Authors: Daksh Jasrotia, Associate Professor Dr.Simarjit Kaur

Abstract: Public Wi-Fi networks in places like airports or cafés is convenient, but they are not very safe. Most people do not realize that these networks don’t usually have strong security and because of that, attackers can use methods like Man-in-the-Middle (MITM) attacks. One common method of MITM attacks is called ARP spoofing. In this method, a hacker tricks your device into sending data through their system instead of the real network. This usually happens in the background and the users don’t notice anything suspicous. As a result, important and personal information can be exposed and stolen without the user knowing. In this paper, I am proposind WiFiShield, a lightweight system or rather an application designed to detect such vulnerabilities in real time. Instead of being a heavy security suite, the application or system runs quietly in the background, sniffing network packets and double-checking the ARP table for any sudden, suspicious changes or spikes in the ARP requests. If the system sees a gateway address "flapping" or changing unexpectedly, it assigns a risk score and alerts the user immediately. I am aiming to make WiFiShield highly effective at spotting these hijacks without slowing down the computer. The application should be a simple, low-power solution for anyone who needs to stay connected on the go without leaving their personal data wide open to hackers.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Akash Suri, Aryan Pathania, Divyayush Verma, Rajat Takkar

Abstract: The proliferation of fake news on social media plat- forms poses significant threats to public discourse and democratic processes. While numerous machine learning approaches have been proposed for fake news detection, limited attention has been given to understanding why different models classify news as fake and whether these explanations are consistent across algorithms. This paper presents a comparative and explainable machine learning framework that addresses two critical research questions: (1) Do different ML models agree on which textual fea- tures indicate fake news? (Trust Gap Analysis), and (2) Do fake news patterns learned from one domain generalize to another? (Cross-Dataset Robustness). We evaluate four classical machine learning algorithms—Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest—using TF-IDF features on two distinct datasets: ISOT (political news, 44,898 articles) and WELFake (general news, 72,134 articles). Using SHAP (SHapley Additive exPlanations) for model interpretability, we compute Jaccard similarity and Spearman rank correlation to quantify agreement between model explanations. Our results reveal that different models exhibit varying levels of agreement on fake news indicators, with implications for model selection in real- world deployment. Furthermore, cross-dataset analysis identifies “universal” fake news features that generalize across domains versus “topic-specific” features that are domain-dependent. This work contributes a novel analytical framework for evaluating the trustworthiness and generalizability of fake news detection systems.

DOI: https://doi.org/10.5281/zenodo.19916551

Published by: vikaspatanker

Authors: Professor Rajendra Pawar, Omkar Walunj, Pranav Hole, Sarthak Thigale, Sohan Sandbhor

Abstract: Early detection of brain tumors is crucial for effective treatment and improved patient outcomes. This study presents an automated system for brain tumor classification using deep learning techniques. A convolutional neural network based on the VGG16 architecture is utilized to analyze MRI images and classify them into different categories such as glioma, meningioma, pituitary tumor, and normal cases. The system includes image preprocessing, model prediction, and a web-based interface developed using Flask for easy user interaction. Users can upload MRI images and receive instant predictions along with confidence scores. Additionally, a PDF report is generated to present the results in a structured format. The proposed approach demonstrates reliable performance and can assist medical professionals in making faster and more accurate preliminary diagnoses.

Published by: vikaspatanker

Authors: Kavya Sharma

Abstract: The residential property market in Noida is complex due to its structured sector-based planning and the coexistence of Authority-developed plots and private high-rise housing societies. These two categories follow different pricing patterns, even within nearby areas. This study aims to develop a transparent price prediction model using Multiple Linear Regression to analyze the impact of hyperlocal features, particularly Metro connectivity, on property prices. A historical dataset of Noida properties was utilized and processed using Python and Pandas. The finalized regression model achieved approximately 85% accuracy on the testing dataset, revealing that Sector Location and Metro Connectivity are the most influential factors, often outweighing flat size. This demonstrates that a transparent regression approach can effectively support fair pricing in high-variance markets.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: R. Oviyashree, M. Loganathan, S.P. Manoj, A.S, Vasunthra, Dr.R.Punithavathi

Abstract: Every year, forensic investigators and humanitarian groups are overwhelmed by thousands of missing people and unidentified bodies. However, traditional methods of identifica-tion are still very slow and prone to mistakes. AMPUIS, the AI-based missing person and Unidentified Body Identification System, solve this problem with a smart, real-time forensic framework. The system uses a ResNet-10-based Single Shot Multi-Box Detector to find faces, Open-face to extract deep embeddings that don’t change with pose, and a Support Vector Machine classifier to give probability scored identity verification. AMPUIS is built on a secure Flask web architecture that lets law enforcement and NGOs access it based on their roles. It has automated case management, severity-based alerts, and a live forensic dashboard. Experimental results show that this method is more accurate and faster than traditional biometric methods.It is also scalable and can be used all over the world for modern forensic identification.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Krishna Pratap Singh Gaur, Om Prakash Sondhiya

Abstract: Innovative solutions that reduce human exposure to life-threatening risks are required because to the increasing frequency and severity of fire accidents in industrial settings, including petrochemical refineries, warehouses, power generation facilities, and chemical manufacturing plants. The methodical design, development, and experimental validation of an autonomous firefighting robot created especially for industrial deployment are presented in this work. The suggested platform combines an embedded multi-sensor array consisting of a FLIR Lepton 3.5 infrared thermal imager, Hamamatsu UV flame sensors, a Velodyne VLP-16 three-dimensional LiDAR, and electrochemical gas detectors with a thermally insulated omnidirectional Mecanum-wheel chassis. On an NVIDIA Jetson Orin NX computation module, FireDetNet-v2, a lightweight convolutional neural network trained on 45,000 annotated industrial fire pictures, achieves a mean average precision (mAP@0.5) of 97.6% at 30 frames per second. A 150-liter onboard water-AFFF suppression module uses a two-degree-of-freedom pan-tilt nozzle gimbal to administer agent at up to 12 bar, with a maximum throw range of 15 meters. GPS-denied autonomous navigation is made possible via simultaneous localization and mapping (SLAM) using the Cartographer framework using VLP-16 LiDAR data.

DOI: https://doi.org/10.5281/zenodo.20053261

Published by: vikaspatanker

Authors: Heli Dholariya

Abstract: Nutrition plays a vital role in maintaining overall health and well-being. A balanced diet provides essential nutrients required for the proper functioning of the body, including growth, repair, and energy production. In recent years, unhealthy eating habits and lifestyle changes have led to an increase in nutritional deficiencies and chronic diseases such as obesity, diabetes, and cardiovascular disorders. This research paper presents a comprehensive study on the importance of nutrition and a balanced diet, including its components, benefits, and impact on human health. It also highlights the consequences of poor nutrition and suggests strategies to maintain a healthy diet. The findings emphasize that proper nutrition is essential for improving quality of life and preventing diseases.

Published by: vikaspatanker

Authors: Pranavvikraman. A, Dr. M. Sakthivanitha

Abstract: Traffic congestion is a critical challenge in rapidly urbanising cities, and conventional fixed-time traffic signals fail to adapt to dynamic real-time variations, leading to longer waiting times, fuel wastage, emissions, and delays in emergency response. To address this, the project designs and implements an AI-Driven Adaptive Traffic Signal Control System at a six-road intersection near Adyar Bridge, Chennai, Tamil Nadu, India. The system integrates a Python backend powered by OpenAI's GPT-5.4-nano model with a real-time HTML/CSS/JavaScript frontend, connected through Flask and Socket.IO. The AI receives time slot inputs, determines traffic density ranges from a lookup table based on real-world observations, and predicts realistic vehicle counts for nine lane paths: R1-R4, R1-R5, R1-R2, R6-R2, R6-R4, R6-R5, R3-R5, R3-R2, and R3-R4. Using these counts, it calculates signal timings for five units — S1, S2, S3, and pedestrian signals P1 and P2 — across five traffic cases (C-1 to C-5). Signals operate independently through Green → Yellow → Red phases, with transitions occurring only when all signals reach red. A midnight mode between 12:01 AM and 4:59 AM switches all signals to blinking red. The dashboard features a dark theme with LCD-style countdown timers and a manual override for emergencies. Economically viable at USD 0.20 per million tokens, the GPT-5.4-nano model demonstrates practical use of AI in structured decision-making for critical infrastructure. Results show reduced delays, improved throughput, and safer pedestrian crossings.

Published by: vikaspatanker

Authors: Ummi Yusuf Adam, Habibu Yusuf Adamu

Abstract: The COVID-19 pandemic led to disruptions in global supply chains, exposing vulnerabilities in organizations that were not adequately prepared for digital operations. This study investigates how digital transformation in supply chain management has influenced the business performance of manufacturing companies in the Democratic Republic of Congo amid the pandemic. Utilizing organizational information processing theory and the dynamic capabilities perspective, a conceptual framework was created to connect the digital environment, digital capabilities, digital supply chain transformation, and business performance. Data were collected through a structured survey of 233 senior logistics managers and the model was tested using partial least squares structural equation modeling (PLS-SEM). Measurement validation confirmed reliability and discriminant validity of the constructs. The results reveal that both digital environment (β = 0.271, p = 0.005) and digital capabilities (β = 0.304, p = 0.003) significantly drive digital supply chain transformation, which in turn exerts a strong positive effect on business performance (β = 0.597, p < 0.001). Mediation analysis further shows that digital supply chain transformation significantly mediates the effects of digital environment on business performance. These findings emphasize the importance of developing robust internal digital capabilities alongside an enabling external digital environment to enhance supply-chain agility in turbulent contexts.

DOI: https://doi.org/10.5281/zenodo.19910152

Published by: vikaspatanker

Authors: Aniket Garg

Abstract: The rapid growth of the Indian digital payments ecosystem which is controlled mainly by the Unified Payments Interface (UPI) has improved financial inclusion whilst alleviating transaction friction. Meanwhile, the magnitude, speed, and functionality of UPI have increased vulnerability to phishing, impersonation, scams, and synthetic identities, mule accounts, and AI-enforced social engineering. The paper under consideration investigates the UPI fraud proliferation in India through the qualitative analysis of official circulars, payment data, cybersecurity reports, and the latest regulatory interventions. It has been shown in the analysis that user confusion, ineffective verification conduct, quick payment rails that cannot be reversed, and more advanced threat agents are the proximal factors influencing the rise in fraud. A multi-level framework of prevention that incorporates beneficiary authentication, concatenation of devices, behavioural danger rating, mule-account recognition, consumer knowledge, and amplified inter-institutional reports is proposed. The paper concludes that future achievements in minimizing fraud through the integration of scale-induced innovation with security-by-design and timely redress framework will be dependent on it. [1], [3], [4], [5].

DOI: https://doi.org/10.5281/zenodo.19910031

Published by: vikaspatanker