Daily Archives: May 12, 2025

Fake News Detection Using Natural Language Processing
Authors:-Professor Kirti Randhe, Nameet Vyavahare, Rajkumar Vishwakarma, Sai Kudale

Abstract-:In the digital age, the rapid dispersal of information through social media and online platforms has increased the spread of fake and exaggerated news, posing serious challenges and threats to public trust, societal stability, democratic processes and national security and peace. This research explores the application of Natural Language Processing (NLP) techniques for the automatic detection of fake news, aiming to enhance the reliability of information consumed by the public. By leveraging and applying machine learning and deep learning models in conjunction with NLP methods such as text preprocessing, tokenization, feature extraction, and sentiment analysis, this study investigates effective strategies for distinguishing between factual, genuine and misleading content. Various algorithms, including Support Vector Machines, Random Forest, Naïve Bayes and deep learning approaches like LSTM and BERT, are evaluated using benchmark datasets. The results demonstrate the potential of NLP-driven solutions to accurately classify news articles, highlighting their significance in combating misinformation. This paper contributes to the growing field of automated fake news detection and offers insights into building more trustworthy digital information ecosystems.

DOI: 10.61137/ijsret.vol.11.issue2.456

Published by: Kajal Tripathi

J.A.R.V.I.S: AI ASSISTANT
Authors:-Bhuneshwar Singh Chauhan, Swati Kumari, D Sai Divya Reddy, Dhananjay Sahu, Professor Neha Soni

Abstract-:This paper examines the rapidly evolving field of modern technology, with a particular focus on virtual assistants developed through Python. It shows how it changes these assistants are having on human-computer interactions by using advanced technologies such as Natural Language Processing (NLP) and Artificial Intelligence (AI). The literature review consolidates key research findings on the functions, capabilities, and design strategies of virtual assistants. In the system architecture section, a clear structure is presented for desktop virtual assistants, detailing key components like the user interface, speech recognition modules, dialogue management, and more. The methodology section outlines a structured approach to designing and building such systems. The conclusion emphasizes the significant advancements in virtual assistant technology while also addressing ongoing challenges such as ensuring system stability and safeguarding data security. Ultimately, the paper underscores the importance of continued innovation in this field to fully unlock the potential of virtual assistants in various industries.

DOI: 10.61137/ijsret.vol.11.issue2.455

Published by: Kajal Tripathi

CFD Analysis on Car Rear Spoiler
Authors:-Turlapati Siva Krishna, M. Hari Sai, K. Naresh

Abstract-:The stability of a car refers to its ability to maintain its trajectory and resist external disturbances, such as wind, road irregularities, or sudden maneuvers. Several key factors affect a car’s stability, including its center of gravity, weight distribution, suspension system, tire characteristics, and aerodynamics. A spoiler is an aerodynamic device attached to a vehicle, typically on the rear deck lid or trunk, designed to improve its stability and reduce drag at high speeds. By altering airflow around the vehicle, a spoiler can enhance its overall performance and handling. There are various types of spoilers, including rear spoilers, front spoilers, and side skirts, each serving a specific purpose. The benefits of spoilers include improved stability, reduced drag, and enhanced performance, making them a popular choice for racing cars, high-performance vehicles, and aftermarket accessories.

DOI: 10.61137/ijsret.vol.11.issue2.454

Published by: Kajal Tripathi

Application of Carbon Nanomaterials in Energy Production and Storage
Authors:-Aryan Kuhar

Abstract-:In this modern world the demand for more sustainable energy production and storage solutions has elevated the interest in nanotechnology, in which carbon-based nanomaterials are particularly interesting in improving energy production systems. This review paper explores the application of carbon nanomaterials, including nanomaterials like carbon nanotubes (CNTs), graphene, fullerene etc., in many energy production methods. Their unique properties such as large surface area, high electrical conductivity and high mechanical strength, make these carbon nanomaterials optimal candidates for improving energy storage and generation processes. In energy devices such as lithium-ion batteries, solar cells and fuel cells, these carbon nanomaterials have demonstrated improvement in better charge transport, energy density, catalytic performance and charge/discharge efficiency. These nanomaterials are also developed so there are more cost-effective alternatives to current technology.

DOI: 10.61137/ijsret.vol.11.issue2.453

Published by: Kajal Tripathi

A Supervised Learning Framework for Predicting GSC Antibody Seropositivity in Guillain–Barré Syndrome Using Multivariate Clinical and Demographic Indicators
Authors:-Ms. Sangeetha Raj S, Ayushi Negi, Ekta Kumari, Christina S

Abstract-:This paper proposes a robust supervised learning framework for predicting ganglioside complex (GSC) antibody seropositivity in patients with Guillain–Barré Syndrome (GBS) using multivariate clinical and demographic features. Drawing from a comprehensive dataset encompassing 129 GBS patients, we employed advanced machine learning methods support vector machines, random forests, decision trees, and k-nearest neighbours to predict seropositivity for six key anti-ganglioside antibodies (GM1, GM2, GD1a, GD1b, GT1b, GQ1b). Rigorous feature selection, cross-validation, and class imbalance handling were implemented to ensure robustness. Results show that routine clinical data can deliver accurate antibody seropositivity predictions, supporting GBS management where serological assays are delayed or unavailable.

DOI: 10.61137/ijsret.vol.11.issue2.452

Published by: Kajal Tripathi

Smart Vehicle Accident Detection and Anti-Theft System
Authors:-Shaikh Mubashshir, Mohammad Zafir, Hrutuja Ingole, Professor A. P. Jaware

Abstract-:The rapid advancement of technology has transformed transportation but has also escalated challenges like road accidents and vehicle theft. This paper presents a smart system integrating accident detection and anti-theft mechanisms using piezoelectric sensors, GPS, GSM, and fingerprint verification technologies. The accident detection module employs a piezoelectric sensor to identify crashes or rollovers, instantly transmitting the vehicle’s location to emergency contacts via GSM and GPS modules. The anti-theft system uses fingerprint authentication to ensure only authorized users can operate the vehicle, with real-time tracking and remote immobilization capabilities. This dual-purpose system enhances road safety by reducing emergency response times and strengthens vehicle security against theft. Designed for scalability, the system leverages Arduino as the central microcontroller, offering a cost effective and reliable solution for modern vehicles. This paper details the system’s design, implementation, and potential impact on automotive safety and security.

DOI: 10.61137/ijsret.vol.11.issue2.451

Published by: Kajal Tripathi