintain the constant and comfortable room temperature by automatically activating the DC fan when the temperature exceeds the normally fixed temperature value and deactivates the DC fan when the temperature value falls below the fixed value. The temperature sensor used here will statically monitors the temperature value of the room. By using the reading data the controller makes the decision either to activate the DC fan or to deactivate the DC fan. This system is the energy saving way that activate the DC fan when only the temperature exceeds the fixed value else the fan will be deactivated. It is one of the best solution for maintaining indoor conditions, minimizing the manual interaction of the user and provide the overall comfort to the user.

Abstract- The restoration of damaged images has become an essential and highly valuable tool in a wide range of technical applications, including space imaging, medical imaging, and numerous other post-processing techniques. These applications often involve the challenging task of correcting images that have been degraded by factors such as blur and noise. Most image restoration methods begin by simulating the processes that cause image degradation, typically focusing on the effects of blur and noise, and then work to approximate the original image. However, in more realistic real-world scenarios, the challenge is to estimate both the true image and the associated blur based on the characteristics of the degraded image, without relying on any prior knowledge of the blurring mechanism. This situation reflects the complexities encountered when dealing with real-world data. This thesis introduces and develops an innovative approach to digital image restoration, utilizing punctual kriging and various machine learning algorithms. The focus of this research is on restoring images that have been degraded by Gaussian noise, achieving a balance between two competing objectives: maintaining smoothness while preserving edge integrity. This approach aims to enhance the effectiveness of image restoration techniques, particularly in situations where the image has been compromised by environmental and other factors.

Abstract- This thesis presents a comprehensive exploration into the design, modeling, and analysis of a wind turbine, employing a multidisciplinary approach to optimize its performance. The blade geometry was generated using QBlade software, a robust tool for blade design in wind turbine applications. The 3D model was then meticulously crafted using SolidWorks, integrating aerodynamic principles and structural considerations. The heart of this project lies in the utilization of SolidWorks Flow Simulation for a detailed analysis of the aerodynamic characteristics of the designed wind turbine. The simulation facilitated a thorough examination of airflow patterns, turbulence effects, and pressure distributions around the blades, offering valuable insights into the efficiency and energy-capturing potential of the turbine under various wind conditions.

Abstract- As climate change continues to detrimentally affect human lives, accurately projecting carbon dioxide (CO2) emissions, one of the largest contributors to climate change, is becoming increasingly critical. However, forecasting CO2 emissions in Taiwan has become challenging due to its rapid development. This paper presents a comprehensive study of 10 univariate and 11 multivariate time series models and then proposes a novel hybrid ensemble model for accurate CO2 forecasting in Taiwan. Our custom dataset, spanning from 1965 to 2022, includes annual data on CO2 emissions as well as gas, coal, and oil consumption. Using standard evaluation metrics, we identified the three top-performing models: Feedforward Neural Network (FFNN), Support Vector Regressor (SVR), and Random Forest Regressor (RFR). We then utilized stacked generalization to combine their predictions with a meta-model. This proposed hybrid ensemble model achieved a MAPE score of 1.398%, demonstrating superior and more robust performance compared to previously proposed models. After extensive optimizations, the model was employed to forecast CO2 emissions in Taiwan for the next 10 years. This study provides a novel hybrid ensemble model and a robust framework for forecasting CO2 emissions, assisting policymakers and industry leaders in making informed decisions to reduce CO2 emissions.

Abstract- In modern world technologies are grooming very fast day by day along with the world semiconductor industry the world of IC is also grooming and enhancing the technologies day by day as we know according to Moore’s law the number of transistors will be double on a chip in every eighteen months that means the size of components will be reducing day by day in the same way types of transistors were introduced like MOSFET and FinFET. FinFET replaced MOSFET, FinFET resolved all the challenges of MOSFET and helped in compact designing of electronic devices, FinFET is widely used in various modern electronic devices because of its structure, fast switching speed, low power consumption and less leakage current.

Abstract- The chassis of a truck is the backbone of the vehicle, incorporating the majority of component systems such as axles, suspension, gearing, cab and trailer, and is typically subjected to the load of the cabin, its contents, and inertia forces induced by rough road surfaces, among other things (i.e. static, dynamic and cyclic loading).In fatigue research and component life prediction, strain analysis is critical for determining the best stress point, also known as the juncture that leads to likely failure. One of the causes that contributes to fatigue loss is this juncture.

Abstract- The greatest challenge in the upcoming decades is to switch from using fossil fuels to a greener form of energy. Solar energy is of the highest priority. However, the frequent change in the sun’s position with respect to the Earth makes it nearly impossible to collect a hundred percent heat energy from the sun. Therefore, the need to improve the energy efficiency of photovoltaic solar panels by building a solar tracking system must be considered. To get maximum energy, PV panels must be perpendicular to the sun’s position. The methodology includes the implementation of an Arduino-based solar tracking system consisting of Light-dependent resistors (LDRs), a PV solar panel, and a servo motor to control the movement of the solar panel based on the position of the sun. The result of this work has clearly shown that the tracking solar panel produces more energy than a fixed panel.

Abstract- Grublers criterion is a foundational concept in kinematics, offering a systematic approach to determining the degrees of freedom (DoF) of planar mechanisms. This study delves into its theoretical basis, exploring its application to various types of plane mechanisms. By analyzing case studies and real-world examples, this research aims to validate the criterion’s utility and highlight its limitations. The findings demonstrate that while Grublers criterion effectively predicts kinematic behavior, it requires adaptation for certain complex mechanisms. The study provides insights into enhancing the understanding and application of this criterion in mechanical design.

Abstract- Emotion recognition plays a vital role in enhancing human-computer interaction systems by enabling empathetic and context-aware AI solutions. This study introduces a hybrid deep learning architecture that integrates BERT for extracting contextual text features and an Artificial Neural Network (ANN) for processing MFCC-based acoustic features. By combining textual and audio modalities, the proposed model effectively addresses the limitations of single-modality approaches. The model is evaluated on the USC-IEMOCAP dataset, encompassing six emotion categories: ‘Happy’, ‘Sad’, ‘Angry’, ‘Neutral’, ‘Frus- trated’, and ‘Excited’. It achieves competitive performance with a weighted F1-score of 0.91 and an accuracy of 86%, outperforming several state-of-the-art methods. The fusion of text and audio features enhances the model’s ability to capture subtle emotional nuances, demonstrating the potential of multimodal learning for robust emotion classification. This research underscores the value of hybrid architectures in advancing emotion recognition for real- world applications.

Abstract- Data mining techniques are used in the numerous industries alongwith the IT sector, Agriculture and education system. Massive technical advancements and opportunities from past decades change the approach and lifestyle of the people. Although data mining techniques are used in the several industries but it is new approach in the Academics. The education system has not greatly profit from the potential of data mining techniuqes. A substantial amount of information are required for the better performance of the students in the academics. There is a vast amount of data are available which can help to find the performance of the students. The role of the data mining technology is to find out the performance of students in academics, the factors also find out which affects the academic performance and also other issues like financial, family background etc. how it effects the performance, how semester wise results so that students aware about the performance and also gender wise how it affects.

Abstract- The purpose of the study was to ascertain the accuracy of blood pressure monitors commonly available in the market. Six devices were chosen including one professional BP monitor for home, clinical and hospital use, manufactured by Mann Electronics India Private Limited, Kota from the market. These devices did not have accessible validation testing results. The subjects for assessment were adults from the general population with varied age groups and sex. The objective was to establish whether the devices conform to the requirements of ISO 81060-2:2018/AMD1: 2020 protocol

Abstract- Efficient algorithms for rendering geometric shapes are fundamental in computer graphics. This study presents a comparative analysis of the Digital Differential Analyzer (DDA), Bresenham’s line-drawing, and Midpoint circle algorithms. We evaluate their performance in terms of computational efficiency, accuracy, and ease of implementation. Python is used as the platform to implement and test the algorithms. Experimental results demonstrate that while DDA offers simplicity in implementation, Bresenham’s algorithm is computationally more efficient for line drawing. The Midpoint circle algorithm proves robust for circular shapes but is relatively complex. This paper provides insights into the algorithms’ suitability for various real-world applications, backed by runtime performance and output quality metrics.

Abstract- Healthcare has changed as an effect of artificial intelligence’s remarkable accuracy and efficiency in medical diagnostics. A technology named artificial intelligence (AI) lets computers along with additional machines to mimic human abilities such as understanding, problem-solving, innovative thinking, autonomy, and the decision-making process Applications and devices with AI capabilities possess the ability to recognize and understand objects. They are able to decode and give response to human speech. AI is transforming the way illnesses are recognized, evaluated, and treated, especially in the field of medical diagnostics. Using machine learning and deep learning algorithms, AI can swiftly and effectively understand enormous quantities of data, offering healthcare professionals insightful information. These developments not only increase the accuracy of diagnoses but also make it possible for early diagnosis and customized treatment plans. In the early days, AI was primarily employed for administrative duties, but its use has risen significantly. Massive quantities of data can now be accurately and quickly evaluated by AI and machine learning systems, which helps healthcare professionals make better decisions. Medical practice can be revolutionised by these technologies, which can interpret medical pictures, discover trends, and even predict the course of diseases. Access to effective healthcare is usually limited in neglected and rural areas, leading to mediocre health outcomes and delayed diagnosis. Existing ways of resolving this issue, such as telemedicine, have struggled to grow in parallel with growing demands for healthcare. According to this method, a system driven by artificial intelligence would be able to comprehend a large volume of medical data, identify symptoms, and converse with patients in order to find out about their medical concerns. The advent of advanced AI- powered technology and the growing popularity of smart assistants like Google and Alexa signal the beginning of an era of change in healthcare innovation.

Abstract- The research project focuses on the design and development of lightweight, high-entropy nanocomposite materials for hard hats and helmets, aimed at enhancing safety across various industrial sectors, including construction and manufacturing. By blending five thermoplastic polymers—high-density polyethylene (HDPE), polycarbonate (PC), polypropylene (PPE), polyethylene terephthalate (PET), and polybutylene terephthalate (PBT) with glass fibers and nanographene, the study produced novel composite materials. Mechanical testing demonstrated improved strength and impact resistance, with a notable 13% weight reduction in the final prototype compared to traditional materials. The project utilized advanced characterization techniques, including FTIR and XRD, to validate the material properties. These innovative materials not only meet industry safety standards but also align with environmental considerations by utilizing readily available raw materials.

Abstract- In this study we try to explore the concept of mobile marketing in a holistic context. The main focus of the research is on consumer’s behaviour towards mobile marketing. The research is conducted through a primary methodology. Both quantitative and qualitative methodology were used. Surveys were conducted from customers of SMEs and interviews were conducted from the managers of those SMEs. The result shows various consumer attitudes towards mobile marketing, which organisations can understand and attract customers.

Abstract- Modern education needs real-time engagement and attendance tracking in order to ensure an effective learning environment. This paper introduces a Smart Classroom Management System, developing together with state-of-art tools like OpenCV for facial recognition and the Mailgun API for effective notifications. The automation of attendance would include sending absence notifications along with the topics missed to students and their parents at login. Furthermore, a camera is turned on during the login session to monitor the activity and engagement levels of the user. The system facilitates instant alerting about inactivity to mentors or parents, thus strengthening accountability. By integrating these technologies, the proposed system is the intelligent, responsive solution for classroom automation, allowing the creation of a more connected and interactive educational ecosystem.

Abstract- Efficient routing is crucial for many practical applications in wireless sensor networks. Nevertheless, they encounter the unavoidable obstacle of restricted energy resources, which underscores the need of developing data transmission mechanisms that optimize the allocated energy to enhance the longevity of the networks and minimize the system’s latency. Implementing efficient clustering and energy management strategies can enhance the longevity of the network while concurrently decreasing the observed delay. The present study introduces a two-tier methodology for reducing unnecessary transmissions in conjunction with particle swarm optimization (PSO). The objective is to minimize the distances inside clusters in order to reduce both latency and energy usage. The evaluation parameters for the proposed method include the delay in the first hop, the latency in the network, and the energy usage. This empirical method has been employed to determine the optimal fitness function so as to optimize latency and energy consumption in WSNs.

Abstract- AI is the science and engineering of creating intelligent machines, particularly clever computer programs. In fact, AI is already being used in healthcare in a number of ways that are pertinent to nurses in both nursing practice and nursing education. It consists of numerous healthcare technologies that improve patient care and change the duties of nurses. The workload of nurses is lessened as a result of it. AI ethics are crucial since the technology can effect not just the outcome for a single patient but also the way it is used in healthcare during the research, design, testing, integration, and continuous usage phases. Mobile health, clinical decision support, and sensor-based technology like voice assistants and robotics are examples of AI tools for nurses.

Abstract- Hotstar, (now Disney+ Hotstar), is the most subscribed–to OTT platform in India, owned by Star India.The major cause of the issue might be an unreliable internet connection or connection that is not operating correctly in hotstar. OTT has boosted experimentation to another level. exchange4media Group held the second edition of its one-day event on OTT titled e4m Play Streaming Media Conference & Metal Announcements on May 12, 2021, at 2 pm. The awards honoured excellence in the on-demand video and audio content. OTT platforms deliver content via the Internet, circumventing the need to pay subscriptions to traditional cable broadcast and satellite TV service providers. Therefore, we are building an OTT platform. We are adding subscription model. The web system is developed with PHP, MySQL and Xampp

Abstract- This study investigates how big data, artificial intelligence (AI), and predictive analytics can work together to transform marketing strategies within the context of Industry 4.0. By utilizing advanced analytical techniques, businesses can enhance their marketing efforts, predict consumer behavior, and optimize resource allocation to improve return on investment (ROI). The research examines the capabilities of AI algorithms and predictive analytics, demonstrating their ability to process large datasets and uncover actionable insights. Through a series of case studies and examples, we highlight how companies across various industries are leveraging these technologies to stay competitive in today’s fast-paced market. Furthermore, the study explores the challenges and ethical concerns related to integrating AI and predictive analytics into marketing strategies. In conclusion, this research underscores the significance of data-driven decision-making in maximizing marketing ROI in the age of Industry 4.0.

Abstract- This research investigatesthe factors affecting to loan defaults of micro credit (special reference to people’s bank branches in Anuradhapura region, Sri Lanka).The study addresses the critical need to understand the factors contributing to loan defaults, arrears, and loan restructuring, providing valuable insights for microfinance institutions to enhance their risk management strategies. The primary objectives of this study are to identify, analyse, and comprehend the factors influencing loan repayment behaviour among microfinance clients at People’s Bank branches in the Anuradhapura region. The research aims to contribute to the existing body of knowledge in microfinance and provide practical recommendations for enhancing the loan repayment process. A quantitative research approach was employed, utilizing Likert scale questionnaires to gather data on socioeconomic factors, loan characteristics, institutional practices, and borrower financial behaviours. The survey was distributed to a representative sample of microfinance clients in the Anuradhapura region. Data analysis was conducted using SPSS and Microsoft Excel, employing statistical methods to draw meaningful insights. The research revealed significant correlations between certain socioeconomic factors and loan repayment behaviour. Income levels, educational background, and employment status demonstrated notable associations with loan default rates. Additionally, institutional factors, such as the loan approval process and collection procedures, played a crucial role in shaping repayment behaviour. This research contributes valuable insights into the multifaceted aspects of loan repayment behaviour in microfinance. By understanding the key determinants, microfinance institutions can tailor their practices to mitigate risks and foster a more sustainable and inclusive financial environment. While efforts were made to ensure the reliability and validity of the research, certain limitations, such as sample size constraints and potential biases, should be acknowledged. Future research endeavours could delve deeper into the cultural and social dimensions influencing loan repayment behaviour. Longitudinal studies may also provide a dynamic perspective on the evolving nature of microfinance clients’ financial behaviours.

Abstract- The purpose of this study was to generate an algorithm able to decipher bots in social media. Prior research shows that variables/parameters affect the detection of AI; however, none attempt to compile an algorithm accurate enough to be deployed into a real-world scenario. Data was collected through mixed methods, in which data was collected online and through questionnaires. Participants included individuals from all demographics, only restricted to demonstrate no bias. Initial results show a strong correlation with variables on the usage of AI. This means that a model which can effectively deduce the usage of AI is plausible. Therefore, the conclusion can be made that it is possible to find bots in social media; however, this is limited to 70% accuracy, given the available resources. Future research should be targeted towards making sure text can be deciphered with more accuracy.

Abstract- In the era of digital transaction people prefer to make online payments and purchases due to the convenience of time, transportation, etc. Credit card fraud has also increased significantly due to the growing trend of e-commerce. Fraudsters try to take advantage of card and internet payment information. Credit card and online payment information is often used by fraudsters for fraudulent purpose. Imbalanced dataset and high dimensionality of data are the key issues observed in credit card fraud detection. The use of various machine learning algorithm has been utilized for identifying anomalies in credit card transaction, focusing on the problem of imbalanced dataset and reduction of dimension which were carefully reviewed and studied. The study investigates the impact of imbalanced datasets on PCA-based fraud detection and provided detailed techniques such as Random Oversampling, SMOTE & Random Undersam- pling to handle imbalanced datasets and various classification as well as anomaly detection methods. Additionally, given the labelled nature of the dataset, various methods are reviewed like Logistic Regression, Random Forests, and Decision Trees. This study analyses and compares the performance of these methods before and after applying PCA and addressing data imbalance to assess their effectiveness in detecting credit card fraud.

Abstract- Database technology has been a central focus for organizations and businesses involved in managing information. As the amount and complexity of data continue to increase, efficient data management has become more critical. This paper examines the wide-ranging uses of database systems across different sectors. It starts with an overview of both relational and non-relational databases, then explores their applications in areas such as enterprise management, retail, education, and government/public services. In enterprise management, databases ensure data is timely, accurate, and reliable, forming the foundation for effective information handling. In retail, they support inventory management, sales analysis, and improve customer interactions. In education, databases help manage student records, support teaching insights, and contribute to online learning platforms. For government and public services, databases enhance information sharing, promote transparency, and are essential for crisis management and emergency response. This paper highlights the diverse and crucial roles of database systems while also addressing current research trends and future advancements in the field.

Abstract- This project demonstrates a high level processing chess engine employing the Minimax algorithm along alpha-beta pruning, one more added feature used is a genetic algorithm which proves useful to make decisions and performance higher. While the Minimax algorithm is a cornerstone of game theory, which helps one to discover best moves and counter-moves in order not to lose in games like chess, with Alpha-beta pruning you can limit the number of nodes that are evaluated and hence restrict computational power needed without loosing optimality. Our evaluation function rates board states, based on which we use a genetic algorithm to fine-tune it. The optimal criteria are formed by the selection and combination of those evaluation functions over generations, while the genetic algorithm evolves a population of candidate solutions. This continuous refinement allows the evaluation function to improve as it gives a better result. While playing, the engine uses the so-called Minimax algorithm with alpha-beta pruning to look ahead and move sequences up to a certain depth for better decision-making. We tackle both tactical and strategic parts of chess in our implementation, showing strong play against humans. The project has had an analysis, which shows that the move selection and game outcomes are superior to conventional Minimax-based engines. This breakthrough in the class of Minimax algorithms achieves higher intelligence levels in computer chess, drastically changing gameplay for both fun and competitive purposes.

Abstract- Social Commerce (S-Commerce) is transforming the retail landscape by combining social media platforms with e-commerce to create a more engaging and personalized shopping experience. This paper looks into the challenges and future opportunities that come with S-Commerce. Some of the main challenges include concerns about data privacy and security, trust issues in online transactions, difficulties in integrating social platforms with e-commerce systems, and managing user-generated content. On the other hand, the future of S-Commerce presents exciting opportunities, such as the use of artificial intelligence (AI) to create customized shopping experiences, the rise of social commerce marketplaces, and the growing significance of video and live-streaming content. These trends provide substantial potential for businesses to improve customer engagement, boost sales, and innovate their digital commerce strategies. The paper delves into these dynamics and discusses how businesses can tackle the challenges while seizing the emerging opportunities in S-Commerce.

Abstract- This application is designed to streamline the analysis of financial statements by allowing users to easily upload company data for comprehensive evaluation. By leveraging advanced algorithms, the application conducts thorough ratio analysis and trend analysis, converting raw financial data into meaningful visual insights, including graphs, pie charts, and heatmaps. These visual representations enhance the understanding of a company’s financial health, revealing trends and performance metrics over time. In addition to historical analysis, the application incorporates sophisticated predictive analytics to forecast the company’s financial performance over the next five years. This feature enables stakeholders to make informed strategic decisions based on projected outcomes. By integrating historical data analysis with predictive modeling, this tool empowers investors, financial analysts, and business managers to identify potential risks and uncover growth opportunities. Ultimately, the application enhances financial decision-making capabilities, providing users with a robust framework for evaluating company performance and making strategic investments. With its user-friendly interface and powerful analytical features, this application is poised to revolutionize how financial data is interpreted and utilized.

Abstract- Children with Dysgraphia, a learning disorder that affects handwriting and fine motor skills, face significant barriers to academic progress and confidence building. This project introduces a novel. By integrating a Raspberry Pi with Optical Character Recognition (OCR) and advanced machine learning algorithms, the system provides precise, real-time feedback on let- ter formation, spacing, and stroke direction. The kit incorporates an intuitive interface, supported by a TFT display, QPC 1010 camera, and peripheral devices, ensuring accessibility and ease of use.To enhance engagement, gamified learning elements are in- tegrated, fostering an enjoyable and motivational environment for skill development. The system seeks to increase self-confidence, enhance motor coordination, and improve handwriting accuracy. By establishing a connection between technology and education. This project provides a portable and scalable solution for schooling that enables kids with dysgraphia to overcome obstacles and succeed academically.

Abstract- A bug is an error in a computer program that causes it to behave unexpectedly or produce incorrect results. The focus of this study is on detecting, analyzing, and fixing of c programming bugs. The process of finding bugs — before users do — is called debugging. Debugging starts after the code is written and continues in stages as code is combined with other units of programming to form a software product, such as an operating system or an application. This research paper is about details explanation about the bug which mostly occurs while doing c programming.

Abstract- This review paper provides an in-depth analysis of diverse energy storage systems, emphasizing their significance, operating principles, and practical applications in tackling contemporary energy issues. As the global shift towards sustainable energy gains momentum, effective Energy Storage Systems (ESS) play a pivotal role in maintaining the balance between supply and demand, especially in the integration of renewable energy sources. The paper explores an extensive array of energy storage solutions, such as Thermal Energy Storage (TES), Chemical Energy Storage (CES), Electrochemical Energy Storage (EcES), Electrical Energy Storage (EES), Hybrid Energy Storage Systems (HES), and Mechanical Energy Storage (MES). By conducting a comparative assessment, it highlights the strengths and weaknesses of each approach and provides insights into emerging trends and challenges within the sector. Furthermore, the study focuses on optimizing Gravity Energy Storage (GES) systems using the Taguchi method to improve energy efficiency and system reliability, showcasing the potential of GES as a viable and adaptable solution for sustainable energy storage.

Abstract- This project brings the entire manual process of Fruits and Herbs Online Shopping which is built using Asp.NET as a front end and SQL Server as a backend. An online Fruits and Herbs shop that allows users to check for various Fruits and Herbs products available at the online store and purchase online. This project helps the users in curing its disease by giving the list of fruits and herbs that the user should consume in order to get rid of its disease. The main purpose of this project is to help the user to easily search for herbs and fruits that will be good for the health of the user depending on any health issue or disease that he/she is suffering from. This system helps the user to reduce its searching time to a great extent by allowing the user to enter its health problem and search accordingly. The admin can add fruits and herbs to the system and its

Abstract- As homes are increasingly adopt smart technologies, their reliability as well as longevity have become paramount to avoid unnecessary downtime and ensure continuous, efficient operations. By incorporating Artificial Intelligence (AI) and Internet of Things (IoT) technologies this research enhances predictive maintenance and thereby contributing sustainability goals. Sensors are utilized to monitor real-time data like temperature, pressure, and vibrations from connected devices and systems. Using the machine learning models – linear regression and decision trees, this research demonstrates how AI can extract actionable insights from sensor data. This research showcases the potential to create more reliable, sustainable, and efficient predictive maintenance solutions that are not only low-cost and accessible but can be adapted for both small-scale and large industrial applications. These advancements will further enhance the predictive capabilities of the system and support long-term environmental sustainability by continuously optimizing resource consumption and reducing waste generation.

Abstract- Modern software systems are becoming increasingly complicated due to which the demand for a reliable, scalable system is on the rise. Cloud-based software-intensive systems (C-SIS) are emerging as the most significant means of meeting these challenges: flexibility, scaling, and increased reliability through distributed computing. This paper looks at the design and implementation of cloud-based systems as they are capable of leveraging the advantages offered by the cloud infrastructure for high availability, fault tolerance, and performance at scale. Cloud-based software-intensive systems are supposed to be a framework for developing systems that are reliable and scalable. The framework brings together the best practices related to cloud architecture, towards automated scaling, load balancing, and fault-tolerance mechanisms to adjust dynamically to varying workloads for always-on service availability. It also discusses the need for microservices and containerization as powerful components for modular and scalable solutions. The results of our experiments demonstrate that this proposed system is able to handle large-scale applications, leading to an understanding of its different performance, fault tolerance, and scalability under certain conditions. This study throws light on how the cloud-based software-intensive systems have a bright perspective to transform the industrial concept, robustly providing high performance and scalable solutions to meet today’s ever-increasing demands of computing environments.

Abstract- Robotics is a field that combines engineering, technology, and science to design, build, and operate robots. Robots are machines that can perform tasks that are repetitive, complex, or dangerous for humans. They can be controlled by humans or operate autonomously. Robotics deals with the design, construction, operation, and use of robots and computer systems for their control, sensory feedback, and information processing. This project presents the design and implementation of a Smart Robotic Rover that integrates an ESP8266 microcontroller with various sensors and modules to achieve autonomous navigation and real-time data transmission. The rover is equipped with ultrasonic sensors for obstacle detection and Previous studies have demonstrated their effectiveness in providing real-time distance measurements, A GPS module for location tracking Such As outdoor navigation and autonomous vehicles Systems, GPS provides accurate location data, which is essential for tasks that require precise positioning. A BMP180 sensor for environmental monitoring, systems for measuring temperature, pressure, and altitude and a servo motor for directional control. The system is controlled remotely via the Blynk platform, and combining it with an ESP32 module for camera control and additional motor functionalities, Research on camera integration in robotics illustrates the benefits of using high- resolution cameras and efficient streaming protocols for real-time visual feedback. The project aims to deliver a comprehensive robotic system that is controllable via a web interface and Blynk application. Blynk’s native IOS and Android mobile apps are most often used as client-facing UI to remotely control the connected devices and visualize data from them in the dashboard The vehicle is designed for autonomous navigation, real-time environmental monitoring, and user-friendly remote control. Allowing for real-time data visualization and interaction. This paper discusses the system architecture, sensor integration, software development, and testing results of the Smart Robotic Rover.

Abstract- An exact sequence E:0⟶A ⟶B ⟶C ⟶0….(1) is called T-pure if any torsion R- module is projective and relative to it and F- copure if any torsion free R- module is injective relative to it. . Since Tis closed under factors and F is closed under sub-modules. Here Walker’s [19] criterion of Co-purity is also applicable in this situation. We also know that 〖Pext〗_T (M,A)=0 if and only if an R- module M is T-pure projective and〖 Pext〗_F (A,M)=0 if it is F – copure injective for all A⊆M. In particular 〖Pext〗_T (T,A)=0 for all T∈T. We write the torsion sub-module of A⊆M by σ(A). Walker proved that the class of I- pure (J- copure) sequences form a proper class whenever I(J) is closed under homomorphic images (sub-modules) of an R- module M and if I(J) is closed under factors (sub-modules) then for any I- pure (J- copure) sequence E:0⟶A ⟶B ⟶C ⟶0 if E ∈π^(-1) (I) (E ∈i^(-1) (I)) and hence in this case the earlier notion of purity coincides with Walker’s I- purity (J- copurity ) . A sequence E:0⟶A ⟶B ⟶C ⟶0 is I- pure (J- copure) if and only if given C^’≤C∈ I, then there existsB’≤B such that B^’≅C’ and A∩B^’=0. We consider an another stronger notion of purity than the Cohn’s purity[11]. If FG denotes the class of all finitely generated R-modules, which is closed under factors. We shall try to develope some characterizations of FG-purity and to determine its relationship with the T- purity and T_C- purity in cyclic torsion modules We also derive some relations of absolutely ϑ- pure modules with it . We try to relate it the with conditions for T- pure projectivity Teply and Golan [18].. We relativize the above concept and also relate it with finite projectivity of Azumaya [8] with respect to a torsion theory and to study the inter-relationship between these concepts. Finite σ-projectivity, (FG,σ)- pure flatness, cyclically σ- pure projectivity and cyclically σ- pure flatness, the concept of locally σ- projectivity and locally σ- splitness are also considered here and we study its inter-relationship with (FG,σ)- purity and semi-simple module.

Abstract- The widespread adoption of Cloud Computing technology in industry, education, and government sectors has made it a standard for IT implementation. Data leakage is one of them, particularly, the unauthorized transfer of information from one environment to any other domain. Data leakage has been a problem much before data was maintained digitally. It is therefore vital to prevent and detect this leakage so that the cloud service provider’s reputation is not jeopardized. Furthermore it is integral that users’ data confidentiality, integrity, and availability is not compromised. Inmost cases, data are handled by a third-party software whose security procedures are unknown to the user. This software serves as a bridge between the user and the cloud service provider. To resolve the issue of data leakage, several methods have already been proposed such as watermarking, cryptographic and probabilistic techniques. This paper, however, aims to use a revised version of the probabilistic approach by encrypting the user data even before it is uploaded through a portal. During the encryption process, a user ID is embedded into the encrypted file. When this file is accessed by another consumer, their user ID is also embedded into the file. Hence it makes it easier for the algorithm to detect the guilty agent by comparing the leaked file against the user file. A list of users who have accessed the file is thus maintained.

Abstract- The increasing demand for electricity and environmental concerns have created a critical need for advanced energy management solutions. This study presents an IoT and cloud-based analytics system that provides real-time insights into power consumption, enabling efficient energy utilization. Leveraging ThingSpeak as the cloud platform, the system integrates smart meters to monitor voltage, power factor, and energy trends. Key contributions include real-time anomaly detection, dynamic visualization, and customizable alert systems. The proposed methodology enhances user engagement and supports scalability for diverse energy applications.

Abstract- In the modern era, Cardio-vascular disease has high prevalence and rate of mortality which proves how critical, identification and intervention strategies are, further highlighting the importance of incorporating this in developing heart disease prediction systems. The heart prediction system research revolves around using AI-driven techniques techniques to strengthen and make heart disease risk prediction robust and effective. The paper explains methodology, dataset characteristics, experimental setup, results and the design of the models in a AI-driven techniques heart prediction system. Additionally, the practical implications of the research output are discussed regarding the use of the system in real life for alleviating heart disease predictions and strategies.

Abstract- The digital world thrives on the secure exchange of information across vast networks. This paper explores cryptography as a fundamental pillar of network security, ensuring data confidentiality, integrity, and authenticity. We delve into the core objectives of network security and how cryptography achieves them through encryption techniques. We explore both symmetric-key and asymmetric-key cryptography, along with their strengths and limitations. The paper further examines cryptography’s role in guaranteeing data integrity and sender authentication. We acknowledge the limitations of cryptography, including computational demands and the looming threat of quantum computers, which necessitates the development of post-quantum cryptography. Finally, the paper emphasizes the crucial role of ongoing research and development in cryptography to safeguard the ever-expanding digital landscape.

Abstract- This paper presents a comparative analysis of handwritten digit recognition performance between LabVIEW and PyTorch frameworks, utilizing a Convolutional Neural Network (CNN). The model is designed to classify digits from the MNIST dataset, which consists of 28×28 grayscale images of handwritten digits (0–9). The dataset includes 60,000 training images and 10,000 test images, providing a standardized benchmark for evaluating model performance. Metrics such as accuracy, training time, memory usage, and inference speed are evaluated. The results provide insight into the strengths and weaknesses of these frameworks in terms of efficiency, scalability, and usability. Results indicate that while both frameworks are effective, PyTorch offers faster training and inference, whereas LabVIEW demonstrates marginally better training accuracy.

Abstract- This review explores the effects of water-proofing admixtures on concrete properties, focusing on their impact on durability, strength, and performance. Water-proofing admixtures are additives designed to reduce water permeability and enhance the resistance of concrete to moisture ingress, which is critical for ensuring the long-term durability of structures, especially in environments with high humidity, rainfall, or exposure to aggressive chemicals. The study systematically examines the various types of water-proofing admixtures, including crystalline, hydrophobic, and integral admixtures, and evaluates their performance characteristics such as compressive strength, permeability, durability, and resistance to chemical attacks. The influence of these admixtures on concrete microstructure, hydration process, and pore structure is discussed in detail. Additionally, the review highlights the factors that affect the effectiveness of water-proofing admixtures, such as admixture type, dosage, water-cement ratio, and curing conditions.

Abstract- The proliferation of smart meter technology offers vast opportunities for harnessing real-time data to optimize energy consumption, predict demand, and support sustainable energy grids. This paper explores the integration of artificial intelligence (AI) techniques, such as machine learning and deep learning, into smart meter data analytics, enhancing the accuracy of predictions and anomaly detection. With the rise of big data from millions of connected devices, AI-based analytics are vital to efficient energy management. We present a comparative analysis of various AI models used for smart meter data analytics and propose improvements for their real-time applications.

Abstract- Herbal Drug Development Plant Selection and Identification The first step is identifying a plant with potential medicinal properties. Ethnobotanical surveys, historical use, and scientific literature guide this process. Extraction and Isolation of Active Constituents Different extraction methods (e.g., solvent extraction, steam distillation, supercritical fluid extraction) are employed to isolate the active ingredients from plant material. Techniques like chromatography and spectroscopy are used to identify and purify these compounds. Standardization Standardization ensures that a herbal product contains a consistent amount of active compounds in each batch. This is crucial for reproducibility and efficacy. Preclinical Studies Laboratory testing on animals and in vitro models to assess the biological activity, toxicity, and pharmacokinetics of the herbal product. Clinical Trials Human trials are conducted to evaluate the safety, efficacy, and dosage of the herbal drug. Technological Approaches in Herbal Drug Development Extraction Techniques Solvent Extraction The most common method, where solvents like ethanol or water are used to extract bioactive compounds. Supercritical Fluid Extraction (SFE) Uses supercritical CO2 as a solvent, offering a cleaner and more efficient extraction method. Microwave-Assisted Extraction (MAE) Uses microwave energy to enhance the efficiency of the extraction process. Ultrasonic Extraction Utilizes high-frequency sound waves to enhance solvent penetration and compound release. Formulation Development Herbal products may be formulated into various forms

Abstract- This research aimed to develop a prototype of an automated Greenhouse Agricultural System (AGAS) for efficient and sustainable cultivation of plants in tropical regions. The AGAS prototype was built using an Arduino Uno microcontroller, which monitors and regulates temperature, humidity, and soil moisture, utilizing sensors and a servo motor for water distribution. Data is transmitted to a website for remote monitoring and control. Data were analyzed mainly using percentages, mean and t-test of independent means. Results showed that, the system achieved a 100% success rate in six trials, demonstrating accurate soil moisture detection, effective servo motor operation, and reliable pump functionality; the website is 100% success rate in four trials in recording analog values, it successfully maintained optimal growing conditions for lettuce, showcasing its potential to improve crop yields and resource efficiency; and the AGAS is efficient compared to the traditional greenhouse system in terms of temperature, humidity and soil moisture. This highlights the significance of AGAS in addressing the challenges of unpredictable weather patterns and resource scarcity in tropical regions. Further development, including a user-friendly application, HVAC system, and error detection mechanisms, is recommended. The AGAS holds the potential to revolutionize greenhouse agriculture, promoting sustainable practices and enhancing food security.

Abstract-Due to the continuous progress of Internet technology, computer network communication service has replaced the traditional short message service and multimedia message service. In order to ensure the security of the instant messaging system, some advanced security encryption algorithms are used in the communication system to prevent attacks and information leakage. By using encryption algorithms, the network security research based. Our system operates on a network of nodes, where each node plays a crucial role in ensuring the security and integrity of transmitted data. The SHA-256 algorithm is employed for generating hash values, providing a secure and efficient means of verifying data integrity. Furthermore, we implement AES (Advanced Encryption Standard) for file encryption, enhancing the confidentiality and privacy of sensitive information. AES is a symmetric key encryption algorithm renowned for its strength and efficiency, by combining SHA-256 for integrity checking and AES for encryption, we Include Face Change Attaining methods to prevent from attackers In Face Change that can support both anonymizing real IDs among neighbor nodes and collecting real ID-based encountering information. For node anonymity, two encountering nodes communicate anonymously. Our system offers a robust defense against various cyber threats, including data breaches and unauthorized access. Prevent malicious actors from intercepting or tampering with encrypted data, our system employs advanced encryption techniques and secure communication protocols.

Abstract-The introduction of artificial intelligence ultimately changing the media landscape, this lead to digital divide between traditional media and modern media. This research paper emphasize on the challenges opportunities strength and weakness faced by traditional media in this artificial intelligence era. Modern technology can replace the older one see but in the case of print media that is News Papers and magazines are not replaceable. Digital technological advancements are a part of our life but usage of traditional print medias became a habit of our generation. Through comparative analysis and expert interview this paper prose how artificial intelligence influence traditional media.

Abstract-In today’s data-driven world, organizations are faced with an ever-growing volume of raw data that often requires sophisticated analysis to extract meaningful insights. However, the complexity of these insights can make it difficult for decision-makers, especially non-experts, to understand and act on the information. This paper proposes a novel framework that leverages Artificial Intelligence (AI) to automatically generate data narratives, transforming raw data into human-readable insights. The framework integrates data preprocessing, advanced AI techniques, and natural language processing (NLP) models to construct compelling and insightful narratives. We present a detailed methodology, including the use of clustering, trend analysis, and regression models to extract key insights from diverse data sources. The generated narratives are tested on multiple datasets, demonstrating their effectiveness in conveying actionable insights in an easily understandable format. Our results show that AI-generated data stories not only provide clarity and context but also enhance decision-making processes across various industries. Future work will focus on enhancing the framework’s adaptability to real-time data and improving narrative customization for different stakeholders.

Abstract-With the fast development of computer technology, research in the fields of multimedia (text, image, audio and video clip) security, image processing and robot vision have recently become popular. Digital image watermarking techniques is one of the best techniques for image authentication. Watermarking algorithms are designed to embed and extract digital watermarks within digital content, such as images, audio, or video. The basic objective of the watermarking technique is to enhance imperceptibility, capacity and robustness. When developing an effective watermark method, it’s necessary to have a highly balanced trade-off between imperceptibility, capacity, and robustness. In this paper we presence about watermarking system, requirements for digital image watermarking, challenging issue of watermarking, application of watermarking, importance of watermarking, image watermarking classification, various watermarking techniques, attacks on watermarking process, performance measure for evaluating the image quality using metrics and a short view of watermarking tools. The work gives a view on various watermarking schemes in digital images that give new ideas to improve the already existing techniques.

Abstract-This paper explores the innovative business model of Embrace Innovations, a social enterprise committed to tackling the critical issue of infant mortality in resource-constrained settings, especially in India. Founded with the mission of offering affordable and effective infant care solutions, Embrace has developed the Embrace Infant Warmer as a cost-effective alternative to traditional incubators. In analysing the operational strategies, market dynamics, and impact of Embrace’s products on neonatal health outcomes, the study uses a mixed-method approach through applying qualitative and quantitative research methods. Conducting in-depth interviews and surveys with relevant stakeholders which lead to important discoveries about how Embrace was able to effectively penetrate these markets through its unique value proposition: affordability, portability, and user-friendliness. The paper discusses the challenges of the organization, such as high maintenance costs and regulatory compliance issues. Ultimately, this research would highlight the potential for Embrace Innovations to transform infant healthcare through continuous innovation and strategic partnerships, thereby contributing significantly to reducing infant mortality rates globally.

Abstract-Quantum state transitions in topologically protected materials have garnered significant attention for their potential applications in quantum computing, spintronics, and material science. Predicting these transitions under varying external conditions remains a challenge due to the intricate interplay of quantum effects and topological invariants. This study proposes an AI-based framework that leverages deep learning techniques to predict quantum state transitions in such materials with high precision. The framework utilizes a custom neural network architecture trained on data derived from simulations and experimental results. By incorporating topological invariants and environmental variables as features, the model accurately predicts phase transitions and provides insights into the factors driving them. The results demonstrate over 95% prediction accuracy, outperforming traditional simulation methods in terms of computational efficiency and scalability. This work lays the foundation for integrating AI into quantum materials research, offering tools for designing next- generation quantum devices.

Abstract-Data Mining and AI are revolutionize the medical field by providing enhanced understanding of disease trends, increasing accuracy in diagnosis, and driving the development of tailored healthcare solutions. This document investigate into how data mining and Artificial intelligence methodologies influence various dimensions of human health, with an emphasis on predictive analytics, diagnostic imaging, real time health tracking, and customized treatment options. Techniques in data analytics, including categorization, grouping, and rule based mining, are utilized on extensive data sets, assisting healthcare professionals in making informed data centric choices for disease prevention and management. AI techniques, featuring ML and deep learning frameworks , significantly improves diagnoses, particularly within medical Imaging, where these models showcase remarkable accuracy in detecting diseases at early stages. In addition, wearable technology and mobile health platforms offer continuous data for ongoing health assessment, facilitating timely medical interventions. Nonetheless, applying data mining and AI in healthcare, introduces challenges, especially concerning data privacy, interpretability of models and ethical issues. This research addresses these hurdles and proposes strategies to bolster data protection, enhance model clarity, Forster patient confidence. With ongoing progress and mindful applications, data mining and Artificial intelligence present considerable potential for enhancing health outcomes, supporting preventive measures and leading to individualized and precision medicines.

Abstract-With the increased demand for advanced childcare solutions, the development of an intelligent and reliable baby monitoring system using the versatile Raspberry Pi has been encouraged. This project is focused on creating a comprehensive monitoring solution that prioritizes the safety and well-being of infants through the integration of sophisticated audio monitoring and environmental sensing capabilities using various sensors. This new system utilizes the central processing unit Raspberry Pi 4 Model B and interfaces nicely with high-quality microphone capability to pick up sound; it comes equipped with environmental sensors capable of monitoring essential conditions in temperature and humidity. The functionalities are advanced and include motion detection, which notifies caregivers upon any baby movement, while cry detection informs caregivers of a crying baby within seconds of its cry. A two-way audio system that connects caregivers with their children can converse and communicate with the baby real-time, providing yet another level of interaction and comfort. The application will be designed so that parents or guardians, through a mobile application, can have instant alerts when their baby’s condition arises from virtually any location. This system was designed to be cost-effective and easy to set up; it can be highly scaled to meet the needs of the users. There is always an integration for push notifications via mobile devices. By incorporating these advanced features and focusing on user-friendly design, this baby monitoring system represents a significant advancement in the realm of smart parenting tools, addressing the critical need for reliable and intelligent childcare solutions in contemporary households.

Abstract-In today’s interconnected global supply chains, the integration of predictive analytics and advanced cybersecurity measures has become a pivotal strategy for fortifying risk management and enhancing resilience. The COVID-19 pandemic underscored the vulnerabilities of supply chains, prompting organizations to adopt cutting-edge technologies to mitigate disruptions and ensure continuity. This paper explores the critical interrelationship between predictive analytics, cybersecurity, and supply chain resilience, highlighting their combined potential to create robust and adaptable systems. The study delves into predictive analytics for risk identification and mitigation, the role of cybersecurity in addressing digital threats, and the need for a holistic risk management approach. Empirical evidence and theoretical insights are discussed to present actionable strategies for organizations aiming to enhance their supply chain resilience in an increasingly uncertain global environment.

Abstract-The rise of Electronic Health Records (EHRs) has revolutionized the way health care is practiced globally, particularly in providing patients with effective and precise care. Nevertheless, given the types of information EHRs contain, they are vulnerable to malicious attacks and access by unauthorized persons. The paper focuses on the importance of end-to-end encryption, role-based access control, and audit logs in maintaining optimal security of EHR data. These aspects are discussed in such a way that their combined effect is presented along with the individual functionality of circumstances and how each of them contributes to security, the legal requirements, and the stakeholders.

Abstract-The measurement of the acoustical transmission loss of an expansion chamber muffler with a rectangular cross section and different cross section aspect ratios is presented in the study. An essential component of noise management for reducing noise from gas flow sources, such as machinery exhaust, is a muffler, also known as a silencer. As a component of an internal combustion engine’s exhaust system, mufflers are usually placed along the exhaust pipe to lessen noise. One-dimensional waves are utilized as simulation tools.

Abstract-Wireless networks have become ubiquitous in our modern digital landscape, facilitating connectivity and enabling seamless access to information. However, the inherent vulnerabilities of wireless communication pose significant security challenges. This paper provides a comprehensive overview of wireless network security, examining various aspects such as encryption, authentication mechanisms, access control, intrusion detection, and physical security measures. The discussion begins by highlighting the importance of encryption protocols, such as WPA2 and WPA3, in safeguarding data transmitted over wireless networks. Strong encryption mechanisms are essential for ensuring the confidentiality and integrity of sensitive information, protecting against eavesdropping and data tampering. The aim of this study was to review some literatures on wireless security in the areas of attacks, threats, vulnerabilities and some solutions to deal with those problems. It was found that attackers (hackers) have different mechanisms to attack the networks through bypassing the security trap developed by organizations and they may use one weak pint to attack the whole network of an organization. Overall, this paper provides valuable insights into the various techniques and strategies for enhancing security in wireless networks.

Abstract-Small extracellular vesicles secreted by most cell types have been crucial for intercellular communication in transferring biologically active molecules, such as proteins, lipids, and RNA. The vesicles regulate the physiological processes that contribute to pathological conditions such as cancer. Exposure to heavy metals, including arsenic, cadmium, and lead, disrupts communication by interfering with the biogenesis of exosomes, the cargo that is transferred within them, and their release. This review discusses the molecular mechanisms through which heavy metals affect exosomes, their downstream effects on recipient cells, and the potential of exosome-based biomarkers for detecting and mitigating heavy metal toxicity. The discussion also brings out therapeutic opportunities and future research directions.

Abstract-Web automation testing has become an essential component of modern software development, enabling developers to ensure the quality, functionality, and performance of web applications. It leverages automated tools and frameworks to perform repetitive and complex testing tasks, thereby reducing human error and speeding up the development lifecycle. This paper explores the methodologies, tools, and advancements in web automation testing, presenting a proposed system designed to enhance efficiency and reliability. Through an experimental prototype, we demonstrate the effectiveness of the proposed architecture in streamlining testing processes. The paper also addresses the challenges faced in script maintenance, scalability, and adaptability of automated tests in dynamic web environments. Finally, we outline future directions for research in this domain, emphasizing the role of AI and real-time analytics in shaping the next generation of automation testing tools.This paper explores the methodologies, tools, and advancements in web automation testing, focusing on overcoming challenges like script maintenance, handling dynamic elements, and frequent application updates. Through an experimental prototype, the proposed system demonstrates improved efficiency by integrating modular test designs and advanced reporting mechanisms.

Abstract-This project focuses on developing a comprehensive platform that bridges the gap between farmers and consumers, allowing users to purchase agricultural products directly from farmers. The application provides seamless online payments, user and farmer profile management, and real-time inventory updates. Administrators play a key role in fostering trust by onboarding verified farmers and uploading schemes that are beneficial to them. Future expansions include vehicle and land renting functionalities as well as fertilizer management to support farmers further. This app allows farmers to effortlessly rent agricultural machinery, such as tractors and harvesters, at nominal costs, empowering them with technology that was previously out of reach. Through user-friendly interfaces and robust backend support, farmers can connect with rental providers, manage bookings, and access real-time updates. Administrators oversee the system, ensuring transparent transactions and efficient dispute resolution, while users can explore and contribute to the ecosystem. Our goal is to uplift the agricultural community by reducing operational costs, enhancing productivity, and fostering collaboration. By leveraging digital tools, this app bridges the gap between modern technology and traditional farming practices, paving the way for a sustainable and prosperous agricultural future.

Abstract-Aim: This study aims to evaluate the effectiveness of the K-Means algorithm in comparison to Logistic Regression (LR) for analyzing a diabetes dataset. Diabetes is a critical and potentially fatal condition, and as it remains incurable, its prevention and management are vital public health concerns. Materials and Methods: For this research, a substantial dataset was sourced from the Kaggle Dataset – Diabetes Disease Analysis and Prediction, encompassing 13 clinical features pertinent to diabetes. The sample consisted of 10 instances, with additional control variables incorporated to account for possible confounding factors and enhance the accuracy of the findings. Both K-Means and Logistic Regression algorithms were employed for predictive analysis. Discussions: Two distinct analyses were conducted to assess the performance of the K-Means algorithm against the proposed LR algorithm. The outcomes indicated that the enhanced LR method yielded superior results. Result: The mean accuracy for the LR algorithm was recorded at 76.8%, while K-Means clustering achieved a mean accuracy of 46.2%, demonstrating that LR outperformed K-Means. The results suggest that machine learning techniques can effectively predict diabetes. The p-value obtained in this study was 0.001, which is less than the threshold of p=0.05, underscoring the importance of utilizing LR for diabetes prediction. Conclusion: The findings reveal that the extended LR algorithm achieved greater accuracy compared to the K-Means algorithm. Nonetheless, it is noted that Logistic Regression would benefit from a larger sample size to enhance the precision of the results.

Abstract-The swift advancement of artificial intelligence (AI) has dramatically altered the e-commerce landscape, allowing companies to improve customer experiences through increasingly customized and emotionally responsive methods. E-commerce platforms can now offer tailored interactions that connect with customers on an emotional plane by utilizing AI to identify and react to their emotional states, whether through written, spoken, or behavioral indicators. Emotion-cognizant AI systems can comprehend sentiments expressed across various contact points, including chatbots, customer support exchanges, product suggestions, and individualized marketing efforts. These AI systems employ sentiment analysis, natural language processing, and emotional intelligence algorithms to modify response promotions, and product recommendations based on weather a customer is content, irritated, perplexed, or enthusiastic. Consequently, customers receive highly personalized and empathetic interactions that boost satisfaction, build trust, and increase conversion rates. This study examines the newest innovations in AI-powered emotional intelligence for e-commerce, its capacity to enhance customer engagement, and its ramifications for businesses aiming to improve customer loyalty through a more profound understanding of emotional dynamics.

Abstract-This study examines the impact of oil sector deregulation on the Nigerian economy, focusing on key economic segments such as households, finance, firms, public sector, and international trade. Utilizing a mixed-methods approach, this research combines quantitative survey data with qualitative observations to capture a comprehensive view of deregulation effects. A sample of 400 respondents, selected through stratified random sampling from relevant stakeholders in the oil industry, was surveyed to ensure representative insights across affected sectors. Anchored in the Circular Flow Model and the General Equilibrium Theory, the study explores how deregulation policies influence macroeconomic stability, cost structures, and resource allocation within Nigeria. Key findings indicate both positive and negative consequences: while deregulation contributes to fiscal savings and potential investment in infrastructure, it has also led to inflationary pressures and increased operational costs for firms. Limitations of this study include its restricted focus on short-term impacts and challenges in capturing the broader social implications of policy shifts. These insights offer policymakers a nuanced understanding to refine future economic strategies.

Abstract-The traditional methods of managing assignments are steadily becoming outdated due to their numerous drawbacks, including inconvenience, inefficiency, and a lack of accuracy. These limitations have prompted a growing need for more effective solutions in the educational domain. With the rapid advancement of web technologies, web-based management systems have gained significant traction and are being widely adopted across various sectors. This paper presents a novel AcademEase: Revolutionizing Online Assignment Management for Enhanced Academic Efficiency that not only integrates the most effective features of existing commercial systems but also introduces innovative functionalities tailored specifically for modern assignment management needs. The proposed system addresses critical gaps in traditional practices by offering a comprehensive platform designed to streamline assignment handling processes for both administrators and students. Key features of the AMS include a user-friendly interface that simplifies the user experience, ensuring that assignments are managed in a convenient, efficient, and systematic manner. Furthermore, the system is designed with a high degree of portability and extensibility, making it adaptable to various educational environments and capable of evolving with future technological advancements. To safeguard sensitive data and ensure secure operations, the system incorporates robust, multi-layered security strategies that enhance its overall reliability. By leveraging the power of web technologies, this innovative system not only improves assignment management workflows but also sets a new benchmark for efficiency, usability, and security in academic institutions. This paper delves into the design, functionality, and benefits of the AMS, showcasing how it effectively meets the demands of modern educational practices.

Abstract-Opinion mining, another name for sentiment analysis, is a crucial task in natural language processing (NLP) that enables the extraction of subjective information from text. Sentiment analysis can use machine learning algorithms to classify opinions in text into three categories: neutral, negative, and positive. In the Internet age, social networking sites have grown rapidly, making them an essential tool for communicating emotions to individuals all over the world. Many people use music, video, photos, and text to express their ideas or perspectives. Sentiment analysis is inadequate in certain applications; therefore, emotion detection is necessary to accurately ascertain a person’s emotional and mental condition. The degrees of sentiment analysis, different models, and the steps involved in sentiment analysis and emotion detection, challenges faced are all explained in this review study.

Abstract-Purpose: This thesis investigates how user-centered design (UCD). , user experience (UX) principles can have a remarkable impact on digital marketing campaigns, focusing on consumer engagement. , conversion rates. With the rising complexity of online consumer behavior. , ever-increasing competition in digital marketplaces, leveraging strategic UX design has emerged as a powerful tool for marketers. Methodology: The study adopts a mixed-methods approach, incorporating both quantitative data (such as user analytics, A/B testing results)., qualitative insights (such as interviews, focus groups). A framework is established to evaluate campaign performance metrics, user satisfaction scores, . , conversion funnels within diverse digital platforms—social media, e- commerce websites, mobile applications. Findings: The findings suggest that user-centered design elements—such as intuitive navigation, responsive interfaces, consistent br. ,ing, . , personalization—lead to higher levels of user satisfaction, br. , trust, , customer retention. In addition, campaigns designed around UX principles witnessed a measurable uptick in conversion rates compared to those that lacked deliberate UX planning. Implications: This thesis contributes to the existing literature on digital marketing by incorporating comprehensive UX design strategies. By applying user-centered methodologies, marketers can cultivate more engaging. , persuasive digital experiences, thus boosting key performance indicators (KPIs) such as click-through rates, time on site, average order value., customer lifetime value.

Abstract-Purpose: The idea of a multilayer inverter originated from the development of inverters to more than two layers in order to lessen distortion from the basic sinusoidal waveform. One drawback of employing multiple level inverters is the installation of more switches, which raises system bulk and cost and reduces system dependability due to the increased component count. In order to address the issue of the system becoming bigger, more expensive, and less dependable with less distortion, this work provides a nineteen-level inverter (19-LI) with fewer switches than a symmetrical H-bridged nineteen-level inverter. The idea is developed using the MATLAB platform, then analysis is done to determine how valuable the final product is.

Abstract-New technology to reduce pollution in motor vehicles and increase mileage.

Abstract-Distributed Denial of Service (DDoS) attacks are a significant threat to the stability and availability of network services, often resulting in financial and reputational damage to organizations. Detecting and mitigating these attacks is a complex task due to their large scale, diverse attack vectors, and evolving nature. This paper explores various methods for DDoS attack detection and classification, with a focus on leveraging machine learning and statistical techniques. The primary objective is to identify attack patterns in network traffic data and classify them in real-time to distinguish between legitimate and malicious activities. We review traditional methods such as signature-based detection and anomaly detection, alongside modern machine learning-based approaches, including supervised and unsupervised classification techniques. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are evaluated for their effectiveness in detecting various types of DDoS attacks, including volumetric, protocol, and application-layer attacks. Additionally, we discuss the challenges posed by high traffic volumes, the need for low-latency detection, and the impact of adversarial tactics on detection systems. Finally, the paper highlights the importance of developing robust, scalable, and adaptive classification models that can efficiently handle the evolving nature of DDoS attacks in dynamic network environments.

Abstract-The continuous evolution of digitalization and the rapid growth of tools and technologies have led to a parallel rise in sophisticated cyberattacks. Attackers deploy advanced techniques to compromise critical systems, steal sensitive data, and disrupt operations. Traditional vulnerability detection and penetration testing methods, which rely heavily on manual processes and frameworks like Metasploit, are labour-intensive, time-consuming, and prone to human error. To address these challenges, this research presents the development of an Automated Penetration Testing Tool (APTT) to streamline cybersecurity assessments. Integrated with the Metasploit framework, APTT automates reconnaissance, vulnerability scanning, and exploitation, reducing time complexity and human error. Initial testing in diverse environments showed a 50% reduction in testing time and improved reliability of results, making it scalable and adaptable to various security needs.

Abstract-The increasing frequency of malware attacks through document files poses a significant risk to personal and organizational data security. This project focuses on developing a real-time malware detection system as a browser extension to protect users from malicious documents. By leveraging machine learning techniques and heuristic analysis, the extension scans documents uploaded or downloaded through the browser, identifying potential threats in real time. The solution ensures high accuracy in detecting various malware types while maintaining lightweight operation for seamless user experience. The system incorporates a user- friendly interface, automated scanning, and secure cloud-based updates for the detection engine. The proposed extension bridges the gap between cybersecurity and accessibility, providing a practical tool for users to protect themselves from file-based threats. Testing and evaluation demonstrate its reliability and effectiveness, making it a valuable addition to modern cybersecurity solutions.

Abstract-Food security remains one of the major problems in the world. Wild edible mushrooms constitute an important source of food due to their nutritional and medical values, as well as a source of income for populations. This study aims to domesticate wild edible mushrooms that grow in the Bamboutos department. An ethnomycological survey was conducted among 154 people through direct and semi-structured interviews in the 04 Districts and in 15 villages of the Department. The macroscopic identification of the different species was carried out in situ using identification keys. The domestication test was carried out in the laboratory, the species inoculated on PDA medium and transplanted onto cereal seeds then onto corn cobs in order to obtain seeds. The seeds obtained were tested on corncob and sawdust substrates with the use of two additives, wheat bran and corn bran.The different substrates composed of slaked lime, urea, fungicide and water. This work reveals that the largest percentage of respondents is made up of men (65%). Knowledge related to the edibility of mushrooms is mainly transmitted by family members (68%). The wild edible mushrooms collected (04 species) belong to the Lyophyllaceae family and the Termitomyces genus: Termitomyces letestui, T. striatus, T. aurantiacus and T. brunneopileatus. The seed production process was a complete success. The substrate made up of corn stalks and wheat bran presented the best weights at harvest (221,66±3,36 g , 89,24±3,74 g and 93,58±7,13g). However, the carpophores obtained from the harvested and cultivated species were undifferentiated.

Abstract-It often has brought inconveniences of unsafe situations and discomfort to its customers owing to vehicular breakdown. Typical roadside assistant applications that come out face problems such as high response times, small cover-up areas, and lack of real-time diagnostic capabilities among other problems. This research proposal intends to establish an innovative, web-based platform called Repair that has AI and LBS technologies integrated to provide real-time assistance for vehicles. The core feature of Repair is an AI-powered chatbot that can troubleshoot the most common vehicle issues independently. Advanced NLP techniques are applied to guide users through the diagnostic steps and provide solutions to problems such as flat tires, dead batteries, or other engine issues. When the problem exceeds the capabilities of the chatbot, the system uses Geolocation API technology to pinpoint the user’s exact location and dispatch the nearest available towing service. This seamless integration of AI and geospatial technology ensures faster response times, reducing user waiting periods and improving service efficiency.

Abstract-With the rapid growth of digital solutions, on- demand service applications have emerged as valuable tools for addressing daily needs, such as home maintenance and freelancing tasks. This survey paper provides a comprehensive review of ten existing mobile and web-based applications designed to connect customers with service providers across a range of sectors. By examining each system’s features, user experience, and limitations, this study highlights the commonalities and distinct approaches used to facilitate service matching. Key findings reveal that, while these applications effectively streamline access to services, they often face challenges such as limited service categories, regional restrictions, and issues with pricing transparency and real-time availability. Through a comparative analysis, this paper identifies trends, limitations, and potential improvements for future on-demand service platforms.

Abstract-This paper studies misspecification tests for stochastic linear regression models, including the Durbin-Watson test, Ramsey’s regression specification error test, Lagrange’s multiplier test, and UTTS’ rainbow test. Specification errors arise when there are deviations from the underlying assumptions of a stochastic linear regression model, impacting associated inferences. Specifically, errors may occur in specifying the error vector ( ) and the data matrix ( X ). Common causes of specification errors involve including irrelevant independent variables or excluding relevant ones in the stochastic linear regression model. Previous research by Ivan Krivy et al. (2000) presented two stochastic algorithms for estimating parameters in nonlinear regression models. In a 1984 paper, Russell Davidson et al. developed a computational procedure for a variety of model specification tests. Ludger Ruschendorf et al. (1993) constructed nonlinear regression representations of general stochastic processes, focusing on specific representations for Markov chains and certain m-dependent sequences. This study contributes to the understanding of misspecification in stochastic linear regression models, utilizing a range of tests to identify errors in model assumptions and parameter estimation. The insights gained from these tests can enhance the accuracy and reliability of regression model inferences.

Abstract-Data Science has become a pivotal element in the evolution of modern Business Intelligence (BI), transforming the way organizations process and analyze vast amounts of data to uncover actionable insights. By leveraging advanced techniques such as machine learning, statistical modeling, and data visualization, businesses can enhance decision-making processes and gain a competitive edge. This report delves into the synergistic integration of Data Science within BI frameworks, illustrating its practical applications through diverse use cases including predictive analytics for forecasting trends, customer segmentation for personalized marketing strategies, and fraud detection to safeguard organizational integrity.While the potential benefits are immense, the implementation of Data Science in BI is not without its challenges. Key hurdles include ensuring data quality and consistency across sources, overcoming integration complexities with legacy systems, and addressing skill gaps in data literacy among employees. These challenges require strategic planning, investment in technology, and workforce training to be effectively mitigated.The report also explores emerging trends shaping the future of BI, such as the increasing adoption of artificial intelligence, real-time analytics, and the use of natural language processing for intuitive data interactions. Finally, it provides actionable recommendations for organizations to build robust and scalable BI strategies, emphasizing the importance of fostering a data-driven culture, prioritizing ethical data practices, and continuously evolving with technological advancements.

Abstract-Testing is a task, which is performed to check the quality of the software and also this process is done for the improvement in software at the same time. Software testing is a critical component of the software development lifecycle, ensuring that applications meet specified requirements and function as intended. Over the years, a wide range of tools and methodologies have been developed to enhance the effectiveness, efficiency, and scalability of testing processes. This paper provides an overview of the most widely used tools and methodologies for software testing, focusing on both manual and automated approaches. It explores popular testing tools for different testing types such as unit testing, functional testing, performance testing, and security testing, with a detailed examination of frameworks like Selenium, JUnit, and TestNG. Additionally, the paper highlights key methodologies, including Agile testing, Behaviour-Driven Development (BDD), and Continuous Integration/Continuous Delivery (CI/CD) integration, emphasizing how these approaches align with modern development practices. The research also addresses the strengths and weaknesses of different tools and methodologies, offering insights into their suitability for various types of projects and testing environments. Challenges related to test maintenance, scalability, and the integration of testing within DevOps pipelines are also discussed. By analysing the current landscape of software testing tools and methodologies, this paper aims to provide valuable guidance for teams looking to improve their testing strategies, optimize workflows, and ensure higher- quality software releases.

Abstract-Women and children today deal with a number of problems, including sexual attacks. The victims’ life will undoubtedly be greatly impacted by such atrocities. It also has an impact on their psychological equilibrium and general wellbeing. The frequency of these acts of violence keeps rising daily. Even schoolchildren are victims of sexual abuse and abduction. In our society, a nine-month-old girl child is not protected; she was abducted, sexually assaulted, and ultimately killed. Seeing the abuses of women makes us want to take action to ensure the protection of women and children. Therefore, we intend to present a device in this project that will serve as a tool for security and guarantee the safety of women and children. GSM microcontroller.

Abstract-Agriculture plays a crucial role in India’s economy, supporting approximately 55% of rural households and contributing about 18% to the nation’s GDP. At the time of independence, the agricultural sector was underdeveloped, with limited land dedicated to key crops. In response, the Indian government implemented programs to modernize farming by introducing high-yielding seed varieties, fertilizers, mechanization, and irrigation. However, higher the costs of these modern techniques presented challenges for many farmers. To make agricultural inputs more affordable to the farmer, the government introduced subsidies based on recommendations from the Food Grain Price Committee. While subsidies are essential for addressing market inefficiencies and promoting societal benefits like poverty alleviation and food security, they are often criticized for issues like poor targeting and governance challenges. In spite of this fact, subsidies have significantly influenced agricultural production, particularly during the Green Revolution. As India moves toward sustainable agricultural development, subsidies remain a vital tool for balancing economic, environmental, and social objectives.

Abstract-Agriculture is crucial for the Indian economy. Rice is a staple crop to more than half of world population. Conventional transplanted rice production faces issues like lowering water tables, lower productivity, methane emissions, soil health deterioration, and labour scarcity. Puddling, a crucial step in wetland rice production, can improve transplanting and weed management but can also cause soil conditions that are unfavourable for post-rice crops. Puddled transplanted rice is energy-intensive and contributes to climate change by emitting methane and nitrous oxide. Direct seeded rice (DSR) technologies can minimize environmental impact and increase productivity. DSR was introduced in 2009-10 to address labour constraints, rising labour prices, and a diminishing groundwater table in Punjab. With agricultural water requirements expected to increase by 20% by 2050, DSR requires about 50% less water under Indian conditions. This review article studies the condition of current rice production practices, the major constrains and DSR, its advantages along with agronomy as substitute of current TPR method.

Abstract-The recognition of food on social media has spawned an growing interest in automated food recognition and recipe era. We gift a system that combines both neighborhood and global functions to create spatiotemporal convnet, this paper outlines the venture of creating particular but special recipes from snap shots of food. on this paper, we use convolutional neural networks and a generative antagonistic community to robotically convert meals photographs into textual content based totally recipes. To generate coherent and contextually relevant recipe instructions, our approach combines image popularity techniques based totally on Convolutional Neural Networks (CNN) [17] for the identity and category of food with herbal Language Processing (NLP)—fashions utilized in conjunction to analyze textual data. extra records: The authors present a large-scale dataset with various meals categories and corresponding recipe (i.e., cooking method) for schooling their proposed framework. at the photograph- to-recipe mission, our experiments set up that it is able to certainly generate a recipe carefully matching with food objects in snap shots. Quantitative assessment benchmarks on preferred datasets display superiority as compared to baseline models and qualitative evaluation verifies that our architecture can produce human-like recipe commands. these consequences assist our approach as a benchmark for more state-of-the-art packages closer to automated culinary content creation by way of offering users with more food-related experience in digital interfaces.

Abstract-The PhilHealth Konsulta program aims to improve healthcare access and affordability in the Philippines by offering full primary care services such as consultations, diagnostic testing, and prescriptions. This study looks at the quality and availability of diagnostic examinations provided by accredited PhilHealth Konsulta providers in Baguio City. A quantitative research approach was used, with a survey disseminated to staff and outpatients from various PhilHealth Konsulta facilities in Baguio City. The study used a Likert scale to assess satisfaction levels in 10 areas of service quality and diagnostic availability, ranging from 1 (least satisfied) to 5 (very highly satisfied). A total of 117 people responded, including 48 personnel and 69 outpatients. Findings indicate generally high satisfaction levels among both healthcare providers and patients regarding consultation services, queue management, and patient instructions within the PhilHealth Konsulta framework. However, moderate satisfaction was noted regarding the availability of medications and diagnostic tests, highlighting potential areas for enhancement in inventory management and diagnostic infrastructure. Disparities between staff and patient perceptions suggest a need for improved communication and alignment in service delivery expectations. While the PhilHealth Konsulta program in Baguio City typically satisfies the demands of patients with moderate to high satisfaction, there are several crucial areas that need to be addressed to increase service quality and diagnostic availability. The study emphasizes the importance of increasing diagnostic test availability through enhanced equipment procurement and supply chain management. Strengthening healthcare manpower by recruiting additional staff and implementing training programs to optimize service delivery efficiency is also advised. Furthermore, leveraging technology to streamline administrative processes and improve patient management systems can enhance overall patient experience and operational effectiveness. This research contributes valuable insights to policymakers, healthcare managers, and practitioners involved in optimizing primary healthcare delivery under the PhilHealth Konsulta program. By addressing identified gaps and leveraging strengths, this study aims to support efforts towards achieving equitable healthcare access and improving health outcomes for residents of Baguio City and similar settings across the Philippines.

Abstract-This paper examines the increasing importance of robust cybersecurity measures in the digital payments industry. As the volume and value of online financial transactions continue to grow exponentially, the sector faces a corresponding surge in cyber-attacks, necessitating advanced cybersecurity protocols. This study explores key cybersecurity measures and implementation strategies, including encryption, multi-factor authentication (MFA), tokenization, artificial intelligence (AI)-based fraud detection, and regulatory compliance, to safeguard digital payments against various cyber threats. Through a detailed review of existing literature, case studies, and statistical analysis, the article provides strategic insights into how organizations can enhance security in digital payment ecosystems, maintain compliance, and achieve resilience in the face of evolving cyber threats.

Abstract-This paper presents a comprehensive study of scalar and vector control techniques for three-phase induction motors fed by inverter topologies. Scalar control, commonly known as Voltage/Frequency (V/f) control, offers a simple, cost-effective method for motor control but is limited in its precision, torque regulation, and dynamic response. In contrast, vector control (or field-oriented control) decouples the motor’s torque and flux components, providing enhanced performance, including faster response times, improved speed and torque accuracy, and reduced harmonic distortion. MATLAB/Simulink simulations are used to evaluate both methods under various load and speed conditions, demonstrating the superior dynamic performance, accuracy, and reduced harmonic content of vector control, making it ideal for high-performance industrial applications.

Abstract-Lifestyle diseases, such as cardiovascular diseases, diabetes, obesity, and hypertension, are significantly influenced by environmental factors and individual habits, including diet, physical activity, and stress. Advances in bioinformatics have allowed researchers to leverage genomics, proteomics, metabolomics, and transcriptomics data for early detection and effective management of these diseases. By analyzing gene expression, protein interactions, and metabolic pathways, bioinformatics helps identify biomarkers and therapeutic targets. This paper explores how bioinformatics-driven approaches can aid in understanding the molecular mechanisms behind lifestyle diseases, facilitating early diagnosis, personalized treatments, and improved health outcomes.

Abstract-Designing scalable mobile applications requires more than just robust code; it involves architectural foresight, optimized data models, and efficient network communication strategies. In this article, we present a comprehensive guide to effective system design for scalable mobile apps. Using real-world examples, we explore advanced data modeling techniques, API architecture (REST vs. GraphQL), and real-time data handling using Server-Sent Events (SSE) and WebSockets. Additionally, we examine design patterns such as Model-View-Presenter (MVP) and the use of Dependency Injection for managing complex dependencies. This paper explores a technical roadmap for developers looking to build scalable, maintainable mobile applications capable of handling growing user bases and evolving requirements.

Abstract-The current pandemic of COVID-19 for global medical care has high demand on rapid and correct diagnosis, especially in a cardiac population with prior heart disease being a large proportion among these patients. In this work, a predictive machine learning (ML) model based on convolutional neural networks (CNNs) is proposed to recognize COVID-19 and heart disease from chest X-ray images. The COVID-19 positive and normal X-ray images were used to train the CNN model. The objective behind was to automate the diagnosis so that it helps in early detection of diseases which can save lives and improve patient management. The model was accurate and demonstrated promising results in clinical scenarios.

Abstract-This paper investigates the factors influencing the adoption of digital payments globally. It discusses the drivers, challenges, and potential future research areas required to enhance the digital payment ecosystem. Emphasis is placed on technology advancements, consumer preferences, and regulatory frameworks, with a data-driven approach. Tables, graphs, and statistical analyses provide insights into the current adoption trends across regions. Future research directions focus on improving the security, user experience, and accessibility of digital payments.

Abstract-This paper explores the role and impact of Generative Artificial Intelligence (AI) in developing countries, emphasizing its potential to address significant socio-economic challenges. Unlike traditional AI, which primarily focuses on decision-making based on existing data, Generative AI can create new content, making it a powerful tool for innovation. This technology offers unique opportunities for sectors such as healthcare, education, agriculture, and infrastructure development, particularly in nations with limited resources and technological infrastructure. Generative AI can revolutionize healthcare by enhancing diagnostic tools, supporting drug discovery, and enabling remote medical services. In agriculture, it assists in optimizing crop yields and improving food security through advanced monitoring techniques. Additionally, the technology can personalize educational experiences and democratize access to learning materials. Despite these advantages, the adoption of Generative AI faces challenges, including ethical concerns, data privacy issues, and the risk of job displacement. The paper concludes that Generative AI holds immense potential to drive sustainable development in developing countries. However, careful implementation and strategic investments in infrastructure and education are required to overcome existing barriers and ensure equitable access to these technologies.

Abstract-The recent upsurge in the demand of PV and wind systems is due to the fact that they produce electric power without hampering the environment by directly converting the solar radiation into electric power. However the solar radiation, wind never remains constant. It keeps on varying throughout the day. The need of the hour is to deliver a constant voltage to the grid irrespective of the variation in temperatures, wind pressure and solar isolation. We have designed a circuit such that it delivers constant and stepped up dc voltage to the load. We have studied the open loop characteristics of the PV array and wind system with variation in temperature and irradiation levels. Then we coupled the PV array and wind system with the boost converter in such a way that with variation in load, the varying input current and voltage to the converter follows the open circuit characteristic of the PV array and wind system closely. At various isolation levels, the load is varied and the corresponding variation in the input voltage and current to the boost converter is noted. It is noted that the changing input voltage and current follows the open circuit characteristics of the PV array and wind system closely.

Abstract-In this work, a Microgrid (MG) test model based on the 14-busbar IEEE distribution system is proposed. This model can constitute an important research tool for the analysis of electrical grids in its transition to Smart Grids (SG). The benchmark is used as a base case for power flow analysis and quality variables related with SG and holds distributed resources. The proposed MG consists of DC and AC buses with different types of loads and distributed generation at two voltage levels. A complete model of this MG has been simulated using the MATLAB/Simulink environmental simulation platform. The proposed electrical system will provide a base case for other studies such as: reactive power compensation, stability and inertia analysis, reliability, demand response studies, hierarchical control, fault tolerant control, optimization and energy storage strategies.

Abstract-Over the past few decades, the rapid expansion of the Internet in India has brought significant challenges in ensuring network security. Network security encompasses the strategies and policies implemented by users to protect and oversee the network infrastructure from unauthorized access. This concept is crucial for both private and public networks in India, safeguarding communications and transactions. In recent years, India’s networks have experienced substantial attacks from unauthorized entities. This paper examines the current network infrastructure and security policies in India, identifies the prevalent types of attacks, and proposes advanced technologies to enhance the robustness of India’s network security framework.

Abstract-This is a review of various image processing methods used in diagnosing glaucoma, an irreversible eye disorder of optic nerve results nerve cell damage. Glaucoma causes slow vision loss and is largely prevalent in rural and semi-urban populations, but people suffering from the disease can be found just about anywhere. The current method to diagnose retinal diseases mainly relies on the analysis of fundus images obtained from a retina through advanced image processing techniques. Image registration, fusion, segmentation, feature extraction, enhancement, morphological operations, medical image understanding are few of the standard methods used for detecting Glaucoma and different eye diseases along with GLCM based analysis and its pattern matching classification statistical techniques used. These methods play a critical role in increasing accurateness with early diagnosis and treatments results required for eye practices.

Abstract-In the order of the World massive quantities of power are inspired in residential buildings leading to a unenthusiastic impact on the surroundings. Also, the number of wireless connected strategy in use around the World is constantly and rapidly increasing, leading to potential health risks due to over exposer to electromagnetic emission. An opportunity appears to decrease the energy consumption in residential buildings by introducing smart home automation systems. Multiple such solutions are available in the market with most of them being wireless, so the challenge is to design such systems that would limit the quantity of newly generated electromagnetic radiation. For this we look at a number of wired, serial communication methods and we successfully test such a method using a simple protocol to switch over data between an Arduino microcontroller board and a Visual C#.Net app running on a Windows computer. We aspire to show that if desired, smart home automation systems can still be built using simple viable alternatives to wireless communication.

Abstract-The banking sector is under increasing pressure to ensure secure and seamless digital payment processes. Tokenization, a method of securing sensitive payment data, has emerged as an effective strategy for mitigating security risks and ensuring compliance with Payment Card Industry Data Security Standards (PCI DSS). This paper explores the implementation of tokenization strategies within the banking sector, emphasizing its role in achieving PCI compliance. Through case studies, statistics, and the presentation of real-world examples, the paper highlights both the challenges and benefits of adopting tokenization strategies.

Abstract-The banking sector is under increasing pressure to ensure secure and seamless digital payment processes. Tokenization, a method of securing sensitive payment data, has emerged as an effective strategy for mitigating security risks and ensuring compliance with Payment Card Industry Data Security Standards (PCI DSS). This paper explores the implementation of tokenization strategies within the banking sector, emphasizing its role in achieving PCI compliance. Through case studies, statistics, and the presentation of real-world examples, the paper highlights both the challenges and benefits of adopting tokenization strategies.

Abstract-Renewable energies are being introduced in countries around the world to move away from the environmental impacts from fossil fuels. In the residential sector, smart buildings that utilize smart appliances, integrate information and communication technology and utilize a renewable energy source for in-house power generation are becoming popular. Accordingly, there is a need to understand what factors influence the accuracy of managing such smart buildings. Thus, this study reviews the application of machine learning prediction algorithms in Home Energy Management Systems. Various aspects are covered, such as load forecasting, household consumption prediction, rooftop solar energy generation, and price prediction. Also, a proposed Home Energy Management System framework is included based on the most accurate machine learning prediction algorithms of previous studies. This review supports research into the selection of an appropriate model for predicting energy consumption of smart buildings.

Abstract-The objective of this synopsis is to provide a control scheme of a power flow management of a grid connected hybrid PV-wind-battery. The hybrid PV wind-battery system is connected to a multi-input transformer coupled bidirectional dc-dc converter and using a fuzzy controller. The power from the PV along with battery charging/discharging is controlled by a bidirectional buck-boost converter. The power from wind is controlled by a transformer coupled boost half-bridge converter. A single-phase full bridge bidirectional converter is used for feeding ac loads and interaction with grid. The proposed converter design has lessened number of power transformation stages with less segmentally, and diminished misfortunes contrasted with existing grid connected hybrid frameworks. In this proposed work analyzing the multi response of a grid connected hybrid PV-wind-battery in different cases. In the proposed system has two renewable power sources, load, grid and battery.

Abstract-This Review deals with two stage solar PV power generating system with improved power quality in three-phase distribution system. This system not only feeds the power to the grid but it also provides the load compensation, power factor correction and harmonics elimination. For this, a double stage system is used where first stage is a DC-DC boost converter, which performs the MPPT (Maximum Power Point Tracking). For extracting maximum power from the PV string, an incremental conductance based MPPT algorithm is used. Moreover, in second stage a voltage source converter (VSC) is utilized. For control of VSC, a damped-SOGI (Second Order Generalized Integrator) algorithm is proposed. By using damped-SOGI based control algorithm, fundamental active and reactive power components of load currents are extracted for estimating the reference grid currents. After comparing these reference grid currents with sensed grid currents, these produce the switching pulses for the grid tied VSC. A prototype of the proposed system is developed in the laboratory. Test results are shown to validate the design and control algorithm under steady state and dynamic conditions at linear and nonlinear loads.

Abstract-today’s business need is more complicated than before. That is why the existing ERP needs to be updated and need to be empowered by Artificial Intelligence techniques. In this regard, this manuscript has provided an overview of the Intelligent ERP System.

Abstract-Artificial Intelligence (AI) is rapidly advancing, transforming industries and reshaping the global job market. While there are concerns that AI will replace human workers, this paper argues that AI will complement rather than substitute the human workforce. The paper explores the irreplaceable human qualities such as emotional intelligence, creativity, and ethical decision-making, and emphasizes the importance of AI-human collaboration. The research draws on a wide range of literature and studies to analyze how AI is enhancing, not replacing, job roles, and contributing to the creation of new job opportunities. The conclusion emphasizes that AI and humans will co-evolve, leading to a more dynamic, adaptive, and skilled workforce in the future.

Abstract-This meta-analysis investigates the impact of inclusive ecologies of knowledge on promoting equitable and sustainable futures in education, synthesizing findings from peer-reviewed journal articles published between 2010 and 2024. The primary objective was to evaluate the effectiveness of inclusive educational practices in fostering equity and sustainability. Studies were selected based on explicit inclusion criteria, focusing on empirical research that addressed inclusivity and sustainability within educational contexts. A comprehensive search of databases including PubMed, ERIC, Web of Science, and Scopus was conducted, and the risk of bias in the included studies was assessed using the Cochrane Collaboration’s tool. The synthesis of results, encompassing 35 studies with a total of 4,500 participants, revealed a moderate positive effect of inclusive practices on educational outcomes (Cohen’s d = 0.45, 95% CI: 0.30–0.60). Limitations include variability in study designs and potential bias in some studies. The findings underscore the importance of integrating diverse knowledge systems into education to achieve equitable and sustainable futures. The review was neither registered nor funded.

Abstract-Over the years, several optimization techniques has been developed for the real life applications. The traditional methods do not solve the nonlinear objectives. This paper presents the review of four popular optimization techniques: genetic algorithm (GA), differential evolution (DE. They can be applied to the linear, non-linear, differential and non-differential problems. The related description for each procedure of optimization is presented.

Abstract-The paper presents an approach for the optimal dispatch of active and reactive power with an aim to generate the optimal generation schedule satisfying the equality and inequality constraints and minimizing the cost of operation of generating units by using Genetic Algorithm and Differential Evolution. The approaches have been applied to IEEE 30 Bus system and the obtained results are compared.

Abstract-Ransomware has emerged as one of the most significant and widespread cyber threats in recent years. This form of malicious software locks or encrypts victims’ data, demanding a ransom in exchange for restoring access. The growing sophistication of ransomware attacks has made them increasingly difficult to detect and mitigate, causing severe economic and operational damage across industries. This paper presents a comprehensive analysis of ransomware, its evolution, types, attack mechanisms, and the defensive measures necessary to combat its spread. We also explore the economic implications of ransomware and present future trends in the fight against these cyberattacks. Finally, we propose best practices for organizations to reduce their vulnerability to ransomware attacks and present case studies on successful and failed mitigations.

Abstract-Ransomware has emerged as one of the most significant and widespread cyber threats in recent years. This form of malicious software locks or encrypts victims’ data, demanding a ransom in exchange for restoring access. The growing sophistication of ransomware attacks has made them increasingly difficult to detect and mitigate, causing severe economic and operational damage across industries. This paper presents a comprehensive analysis of ransomware, its evolution, types, attack mechanisms, and the defensive measures necessary to combat its spread. We also explore the economic implications of ransomware and present future trends in the fight against these cyberattacks. Finally, we propose best practices for organizations to reduce their vulnerability to ransomware attacks and present case studies on successful and failed mitigations.

Abstract-Tracking players and the ball in football games is crucial for accurately evaluating team strategies and individual performance. To derive meaningful metrics such as players’ positions, ball possession, and tactical movements throughout a match, continuous tracking of both players and the ball is required. Traditionally, these analyses are conducted manually by professional analysts. However, automated systems using advanced image processing and machine learning techniques have begun to enhance the efficiency and accuracy of such analyses. In this paper, we explore a method utilizing YOLOv8 for object detection and tracking, combined with K-means clustering and homography-based transformations, to provide a comprehensive real-time analysis of football games. We discuss the integration of these technologies into a user-friendly application for coaches and analysts to enhance tactical planning and performance evaluation in sports.

Abstract-A serious and pervasive lung disease with a poor diagnosis rate is tuberculosis. Following the vacuity of high-resolution coffin x-rays, deep literacy can now yield results for the successful discovery of this unpleasant complaint and other possible operations in the health sector. This study presents a new deep learning algorithm for tuberculosis identification using a coffin x-ray image bracket to acquire geographical data. It combines the ImageNet dataset with two popular, trained vgg16 and vgg19 models. The system that is being described is validated through trials using the chest x-ray dataset. After assessing the model on the test set, we receive a score of 0.9992 for each of the criteria (delicacy, perfection, recall, and f1-score).

Abstract-Banking system vulnerabilities have made us vulnerable to fraudulent activities that seriously harm the bank’s reputation and financial standing in addition to harming clients. An estimated large sum of money is lost financially each year as a result of financial fraud in banks. Early discovery aids in the mitigation of the fraud by allowing for the development of a countermeasure and the recovery of such losses. This research proposes a machine learning-based method to effectively aid in fraud detection. In order to combat counterfeits and minimise damage, the artificial intelligence (AI) based model will expedite the check verification process. In order to determine the association between specific parameters and fraudulence, we examined a number of clever algorithms that were trained on a public dataset in this article.

Abstract-The construction industry faces increasing pressure to adopt sustainable practices due to resource depletion and waste management challenges. This review critically examines the use of Portland Slag Cement (PSC) in combination with Recycled Aggregate Concrete (RAC) to enhance sustainability and performance in construction. The analysis consolidates research on the mechanical properties, durability, and environmental impact of PSC-RAC composites. Findings show that PSC enhances compressive strength, tensile strength, and long-term durability while reducing the carbon footprint of concrete production. The review highlights the superior performance of PSC in comparison to traditional cementitious materials, particularly in harsh environments. However, challenges remain regarding the variability in the quality of recycled aggregates, workability issues, and economic feasibility. This review emphasizes the need for standardized quality controls for recycled materials and advocates for further research into long-term performance and the integration of PSC with advanced materials such as Nano-Silica. Comprehensive studies and cost-benefit analyses are recommended to fully explore the feasibility of PSC-RAC in both structural and non-structural applications.

Abstract-The multiplier is a crucial component in digital signal processing. Many scientists have attempted—and continue to attempt—to construct multipliers that satisfy the two flowing pan criteria of fast speed, low power consumption, consistent design, and fewer zones. This is made possible by technological advancements. They are appropriate for a range of applications needing high speed, low power, and less VSI consumption because they can even combine these two objectives into a single multiplier. In This paper present modified dadda multipliers using approximate multiplier with high speed and energy economy is discussed. This way, speed and energy efficiency are increased at the cost of a slight inaccuracy, as the computationally intensive part of the multiplication is bypassed. Whereas Isim Simulator is used for simulation, Xilinx 14.7 is used for implementation. Data from test bench validation indicates that it provides a higher accuracy than the others. Based on the simulation results, the suggested multiplier design outperforms earlier designs in terms of space, latency, speed, and power. Unlike prior proposals which could only construct 16 or 32 bit multipliers, the proposed multipliers can be constructed with 64 bits.

Abstract-This paper introduces the RecoilLogger component, a lightweight utility designed to track and log state changes within Recoil-based React applications. The component provides developers with the ability to monitor both current and previous state values, aiding in debugging and state management performance analysis. The paper outlines the implementation, use cases, and potential applications of the RecoilLogger, discussing its methodology in comparison to existing logging utilities in React. Results demonstrate its effectiveness in state tracking without causing performance overhead or altering the UI.

Abstract-Vertical farming is becoming a valuable complement to traditional agriculture, enhancing sustainable food production as climate pressures increase. Initially, vertical farming focused on technological advancements like design innovation, automated hydroponic systems, and advanced LED lighting. However, recent studies emphasize improving resilience and sustainability, particularly through water quality and microbial life in hydroponic environments. Plant growth-promoting rhizobacteria (PGPR) have proven effective in boosting plant growth and resilience to both biotic and abiotic stress. Using PGPR in plant-growing media enhances microbial diversity, helping reduce reliance on chemical fertilizers and pesticides. This overview explores the history of vertical farming, its economic, environmental, social, and political opportunities and challenges, and the role of the rhizosphere microbiome in advancing hydroponic systems.

Abstract-This paper presents the design and construction of an obstacle detection system for railway level crossings. The focus of this research is on reducing accident rates attributed to obstructions between the gates of the level crossing. Research indicates that approximately 30% of railway accidents at level crossings are resulting from obstacles blocking the tracks. To address this issue, we developed a system utilizing an Arduino Uno microcontroller, along with ultrasonic and reed switch sensors, and a GSM module for real-time alerts. While the ultrasonic sensors are deployed to monitor the gate crossing arena, the reed switches are positioned 3km away from each gate to detect the arrival/departure of the train. Such that when there is any obstacle detected the GSM triggers sms alert to the train operators for a possible halt to create room for evacuation of the obstacle. By facilitating timely responses, this system aims to decrease the likelihood of accidents, thereby enhancing safety for both rail and road users. This innovative solution highlights the potential for improved safety measures within railway infrastructure.

Abstract-Effective management and treatment of these stones depend on early and precise detection. Ultrasound and X-ray are two conventional methods for kidney stone detection, but their resolution and accuracy are limited. Because of its increased resolution and capacity to produce precise anatomical information, computed tomography (CT) imaging has grown in reliability. However, it takes a lot of experience and time to interpret CT scans for kidney stone detection. Recent developments in Convolutional Neural Networks (CNNs) provide a promising solution to these problems. CNNs, a class of deep learning algorithms, have demonstrated remarkable performance in image analysis tasks by automatically learning hierarchical features from large datasets.

Abstract-India’s water sector is crucial for economic growth, public health, and environmental sustainability. With a population exceeding 1.4 billion, the water demand has risen sharply due to urbanisation, agriculture, and industrialization. However, the sector faces significant challenges, including water scarcity, pollution, and inadequate infrastructure. With 18% of the world’s population but only 4% of the world’s water sources, India grapples with water scarcity in many regions. India is the world’s largest user of groundwater that extracts more than any other country in the world and accounts for nearly 25 percent of the world’s extracted groundwater. With an estimated $250 billion investment requirement over the next 20 years, the Indian water sector offers immense opportunities for both domestic and international investors. This report highlights the structure of the water value chain in India, identifies investment opportunities, and names the key players and beneficiaries in the ecosystem.

Abstract-Bioethanol is generated by fermenting sugars obtained from biomass such as crops, agricultural waste, and organic refuse, and is a sustainable and eco-friendly energy option. It provides a long-term solution to fossil fuels, which has the capacity to decrease greenhouse gas emissions and combat climate change. Potato peel waste (PPW) is one of the many feedstocks that shows potential for bioethanol production because of its high starch content. PPW is a waste product from the potato processing sector, commonly thrown away or utilized for less valuable purposes. This study investigates the possibility of using PPW as a productive raw material for bioethanol manufacturing, specifically examining its preparation, breakdown, conversion, and purification stages. Even though bioethanol from PPW shows potential, economic and technical limitations arise due to high moisture levels, composition variability, and the requirement for substantial pre-treatment processes. However, the use of PPW for bioethanol production is in line with worldwide initiatives for sustainable energy, waste reduction, and the circular economy.

Abstract-Late blight, caused by Phytophthora infestans, continues to pose a significant threat to potato production globally. While traditional breeding methods have been used to create resistant cultivars, these methods can be slow and often face limitations due to the availability of genetic resources. Marker-assisted selection (MAS) provides a more efficient and accurate approach by using molecular markers to identify plants that possess resistance genes. This review offers a thorough overview of MAS for late blight resistance in potatoes, discussing its historical development, genetic foundations, molecular markers, and the steps involved in its application. Key topics include the identification of resistance genes and their corresponding markers, the establishment of PCR conditions for marker amplification, and the combination of MAS with traditional breeding techniques. The review also addresses the challenges and future directions of MAS, emphasizing the importance of ongoing marker development, maintaining genetic diversity, and adapting to changing pathogens. In summary, MAS is a valuable tool for improving late blight resistance in potatoes. By integrating MAS with traditional breeding methods and tackling its challenges, breeders can create cultivars that are more resilient to this destructive disease, thereby supporting sustainable potato production.

Abstract-This research aims to address critical challenges in agricultural sustainability by proposing a multifaceted approach to the detection and prediction of diseases affecting cotton plants. The objectives of this study are threefold. Firstly, the research focuses on the classification of cotton plant leaves, essential for accurate disease diagnosis. Through dataset analysis, normalization techniques, and feature extraction using Local Binary Patterns (LBP), cotton plant leaves are effectively differentiated from other foliage. Classification is accomplished utilizing Lightweight Convolutional Neural Networks (CNN), with performance parameters rigorously evaluated to ensure efficacy. Secondly, the study extends its scope to the classification of diseases affecting tomato plant leaves, offering insights into disease identification methodologies applicable to cotton plants. Leveraging the Coral Reef Optimization approach for feature extraction and a hybrid classifier comprising ResNet50 and VGG16 architectures, the system achieves precise disease classification. Lastly, the research addresses the critical need for predictive analytics in disease management by forecasting the occurrence of diseases in cotton plants. Utilizing historical time series weather data, machine learning and deep learning models, specifically Quantile Regression Forests coupled with Long Short-Term Memory (LSTM) algorithms, predict temperature and relative humidity parameters crucial for disease occurrence. By integrating these objectives, this study endeavors to provide a comprehensive framework for proactive disease management in cotton cultivation, thereby contributing to sustainable agricultural practices and food security.

Abstract-CRISPR-Cas technology has revolutionized the field of crop biotechnology, offering precise and efficient tools for enhancing the nutritional value of plants. This review highlights the current applications of CRISPR-Cas in biofortifying staple crops to combat global malnutrition. By editing specific genes, researchers have been able to increase essential nutrients such as vitamins, minerals, and proteins. However, challenges remain, including off-target effects, regulatory and biosafety concerns, and ethical considerations. Future directions point toward innovations in precision editing, multiplex gene editing for complex traits, and integration with synthetic biology and traditional breeding. Additionally, harmonizing global regulatory frameworks and ensuring equitable access to CRISPR technologies will be essential for realizing its potential to improve food security. This review underscores the transformative potential of CRISPR-Cas to address global nutritional deficiencies and enhance crop resilience in the face of climate change, ultimately contributing to a sustainable and food-secure future.

Abstract-The purpose of this research is to use Natural Language Processing (NLP) to categorize customer reviews into three groups: favorable, negative, and neutral. We employ machine learning models to categorize sentiment by preprocessing textual data. Matplotlib is then used to illustrate the results using area plots, pie charts, and keyword-based analysis. Our investigation shows how sentiment analysis, which provides actionable insights generated from consumer feedback, can advise firms on how to improve customer satisfaction and experience.

Abstract-Legumes are important sources of nutrients and phytochemicals. Phytochemicals are plant derived chemicals known to possess many properties, including anti-oxidant, anti-microbial and physiological activities. Though phytochemicals are vital to both plants and animals, they are not established as essential nutrients and they can also have adverse effects by functioning as anti-nutrients. Processing affects the nutritional values of plant-based food and such food products may lose part of their functionality as these chemicals are sensitive to the impact of processing methods. Therefore, the objective of this study was to evaluate the phytochemical contents of legume-based lima beans/cowpea diet combinations so as to recommend the best combination to maximize their pharmacological potentials and reduce the anti-nutritional effects. Quantitative analysis of phytochemical constituents of the formulated diet combinations were carried out using standard procedures for oxalate, alkaloids, flavonoids, saponin, cardiac glycosides, tannin, phytate, cyanogenic glycoside while spectrophotometer method was used for the determination of steroids and phenols. Among whole legume-based diet combinations, 75:25 ratio lima beans/cowpea diet recorded the lowest alkaloid, flavonoid, cyanogenic glycosides and saponin levels of 5.20 %, 4.0 %, 4.8 % and 3.0 %, respectively. However, among the dehulled legume-based diet, the 50:50 ratio lima beans/cowpea combination had the lowest saponin, steroid, alkaloid, cyanogenic glycoside and flavonoid levels of 1.90 %, 5.38 mg/g, 3.0 %, 5.55 % and 4 %, respectively. Over all, the 50:50 ratio dehulled lima beans/cowpea diet combination, compared to other diet combinations, had the lowest contents of saponin, steroid, alkaloid and flavonoid out of the nine phytochemicals quantified. Pharmacological properties of phytochemicals are beneficial to human health. However, these phytochemicals could also be detrimental to human health when consumed in excess. Therefore, legume-based lima beans/cowpea diet combination ratios should be done with respect to the pharmacological properties of interest.

Abstract-This project is used to develop a conceptual design for an UNMANNED WATER POWERED AERIAL VEHICLE (UWAV) that utilizes a waterjet propulsion system instead of traditional propulsion methods such as propellers or jet engines. The project idea is based on the flyboard system where the drone flies with the force generated by water jet from the nozzles and directing the force in required directions. The purpose of the project is to optimize the efficiency of the waterjet propulsion system to achieve maximum thrust while minimizing energy consumption by improving the design of nozzle. This propulsion systems reduces noise generated in conventional UAV’s. These types of drones are used for Aquatic ecosystem surveillance, agriculture, cleaning of building without human interface.

Risk Management Using VaR, CVaR and Baye’s Model/strong>
Authors:-Arshad Ahmad Khan, Kiran Kumari