International Journal of Computer Communication and Informatics

WAPOT: Data Driven Approach for Water Potability Detection using Machine Learning

Saleem Raja Abdul Samad — 2025-12-03

Water potability grading is crucial to public health and safety. It is a critical responsibility of regulatory authorities and water treatment facilities to guarantee that individuals have access to potable and secure drinking water, an inherent human right. The water potability classification is a preventative measure to detect potential impurities or contaminants that may present adverse health effects upon ingestion. This study examines a machine learning approach for classifying the potability of drinking water, utilizing ensemble learning methods (WAPOT) such as Stacking classifiers. Stacking, as a form of ensemble learning, consistently outperforms standalone classifiers and other existing research works, offering improved accuracy of 97% in potability classification. The findings underscore the capacity of machine learning to significantly contribute to the monitoring and managing of water treatment processes.

Quantum-Chaotic Encryption Integrated DCT-Based Steganography

Kalaiselvi M — 2025-12-05

Ensuring safe and convenient access to essential services, including pension retrieval, is crucial in the current digital era. Passwords and PINs are examples of traditional authentication systems that frequently expose people to fraud and identity theft. In order to replace these traditional methods with biometric verification (such as fingerprint and facial recognition), this project suggests a Web Biometric Credentialing System for pension retrieval. The system incorporates Auth0 for secure identity and management of sessions and WebAuthn API for biometric authentication. This method greatly enhances security and user experience by enabling pensioners to verify their identity using biometric information. The technology makes sure that only authorized people can access sensitive financial data and, after successful verification, enables pensioners to safely retrieve their pension amounts. By lowering fraud, eliminating unwanted access, and streamlining the authentication procedure, the suggested solution improves security.

Development of Trash Trio an Automated Waste Segregation System

Infant Vinoth C — 2026-02-17

The Trash Trio an automatic waste dispenser Project is an automated waste segregation system designed to address improper waste disposal in urban and industrial areas. With increasing waste generation, effective source-level segregation is vital to reduce landfill usage, control pollution, and enhance recycling efficiency. The system separates waste into Dry Waste, Wet Waste, and Electronic Waste (E- Waste) using sensors, microcontrollers, and mechanical units that detect properties such as moisture, conductivity, and material composition. Once deposited, waste is automatically sorted into the correct compartment, reducing human error and improving accuracy. To improve usability, the Trash Trio an automatic waste dispenser is integrated with a mobile application that enables real-time bin monitoring, collection alerts, and access to recycling guidelines. The app also includes an e-commerce feature for purchasing the system. By combining automation, smart sensing, and digital connectivity, the Trash Trio an automatic waste dispenser offers a practical and scalable solution for sustainable waste management.

Hybrid Energy Harvesting in Electric Vehicles: Integrating Rotational and Solar Power for Extended Range and Energy Autonomy

Bhavani S — 2025-12-06

Electric vehicles (EVs) offer a cleaner alternative to internal combustion engines but face persistent challenges including limited driving range, dependence on grid-based recharging infrastructure, and battery degradation. This paper proposes a hybrid energy-harvesting system that supplements conventional charging methods by integrating two passive, continuous energy sources: rotational kinetic energy from wheel motion and solar energy via rooftop photovoltaic panels. The system aims to reduce range anxiety, enhance energy efficiency, and improve battery longevity by offloading auxiliary loads and supplying trickle charge during operation and rest. Technical feasibility, energy output modeling, and integration challenges are analyzed. The results suggest that up to 800W of supplementary power can be generated under optimal conditions, translating to significant weekly range extension and auxiliary system support. Limitations, implementation barriers, and future research pathways are also discussed, positioning the system as a scalable solution for enhancing EV autonomy, especially in remote or grid-limited environments.

A Comprehensive Review of Glaucoma and Stargardt Disease Detection using Retinal Images

Senthilkumar A — 2026-02-17

Digital image processing permit ophthalmologists to discover and treat various eye diseases. Precise and early identification is important in biomedical and healthcare communities. Retinal imaging discovers several diseases in eye. Retinal images are essential in diagnosing ocular diseases such as diabetic retinopathy (DR), Glaucoma and Stargardt disease. These diseases lead to blindness if not identified precisely. Glaucoma is chronic, progressive neuropathy which damages optic nerve and neural fiber bundle that transmits visual information from eye to brain. Stargardt disease (STGD) is form of hereditary macular dystrophy in childhood damaging one among in 10,000 individuals. Several researchers performed their research on Glaucoma and STGD identification. But, accuracy and time consumption was not enhanced. To resolve these issues, several glaucoma identification methods are reviewed and drawbacks are detected.