IJSRET Volume 10 Issue 5, Sep-oct-2024

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