Category Archives: Uncategorized

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

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-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-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).