Journal of Advanced Research in Cloud Computing, Virtualization and Web Applications

Detecting Credit Card Fraud with Machine Learning Algorithms

Rohit kumar — 2023-12-29

In the modern digital age, credit card fraud has become a prevalent and costly problem, affecting both financial institutions and consumers. As more financial transactions move online, the opportunities for fraudulent activities increase, and fraudsters continually evolve their techniques to exploit vulnerabilities in the payment system. In response to this growing threat, the financial industry has turned to advanced technologies, including machine learning algorithms, to detect and prevent credit card fraud. This article explores how machine learning is being employed to identify fraudulent transactions, providing an effective and efficient way to safeguard financial systems and protect consumers. Credit card fraud encompasses various activities, including unauthorized transactions, identity theft, and fraudulent credit applications. Criminals employ different tactics, such as Card Not Present (CNP) fraud, Card Present (CP) fraud, and account takeover. Machine learning has revolutionized the field of credit card fraud detection by offering dynamic, adaptable, and efficient solutions. These algorithms can identify suspicious patterns and anomalies within a vast amount of transaction data, enabling timely intervention to prevent fraud. Key machine learning techniques, including anomaly detection, supervised learning, and unsupervised learning, play essential roles in credit card fraud detection. Anomaly detection focuses on identifying irregularities in transaction data, while supervised learning leverages labeled data to learn patterns of fraud. Unsupervised learning, on the other hand, operates without labeled data, making it particularly effective at detecting emerging fraud patterns. Deep learning, specifically neural networks, has emerged as a revolutionary technology in the field, capable of automatically learning complex patterns and adapting to evolving fraud tactics.

Ether Vote: Revolutionizing Elections with Blockchain Powered Electronic Voting System

Shaurya Gautam — 2023-12-29

Voting is an essential component of democracy, as it enables citizens to express their will, hold their elected officials accountable, promote diversity in government, foster civic engagement, and protect against tyranny. Considering the technological advancement that people have acquired in recent times voting system is almost outdated. Electronic Voting, or E-Voting, is a modern method of casting and counting votes in an election. It promises greater efficiency, speed, and accuracy compared to traditional paper-based voting systems. However, E-Voting systems face several challenges, such as security concerns and lack of transparency. To address these challenges, the paper proposes Blockchain Technology as a potential solution. Blockchain is a distributed ledger technology that is immutable, transparent, and secure. By using blockchain for E-Voting, it is possible to create a tamper-proof and transparent system that can ensure the accuracy and integrity of the voting process. Ethereum Blockchain, a decentralized open-source platform for building Decentralized Applications (dApps) using smart contracts is being used. Once a vote is recorded on the Ethereum Blockchain, it cannot be changed or deleted, ensuring the integrity of the voting process. Being decentralized there is no central authority controlling the voting process, which increases transparency and reduces the possibility of fraud. Smart contracts can help automation of the process ensuring basic rules and regulations being followed. The proposed system can be foul proof in most cases and there are proposed norms that can be followed to manage some exceptional cases.

Lung Cancer Detection: A Machine Learning Approach

Durga Bagavathi Sankar — 2023-12-29

Lung cancer is a formidable adversary, ranking among the most prevalent and deadly cancers worldwide, claiming countless lives each year. This insidious disease often lurks in the shadows, remaining asymptomatic during its early stages, making early detection an elusive goal. Traditional diagnostic methods, such as biopsies and radiological imaging, have served as indispensable tools, but they come with their share of limitations. Fortunately, the emergence of machine learning and artificial intelligence (AI) has ushered in a new era of possibilities, promising more accurate and timely lung cancer detection than ever before. Machine learning, a subset of AI, allows computers to learn from data and make predictions or decisions based on this acquired knowledge. In the context of healthcare, machine learning has emerged as a powerful instrument for diagnosis, prognosis, and the optimization of treatment plans. It presents a transformative opportunity to redefine how we perceive, diagnose, and manage diseases such as lung cancer. In this article, we delve into the challenges posed by lung cancer detection, the promises and potential offered by machine learning, and the myriad ways in which this innovative approach can revolutionize the battle against this deadly disease. We explore the benefits of machine learning in improving accuracy, reducing healthcare costs, and enabling personalized treatment plans. Moreover, we discuss the challenges and ethical considerations, such as data privacy, regulatory compliance, and addressing bias, which must be addressed in the implementation of machine learning in healthcare. As we journey further into the era of AI-driven healthcare, the promise of machine learning in lung cancer detection shines brightly, offering hope for a future with improved patient outcomes and ultimately a world where lung cancer is no longer a leading cause of suffering and loss.

Navigating the Skies: The Rise of Drone Tracking Technologies

Vipin Gaur — 2023-12-29

Drones have rapidly evolved from military tools and hobbyist gadgets to ubiquitous and transformative technology across diverse industries. This article explores the world of "Drone Track," an encompassing term for the technologies and methods developed to monitor, manage, and track the expanding drone ecosystem. As drones find applications in agriculture, logistics, surveillance, and entertainment, the need for robust drone tracking systems has become paramount. This article delves into the significance, technologies, challenges, and promising future of drone tracking. The proliferation of drones in recent years has been remarkable, with applications spanning precision agriculture, environmental monitoring, logistics, and even autonomous deliveries. However, this expansion has brought about challenges related to safety, privacy, and regulatory compliance. A comprehensive drone tracking system has evolved from a convenience to a necessity, addressing issues like airspace congestion, safety hazards, and privacy concerns. The article provides an overview of various drone tracking technologies, including GPS, radio frequency, visual recognition, acoustic tracking, radar, geofencing, and blockchain-based decentralized tracking. Each technology contributes to safer and more controlled drone integration into our airspace. In a world where drones are no longer a novelty but a transformative technology, drone tracking is a crucial element in maintaining safety, security, and privacy in the skies. This article embarks on a comprehensive journey into the realm of drone tracking, uncovering the array of technologies and methods employed to ensure the responsible use and monitoring of UAVs.

Utilizing HRM in Web Services and the Role of AI: Triple Bottom Line Sustainability

Immanuel Johnson — 2023-12-29

In the contemporary business landscape, organizations are increasingly recognizing the pivotal role of their employees as their most valuable assets. This acknowledgment has propelled Human Resource Management (HRM) into a strategic position where it ensures that a company's workforce is effectively managed, motivated, and engaged. HRM has evolved significantly over time, transitioning from its historical administrative functions to a more holistic and strategic approach in managing human capital. With the integration of web services and artificial intelligence (AI) into HRM practices, there has been a profound transformation in the way organizations manage their human resources, enhancing efficiency and effectiveness while contributing to a comprehensive approach to sustainability known as the Triple Bottom Line (TBL). This article delves into the utilization of HRM in web services and the transformative role of AI in achieving Triple Bottom Line sustainability. It also highlights how AI fosters social sustainability by reducing bias and discrimination, promoting diverse and inclusive recruitment, and supporting employee well-being. Furthermore, it discusses the impact of AI on environmental sustainability, particularly in optimizing remote work and minimizing ecological footprints. In conclusion, this article underscores the symbiotic relationship between HRM, web services, and AI and their pivotal role in achieving the Triple Bottom Line. It demonstrates how these modern tools and technologies enhance economic, social, and environmental sustainability, making HRM an essential component of responsible and sustainable business practices in the digital age.