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7 papers published
Himanshu Tiwari
Customer churn prediction is a critical challenge for businesses aiming to retain customers and improve profitability. This paper presents a real-time customer churn prediction system using Artificial Neural Networks (ANN) integrated into a web-based application. The proposed system analyzes customer demographic and behavioral data to identify patterns associated with churn. Data preprocessing techniques such as normalization and encoding are applied to improve model performance. The ANN model is trained using a telecom customer dataset and optimized using the Adam optimizer with binary cross-entropy loss. The system is implemented using a full-stack architecture, where the frontend is developed using React, and the backend is built using Node.js and Express, with the machine learning model integrated via API. Experimental results show that the proposed ANN model achieves high accuracy and outperforms traditional machine learning algorithms in handling non-linear relationships. The developed system provides a scalable and efficient solution for real-time churn prediction and can assist businesses in making proactive retention strategies.
Rishabh Hirwe, Gourav Kumar Mallick, Pratik Shukla, Arindam Ghosh
This paper presents a lightweight memory safety allocator designed to detect common heap-related errors with minimal overhead. The proposed system augments standard allocation routines with metadata tracking, guard regions, and runtime validation mechanisms to identify memory misuse during pro gram execution. Unlike heavyweight sanitization tools, the design emphasizes low overhead and ease of integration into existing systems.
Patel Dhruvkumar
Digital healthcare platforms, including mobile health applications, telemedicine systems, and electronic medical services, have significantly improved the speed and accessibility of healthcare delivery. However, despite these technological advancements, usability and user experience remain critical concerns. A large number of healthcare applications are still not designed to accommodate users with varying levels of digital literacy, particularly elderly individuals and those unfamiliar with modern technologies. Many systems rely on static interfaces that do not adjust according to user needs, behavior, or emotional state, which often leads to confusion, operational errors, and reduced user retention. Artificial Intelligence offers new possibilities for enhancing the intelligence and usability of healthcare applications. This study introduces an adaptive UX framework that personalizes the interface by analyzing user interactions and emotional patterns. The system continuously monitors user behavior and dynamically modifies interface elements in real time to improve usability and reduce cognitive burden. The proposed approach aims to minimize user effort, decrease interaction errors, and improve overall engagement in digital healthcare environments. A significant contribution of this research is the amalgamation of AI-driven adaptability with emotion-aware interface design, facilitating the creation of more responsive and user-centric healthcare applications.
Arth
The proposed system is developed using modern web technologies and follows a structured approach to ensure reliability, scalability, and ease of use. It improves communication between event organizers and participants, reduces manual workload, and enhances overall system performance. The results indicate that the system provides a user-friendly, secure, and efficient solution for modern event management needs.
Yuvraj kumar Tiwari, Akash Rajbhar, Simraan Siddiqui, Shivangi Roy
The rapid growth of online food delivery platforms has increased convenience, but most existing systems lack nutritional transparency and personalized food customization. This paper presents a Smart Food Nutrition and Customization System designed to integrate food ordering with nutritional awareness. The proposed system provides users with detailed nutritional information, including calories, proteins, fats, and carbohydrates for each food item, helping users make informed dietary decisions. The system also supports real-time ingredient customization, allowing users to add or remove ingredients based on personal preferences and dietary requirements. Any modification dynamically updates nutritional values and pricing, ensuring transparency and improving user control over food choices. The system is developed using modern web technologies and follows a user-friendly approach to enhance usability and accessibility. It also aims to promote healthier eating habits by bridging the gap between convenience and health awareness. Additionally, the proposed system can be extended to include personalized recommendations based on user behavior and health goals. The research highlights how integrating nutrition tracking with food ordering can improve user engagement, support better health management, and contribute to smarter digital food solutions.
Talha Patel, Md Arshad Hussain
E-Waste is a rapidly growing environmental problem, where pollution and inappropriate disposal for praise of resources contributes. Traditional e-waste management systems often lack transparency and efficiency, making it difficult for users to take responsibility for disposing of electronic devices. This study introduces a middleware-based e-waste management system that combines users and certified recyclers to ensure proper collection and processing. The platform developed by Mern Stack Technology allows users to submit e-waste enquiries, submit recyclers to inspect elements, and dynamically adapt pricing before payment. The system integrates real-time tracking, verified recycling participation, and secure transactions, improving transparency and preventing unauthorized disposal. By using digital solutions, the platform improves sustainable waste management, promotes responsible e-waste disposal, and ensures fair pricing. This study highlights the role of technological operation platforms in optimizing e-waste recycling processes, contributing to ecological sustainability.
Gourav Kumar Mallick, Pratik Shukla, Arindam Ghosh
This paper presents a lightweight memory safety allocator designed to detect common heap-related errors with minimal overhead. The proposed system augments standard allocation routines with metadata tracking, guard regions, and runtime validation mechanisms to identify memory misuse during pro gram execution. Unlike heavyweight sanitization tools, the design emphasizes low overhead and ease of integration into existing systems.
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