Daily Archives: November 30, 2024

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).

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-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.