Algorithmic Harmonies the Sounds of AI Composition (MUZIKOGEN)
Online Fraud Detection using Random Forest Algorithm
Price Pulse-An Intelligent Multi-Platform System for Real Time Price Tracking and Forecasting
Development of a Web Based Smart Memory Aid for Alzheimer's Patients
Literature Survey on Design and Development of a Smart Traffic Management System using Object Detection
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 introduces MUZIKOGEN, a cutting-edge AI-powered platform aimed at revolutionizing the way music is created and experienced. The system provides users with secure login and personalized experience, allowing access to a wide range of music genres, including pop, rock, Jazz, classical, hip-hop, and electronic. By harnessing advanced AI algorithms, MUZIKOGEN enables users to generate custom beats, lyrics, and vocals tailored to their chosen genre, catering to both novice and experienced musicians. The platform features an AI-driven Helper Bot that offers real- time assistance, enhancing the user experience with technical support, creative guidance, and feedback. Additionally, MUZIKOGEN serves as an educational tool by providing music tips to refine users' skills, fostering creativity and learning. By integrating secure authentication, AI-generated music, and interactive assistance, Algorithmic Harmonies aims to transform music production, making it accessible, enjoyable, and efficient for all.
Online financial transactions have witnessed exponential growth in recent years, leading to a parallel rise in fraudulent activities across e-commerce and digital payment systems. To address this pressing issue, we propose a robust fraud detection framework that integrates machine learning and deep learning techniques, with a primary focus on Random Forest and ensemble-based architectures. Our approach includes comprehensive data preprocessing strategies such as label encoding, normalization, and handling class imbalance through the SMOTE technique. Furthermore, advanced feature extraction is performed using auto encoders and ResNeXt, followed by sequential learning with Gated Recurrent Units (GRUs) for temporal pattern recognition. The proposed model is evaluated using three benchmark datasets IEEE-CIS, PaySim, and the European card transaction dataset. Experimental results demonstrate that our method outperforms conventional models, achieving an accuracy of 96.0%, sensitivity of 99.8%, and specificity of 93.5%. The model not only enhances detection accuracy but also adapts effectively to evolving fraud patterns, making it suitable for real-time financial fraud prevention in diverse domains such as banking, e-commerce, and mobile transactions.
Price Pulse presents an intelligent, real-time price tracking system designed for dynamic e-commerce platforms such as Amazon, Flipkart, and Walmart. Leveraging headless browser automation and proxy rotation, the system bypasses modern anti-scraping defences including CAPTCHAs, IP bans, and JavaScript-heavy interfaces. Unlike traditional trackers, Price Pulse integrates predictive analytics using LSTM-based models to forecast price trends and alert users based on probabilistic price drops. The architecture consists of a React-based frontend, a robust Node.js backend with MongoDB, and a modular scraping engine using Playwright and Cheerio. Notifications are delivered through both email and SMS for timely consumer awareness. Furthermore, the system is built with GDPR-compliant data handling practices and emphasizes ethical scraping standards. Experimental evaluation demonstrates high accuracy in price detection and forecasting, efficient alert generation, and strong system scalability. Price Pulse aims to empower consumers with actionable pricing insights while maintaining responsible data practices.
This paper presents the development of a web based Smart Memory Aid designed to support individuals with Alzheimer's disease in managing their daily routines and cognitive well-being. The system integrates features such as music therapy, cognitive games, medication reminders, and caregiver monitoring, with the goal of enhancing independence and emotional health among users. Built using React, the platform emphasizes accessibility and user-friendliness, catering to the cognitive limitations and mobility challenges associated with Alzheimer's. The design process followed a user centered methodology, incorporating insights from caregivers and healthcare professionals. The final prototype was evaluated through pilot usability testing with a sample of patients and caregivers, with results indicating high levels of engagement and ease of use. This paper discusses the design rationale, implementation framework, and early feedback, offering a promising direction for future development in assistive technologies for neurodegenerative conditions.
Urban traffic congestion has emerged as a critical challenge due to rapid urbanization and increased vehicle density. Smart Traffic Management Systems (STMS), enhanced by artificial intelligence and object detection techniques, have shown promising potential in addressing these issues through real-time monitoring, adaptive signal control, and data- driven decision-making. This literature survey systematically reviews recent approaches in STMS design, focusing on the application of computer vision models such as YOLO (You Only Look Once), IoT infrastructure, cloud computing, and embedded systems. Key contributions of each system are analyzed in terms of traffic flow optimization, environmental impact reduction, cost-effectiveness, and emergency response capabilities. Additionally, the survey identifies common challenges such as sensor reliability, high deployment costs, scalability limitations, and cybersecurity concerns. By synthesizing findings across diverse methodologies, this paper highlights emerging trends and provides a comprehensive foundation for future research aimed at developing robust, scalable, and intelligent traffic management frameworks for smart cities.
