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Research Paper

Detecting Misinformation in News Using BERT and Natural Language Processing

The widespread use of social media and online news platforms has made it easier for misinformation and fake news to spread rapidly. This creates serious challenges for individuals and organizations that rely on accurate information. To address this problem, this study proposes a fake news detection system that combines Natural Language Processing (NLP) techniques with both traditional machine learning and transformer-based models. The dataset used for the study is derived from the WELFake dataset, containing labeled news articles categorized as real or fake. Text preprocessing techniques such as tokenization, removal of noise, and normalization are applied to prepare the data. Traditional models like Support Vector Machine (SVM) and LightGBM use TF-IDF features to capture important word patterns, while DistilBERT is used to understand contextual meaning in text. The results show that transformer-based models achieve higher accuracy, while traditional models remain efficient and reliable. This hybrid approach improves the overall effectiveness of fake news detection systems.

Published by: Ankannagari Harshith Reddy, Tabitha Indupalli, Dinesh Ragipani, T.Dheeraj, Ch.Bhanu uday

Author: Ankannagari Harshith Reddy

Paper ID: V12I2-1258

Paper Status: published

Published: April 24, 2026

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Research Paper

FindMyStuff – An Automated Lost and Found Management System

FindMyStuff is an automated, centralized web-based platform developed to address the inefficiencies of traditional lost-and-found systems within university campuses. Conventional methods, such as physical notice boards and scattered social media groups, lack structured organization, searchability, and data persistence, resulting in low recovery rates of misplaced items. The proposed system introduces an intelligent and systematic approach to asset recovery through the implementation of a heuristic matching algorithm. This algorithm automatically links “Lost” and “Found” reports by analyzing key parameters such as item category, textual similarity, location tags, and temporal proximity. The platform is designed using modern web technologies, including ReactJS and Tailwind CSS for the frontend, and Django with Django REST Framework for the backend, ensuring scalability, responsiveness, and cross-platform compatibility.

Published by: Vaishnavi Duratkar, Snehal Ninawe, Twinkal Sapate, Sharwari Mohadikar, Ashwary Dhakate, Sanket Barapatre

Author: Vaishnavi Duratkar

Paper ID: V12I2-1255

Paper Status: published

Published: April 22, 2026

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Research Paper

Narcissistic Personality Disorder: An Overview

The following paper aims to provide an overview of ‘Narcissistic Personality Disorder’ (NPD). It endeavors to do the same by covering the disorders various aspects, starting by exploring the terms mythological origins, the evolution of the definition and usage of the term, as well as psychoanalytic contributions. The paper then moves on to the various subtypes of the disorder as categorized by various theorists, followed by the diagnostic criteria and differential diagnosis as written in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5-TR). The contributions and theories of Otto Kernberg and Heinz Kohut have also been touched upon, along with the treatment methods and therapeutic approaches used in clinical settings to treat NPD. Finally, the paper concludes with the topics of the societal impact of NPD and also briefly talks about the hypothesis of a ‘narcissism epidemic’.

Published by: Aryan Batra

Author: Aryan Batra

Paper ID: V12I2-1248

Paper Status: published

Published: April 17, 2026

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Research Paper

The Promise of Xenotransplantation

Xenotransplantation is emerging as an innovative solution to the global organ shortage crisis, where the demand for transplants far exceeds the supply. This paper explores the historical need for an alternative source for allotransplantation and examines the key organs in play for such a transplant. It highlights the immunobiological barriers that challenge such a process, along with the risks of zoonotic disease transmission and the safety protocols to address them. Overall, the research findings prove that xenotransplantation presents a promising yet complex approach that could redefine modern medicine.

Published by: Sneha Rajasekar

Author: Sneha Rajasekar

Paper ID: V12I2-1235

Paper Status: published

Published: April 17, 2026

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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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