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Research Paper

Wearable IoT-Based Real-Time Arrhythmia Detection and Cardiac Risk Prediction System Using Machine Learning

Cardiovascular diseases are among the leading causes of death across the world. Early detection of heart abnormalities such as arrhythmia can significantly reduce the risk of severe complications and improve patient survival rates. Arrhythmia refers to irregular heartbeats that may be too fast, too slow, or irregular. Continuous monitoring of heart signals can help identify such abnormalities at an early stage. This project proposes a wearable Internet of Things-based system for real-time arrhythmia detection and cardiac risk prediction using machine learning techniques. The system uses wearable sensors to continuously collect electrocardiogram signals and other physiological parameters from the user. These signals are transmitted through IoT communication technologies to a processing platform where machine learning algorithms analyze the data. The proposed system aims to detect abnormal heart rhythms in real time and alert patients or healthcare providers immediately. By integrating wearable devices, IoT communication, and machine learning analysis, the system supports remote healthcare monitoring and early diagnosis. This technology can improve patient safety, reduce hospital visits, and support preventive healthcare solutions.

Published by: Shanmugapriya. R, Thavaselvi. D, Thiripurasundari. M, kalaivanan. M, Vigneshwaran. R

Author: Shanmugapriya. R

Paper ID: V12I2-1172

Paper Status: published

Published: April 17, 2026

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Research Paper

Understanding Popular Music: What Makes it Catchy?

Music is an emotional stimulus that reflects important human psychology of the listener based on their taste. It can also reveal the emotional state of humanity by referring to the most played or popular songs. It imprints on listeners a memory, which, if it is pleasing to the brain, tends to get replayed. This replay of a song in the brain is almost always a certain part of the song, known as the hook, and is known as an "earworm." Earworms are generally caused by a piece of music played by the listener that sounds catchy or intriguing to the brain. The purpose of this paper is to understand and analyse what features of a certain music piece influence its catchy quality, whether it is based on the lyrics, the melodies, the synth or acoustic quality, the gender of the artist, or a music video and various other factors. It does so by analysing the number of streams that a particular song has received, which shows the number of times people have listened to it due to its catchy nature.

Published by: Anya Dayal

Author: Anya Dayal

Paper ID: V12I2-1163

Paper Status: published

Published: April 16, 2026

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Research Paper

FinTrack – A Finance Tracker

Managing personal finances effectively has become essential in today’s fast-paced digital environment, where individuals often face difficulty in tracking their income and expenses systematically. This paper presents FinTrack – A Finance Tracker, a web-based application designed to simplify and improve personal financial management. The system is an enhanced continuation of the previously developed “Expenzo” project, redesigned with improved usability, efficiency, and performance. FinTrack enables users to record daily transactions, categorize expenses, and monitor income in a structured manner, helping them maintain better financial discipline. It also provides budgeting functionality, allowing users to set spending limits and evaluate their financial behavior against predefined goals. To support better decision-making, the system offers visual insights through charts and summarized reports, making financial patterns easier to understand. The application is developed using Python with the Flask framework for backend operations, ensuring efficient handling of user requests and data processing, while SQLite is used as a lightweight and reliable database for storing user information securely. The frontend is built using HTML, CSS, and Bootstrap to deliver a responsive and user-friendly interface across multiple devices. Additionally, secure authentication mechanisms are implemented to protect user data and maintain privacy. The results indicate that FinTrack helps users gain better control over their finances by promoting awareness and organized tracking. The system is especially beneficial for students and working professionals seeking a simple yet effective solution for managing daily expenses, with future scope including advanced analytics, AI-based predictions, and integration with modern digital payment platforms.

Published by: Sahil C. Madankar, Sankalp S. Pawar, Tanushree S. Patle, Vidhi P. Harode, Shivang R. Nagpure, Sakshi S. Zade, Shrawani H. Bijwar, Prof. P. A. Kuchewar, Prof. M. R. Balbudhe

Author: Sahil C. Madankar

Paper ID: V12I2-1242

Paper Status: published

Published: April 16, 2026

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Case Study

Design and Development of a Smart Drug Delivery System Using IoT

This paper presents the design and development of a smart drug delivery system using Internet of Things (IoT) technology. The system ensures accurate and timely drug administration by integrating sensors, microcontrollers, and wireless communication. It improves patient safety, medication adherence, and remote monitoring by healthcare professionals.

Published by: V. Saranya V, S. Rajasri, M.S Gayathri, M. Sanjay, M. Jayanthi

Author: V. Saranya V

Paper ID: V12I2-1241

Paper Status: published

Published: April 16, 2026

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Research Paper

Smart IoT Based Battery Management System for EV Battery

Advanced battery management is now essential for lifetime and safety due to the quick spread of lithium-ion batteries in electric cars and renewable energy systems. Static hardware thresholds are usually used by traditional battery management systems (BMS) to guard against overvoltage, undervoltage, and overcurrent. Although these static methods work well for simple defects, they frequently fail to anticipate compounding stress elements that cause thermal runaway or cell deterioration. The design and implementation of a sophisticated, Internet of Things-enabled BMS for a 3S (11.1V) Lithium-ion battery pack is shown in this study. It integrates real-time telemetry with an edge-computed Machine Learning (ML) algorithm. The system accomplishes high-fidelity sensing by using a 16-bit ADS1115 Analogue-to-Digital Converter and an ESP8266 microprocessor dynamic "Risk Score" is continually computed by a Logistic Regression model using voltage, current, temperature, State of Charge (SoC), and State of Health (SoH). The system has a multi-tiered protection procedure that includes a physical relay cutoff during critical stages and a nonblocking audio warning during medium-risk conditions. Additionally, the BMS transmits telemetry to the ThingSpeak cloud platform in its capacity as a smart-grid edge device. The dependability and remote observability of energy storage systems are greatly improved by this predictive method, as demonstrated by experimental data.

Published by: Naveen Kumar Vadduri, Sekhar Bodaballa, Ajay Kumar Babu Penigandla, Sunil Kumar Varagani, Rakesh Mudadla

Author: Naveen Kumar Vadduri

Paper ID: V12I2-1239

Paper Status: published

Published: April 13, 2026

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Research Paper

Hack Hub

Hackathons have become an essential part of technical education, encouraging students to apply their knowledge, enhance problem-solving skills, and work collaboratively in teams. However, many students face challenges while participating in hackathons, such as difficulty in finding suitable team members, lack of awareness about peers’ skills, limited interaction across different classes or departments, and hesitation among introverted students to communicate openly. These challenges often lead to poor team formation or discourage students from participating in hackathons altogether. To overcome these issues, this project proposes a Hackathon Collaboration Website designed specifically for college students. The platform functions as a professional networking system similar to LinkedIn but focuses exclusively on hackathon-related collaboration. The website allows users to register, create detailed profiles, list their technical and nontechnical skills, share posts related to hackathon ideas, like and comment on posts, and communicate with other students through a chat feature. This helps students identify peers with similar interests and complementary skills, enabling effective team formation. The system is developed using HTML and CSS for the frontend interface, PHP for server-side processing, and MySQL (phpMyAdmin) for database management. The platform ensures secure data handling, easy accessibility, and a userfriendly interface. By providing a structured and centralized environment for interaction, the proposed system enhances collaboration, increases student participation in hackathons, and promotes inclusive teamwork within the college.

Published by: Singh Himanshu Arvind, Shraddha Jaysing Taware, Afifa Chaudhary, Samruddhi Mahesh Surve, Pratiksha Deshmukh

Author: Singh Himanshu Arvind

Paper ID: V12I2-1233

Paper Status: published

Published: April 13, 2026

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