Category Archives: Uncategorized

Authors: Sandeep Kumar Kashyap, Shweta Vikram

Abstract: High-precision GNSS Real-Time Kinematic (RTK) positioning often suffers from gross errors caused by non-line-of- sight (NLOS) multipath and other anomalies, which can dramatically bias simple coordinate averages. This paper presents Entropic-Topological Barycentric Synthesis (ETBS), a novel framework that dynamically selects a reliable subset of GNSS coordinates and computes a weighted barycentric average. The method proceeds in phases: (1) Topological filtering of the raw point set using kernel density estimation to identify and remove outliers; (2) Entropy weighting of remaining points based on multiple quality metrics (e.g. carrier-to-noise ratio, PDOP, satellite elevation variability) to assign higher weight to more reliable observations; and (3) Barycentric coordinate synthesis by computing the Wasserstein (transport) barycenter of the weighted points, yielding the final coordinate estimate. In synthetic tests mimicking open-sky and harsh urban conditions, ETBS consistently isolates outliers and yields centimeter-level accuracy, whereas traditional mean/median or robust least-squares methods produce errors on the order of decimeters or more. The results demonstrate that ETBS effectively neutralizes extreme outliers and achieves superior positioning precision.

DOI: https://doi.org/10.5281/zenodo.19606990

Published by: vikaspatanker

Authors: Ms. Babita, Dr. Brij Mohan Goel

Abstract: Cancer will continue to be a leading cause of mortality worldwide, making early detection and timely intervention essential for improving survival rates. This study will propose a hybrid Artificial Intelligence (AI)-based healthcare framework for early cancer detection and preventive analysis using deep learning techniques. The model will integrate Convolutional Neural Networks (CNN) for medical image feature extraction and Long Short-Term Memory (LSTM) networks for analyzing sequential clinical data.The system will be evaluated on benchmark cancer datasets using performance metrics such as accuracy, precision, recall, and F1-score. The proposed hybrid model is expected to outperform traditional machine learning approaches by achieving higher accuracy and lower error rates.The framework will support early-stage diagnosis, risk prediction, and personalized preventive strategies. Although challenges such as computational complexity and data privacy will persist, the proposed system is anticipated to offer strong potential for real-world healthcare applications and contribute to AI-driven cancer care.

Published by: vikaspatanker

Authors: Navipriyaa M, Pooja Ponrani D, Prathiba Devi V S, Mr. Prasannavenkatesan K

Abstract: Penetration testing plays a vital role in identifying security weaknesses in modern computing systems. With the rapid growth of distributed architectures, cloud-native applications, and microservices, traditional penetration testing approaches have become increasingly complex and time-consuming. Although automated tools are widely used, they typically function in isolation and require significant human expertise to coordinate multi-stage attack scenarios This paper presents an enhanced AI-driven penetration testing framework that leverages the Model Context Protocol (MCP) for structured communication and orchestration among multiple intelligent agents. The proposed system integrates reconnaissance, vulnerability assessment, exploitation, privilege escalation, and reporting into a cohesive pipeline. Unlike traditional systems, the framework incorporates contextual reasoning, adaptive decision-making, and dynamic exploit chaining using an AI Planner. Additionally, the system constructs real-time attack graphs and computes risk scores based on vulnerability severity, exploit confidence, and attack depth. Experimental results demonstrate significant improvements in automation efficiency, reduction in manual effort, and higher success rates in identifying complex exploit chains. The proposed framework represents a shift from static automation toward intelligent, adaptive penetration testing systems.

Published by: vikaspatanker

Authors: Shradha Kumavat, Kapil Shah

Abstract: Image restoration the task of recovering degraded or damaged images has become essential across many technical domains, including space imaging, medical imaging, and several post-processing applications. Most restoration techniques begin by modeling the degradation process that corrupts an image, typically involving blur and noise, and then attempt to reconstruct an approximation of the original image. However, in real-world scenarios, degradation is often unknown, requiring the simultaneous estimation of both the true image and the blurring function directly from the observed degraded image, without relying on prior knowledge of the blur mechanism. This thesis proposes a novel digital image restoration approach based on punctual kriging, supported by multiple machine learning algorithms. The work focuses on restoring images corrupted by Gaussian noise by achieving an effective trade-off between two competing goals: producing smooth, visually pleasing results while preserving edge details and structural integrity.

Published by: vikaspatanker

Authors: Priya Yadav

Abstract: Wireless Sensor Networks (WSNs) have become an essential technology for monitoring and data collection in various applications such as environmental monitoring, smart cities, healthcare systems, industrial automation, and military surveillance. These networks consist of numerous sensor nodes that are deployed in remote locations and operate with limited battery power. Since replacing or recharging batteries is often difficult, efficient power management becomes a crucial factor in ensuring the long-term operation of wireless sensor networks. Power management strategies aim to reduce energy consumption while maintaining network performance and reliability. This review paper presents a classification of major power management strategies used in wireless sensor networks and analyzes their performance based on key parameters such as energy consumption, network lifetime, scalability, and reliability. The study categorizes power management techniques into duty-cycling mechanisms, transmission power control methods, energy harvesting techniques, and energy-efficient routing approaches. Furthermore, recent advancements such as artificial intelligence-based energy optimization and adaptive power management techniques are discussed. The paper provides insights into current research trends and identifies future research directions for improving energy efficiency in wireless sensor networks.

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Published by: vikaspatanker

Authors: Mr Gipson M, Assistant Professor Ms. Bushra B

Abstract: Artificial Intelligence (AI) has emerged as a transformative force reshaping modern businesses across industries. It enables automation, enhances decision-making, and improves customer experience. This study examines how AI is transforming business operations, marketing strategies, and organizational efficiency. A descriptive research design was used, and data was collected through structured questionnaires and supported by secondary data sources. The findings reveal that AI significantly increases productivity, reduces operational costs, and enhances customer satisfaction. Businesses that adopt AI gain a competitive advantage in the market. However, challenges such as high implementation cost, lack of skilled workforce, and ethical concerns still exist. The study concludes that effective AI adoption strategies are essential for sustainable business growth.

DOI: https://zenodo.org/records/19606178

Published by: vikaspatanker

Authors: Kritika Kumari Ojha

 

Abstract: Phishing remains a critical cybersecurity threat, as traditional blacklist-based systems struggle against rapidly evolving domains and zero-day attacks. While machine learning (ML) has emerged as an adaptive solution for detection, the ultimate defense lies in re-engineering the authentication handshake itself. This paper explores the transition from "pass/fail" binary assertions toward a richer, contextual verification ecosystem powered by FIDO (Fast Identity Online) standards. We analyze how WebAuthn and CTAP2 shift the paradigm from possession-based secrets to high-assurance, phishing-resistant identity verification.

DOI:

 

Published by: vikaspatanker

Authors: A Banu, Associate Professor Dr. T. M. Hemalatha

Abstract: Social media marketing has emerged as a powerful tool in influencing consumer behavior and driving the success of e-commerce businesses. With the rapid growth of digital platforms such as Instagram, Facebook, YouTube, and WhatsApp, online retailers increasingly rely on social media to attract, engage, and retain customers. This study aims to analyze the role of social media marketing in shaping e-commerce success by examining consumer awareness, purchasing behavior, engagement levels, and perceived effectiveness of social media campaigns. Primary data were collected through a structured questionnaire from 200 respondents actively involved in online shopping. Statistical tools such as Percentage Analysis, Correlation, Chi-Square Test, and One-Way ANOVA were applied to analyze the data. The findings reveal a significant relationship between social media marketing strategies and e-commerce performance, indicating that social media plays a crucial role in enhancing brand visibility, customer trust, and sales growth.

DOI: https://zenodo.org/records/19605881

Published by: vikaspatanker

Authors: Mohamed Issam S, Associate Professor Dr. T. M. Hemalatha

Abstract: Artificial Intelligence (AI) is a key driver of modern business transformation, especially in corporate environments where data-driven decision-making remains complex. AI-driven tools play a significant role in bridging this gap by providing scalable predictive analytics, automated reporting, and risk assessment to businesses. This study examines the role of Artificial Intelligence in enhancing business decision-making. The research focuses on understanding the accessibility, efficiency, and effectiveness of AI services in improving the operational and strategic outcomes of businesses. Using a descriptive research design, data was collected through a structured questionnaire and supported by secondary sources. The findings indicate that AI has contributed positively to decision-making by enhancing data processing speed, encouraging proactive strategies, supporting revenue-generating activities, and reducing dependence on manual heuristics. The study highlights the importance of strengthening AI integration practices to achieve sustainable business growth.

DOI: https://zenodo.org/records/19605830

Published by: vikaspatanker

Authors: Rohit singh, Vikas pal, Minal suthar, Priti tangadi

Abstract: Agriculture forms the backbone of the Indian economy, yet smallholder farmers continue to face critical challenges including crop failure, rampant plant disease, unpredictable weather, and limited access to expert advisory services. This paper presents Smart-Kheti, a web-based AI-powered smart agriculture platform designed to democratize data-driven decision support for farmers. The proposed system integrates a personalized crop recommendation engine utilizing soil nutrient parameters (N, P, K), pH, temperature, humidity, and rainfall processed through an XGBoost-based multi-class classifier; an automated plant disease detection module employing a Convolutional Neural Network (CNN) trained on the PlantVillage dataset and deployed via TensorFlow Lite for server-side inference and TensorFlow.js for offline client-side inference; and a yield prediction module utilizing XGBoost regression on multi-year historical agricultural data. The platform employs a full- stack architecture with React.js and TypeScript on the frontend and Python FastAPI on the backend, containerized using Docker for scalable deployment. Additional features include a profit calculator, real-time market insights from government data APIs, offline support, and multilingual accessibility. Experimental evaluation demonstrates crop recommendation accuracy of 97.4%, disease detection accuracy of 93.7%, and yield prediction RZ of 0.87.

Published by: vikaspatanker