Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
Algorithmic Harmonies: The Sounds of AI Composition (MUZIKOGEN) as a platform that makes creating music easier with the help of artificial intelligence (AI). Users can log in safely, choose from different music styles like pop, rock, and jazz, and the AI helps them create beats, lyrics, and vocals. The system is designed for both beginners and experienced musicians. It also has a helper bot to answer questions and give advice in real time. The main goal of the platform is to make music creation simple, fun, and available to everyone.
Smart Traffic Management Systems (STMS) are transforming urban transportation by leveraging real-time data analytics and adaptive signal control to optimize traffic flow, reduce congestion, and improve safety at intersections. Utilizing data from sensors, cameras, and smartphones, these systems enable dynamic adjustments to traffic signals, leading to minimized travel times and enhanced road efficiency. STMS also promote environmental sustainability by reducing fuel consumption and lowering emissions. However, despite their promising benefits, challenges such as privacy concerns and cybersecurity risks remain critical areas for development. This paper provides an overview of STMS, emphasizing their role in advancing urban mobility and highlighting the need for ongoing research and integration into urban planning. By addressing these challenges and enhancing their functionalities, STMS have the potential to create smarter, safer, and greener transportation networks, contributing significantly to more efficient and sustainable cities.
Enhance the quality of life for individuals living with Alzheimer's disease. This innovative tool incorporates features such as music therapy, brain games, and daily routine management, tailored to the unique needs of each user. By utilizing a user-friendly interface, the prototype facilitates personalized content delivery, enabling patients to engage in stimulating activities that promote cognitive function and memory retention. The application offers reminders for medications and appointments, helping The Smart Memory Aid for Alzheimer's Patients is a web-based prototype designed to support cognitive health and patients adhere to their daily routines. It also includes a social engagement feature, allowing users to connect with family members and friends through shared activities and communications, thereby reducing feelings of isolation. Additionally, caregivers can access monitoring tools to track patient progress and adjust activities accordingly, enabling them to provide more effective support. The integration of technology into therapeutic practices presents a promising approach to aid Alzheimer's patients in maintaining their independence, enhancing cognitive engagement, and improving their overall well-being. Finally, the system is designed with accessibility in mind, ensuring that it accommodates the varying levels of cognitive ability and physical mobility of users.
The PricePulse project presents an automated, real-time price monitoring tool specifically designed for tracking prices on Amazon. This research paper outlines the development, architecture, and implementation of PricePulse. By employing web scraping techniques—using Next.js for the frontend, Cheerio for HTML parsing, and MongoDB for data storage—along with Bright Data’s proxy service to bypass Amazon’s anti-scraping measures, PricePulse reliably extracts price information and alerts users to significant price changes. The proposed solution addresses the challenges posed by frequent price fluctuations and the inherent difficulties of manual monitoring on e-commerce platforms. Results demonstrate successful real-time tracking, overcoming obstacles such as CAPTCHAs and IP restrictions. Future enhancements include expanding support to additional platforms and incorporating predictive analytics to further empower consumers in their purchasing decisions.
The online payment method leads to fraud that can happen using any payment app. That is why Online Payment Fraud Detection is very important. Online Payment Fraud Detection using Machine Learning in Python. Here we will try to solve this issue with the help of machine learning in Python. The process begins with AI data input and preprocessing, utilizing the SMOTE technique to mitigate data imbalance, ensuring effective training and more reliable predictions. Our AI model undergoes thorough evaluation on three real-world financial transaction datasets, outperforming existing algorithms by 10% to 18% across various performance metrics. Additionally, it maintains excellent computational efficiency. Online payment fraud detection refers to the methods, technologies, and strategies used to identify and prevent fraudulent activities in electronic transactions. As more and more transactions take place over the internet, particularly in e-commerce, online banking, and other digital services, fraud detection has become a critical part of securing online payments and safeguarding both consumers and businesses. In this innovative world, around 1 billion online exchanges occurred each day which benefits us with its administrations as well as prompts fake exercises. Wholesale fraud, monetary misrepresentation, taken cards, deliberate non-installment, complementary plan abuse lead to enormous income misfortune to the organization. The proficiency of E- Commerce is debasing step by step because of online extortion. A statistical approach ought to be taken to conquer these fake exercises and in this paper, we will talk about online extortion recognition system utilizing Machine Learning algorithms.
