Authors: Sharyu Chinchole

Abstract: As climate change continues to reshape coastlines and alter ecosystems, architecture stands at a crossroads—between crisis and creativity. This research explores the architectural response to rising sea levels through the lens of adaptive overwater villas. Traditionally associated with luxury, overwater villas are reimagined here as resilient, climate-responsive habitats that float, adapt, investigates and endure. The study floating systems, modular strategies, sustainable design tools, and precedents from across the globe, aiming to develop a contextual, feasible solution for future-ready living. The final proposition blends engineering and environmentalism into a design that is not only functional but poetic—an architecture that floats with the planet, not against it.

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Published by: vikaspatanker

Authors: Shreyash Akole, Mansi Rakhonde, Saish Desai

Abstract: The rapid growth of online news and digital media has significantly impacted financial markets, where even a single headline can influence investor behavior. With this increasing dependence on news, ensuring the authenticity and sentiment of financial information has become more important than ever. This research presents a dual-purpose system that combines stock market news sentiment analysis with fake news detection. Our model aims to solve this by using Natural Language Processing (NLP) techniques and Machine Learning (ML) algorithms to analyze financial news, detect fake information, and suggest investment actions such as Buy, Sell, or Hold. This system uses Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) models for sentiment and authenticity analysis, ensuring reliability and accuracy. The framework empowers traders, investors, and institutions to make smarter, safer financial decisions by combining sentiment analysis and fake news detection into a single platform. Most systems only do either sentiment analysis or fake news detection. Our system does both in one place, making it more reliable. It reached 93% accuracy in sentiment analysis and 96% in fake news detection, helping users make better and safer financial decisions.

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Published by: vikaspatanker

Authors: P Prakash, Aakash K, Amizhdhan L, Pragathesh Kumar, L. Kannagi

Abstract: This autonomous agricultural vehicle, equipped with a camera and GPS, is designed to optimize farming efficiency by automating critical tasks such as harvesting, weed removal, and pest control. The vehicle autonomously navigates through fields, reducing the reliance on manual labor while ensuring precise and timely execution of agricultural operations. For harvesting, the vehicle identifies and collects ripe crops efficiently, minimizing losses and enhancing overall productivity. In weed removal, it detects and eliminates unwanted plants, ensuring crops have access to nutrients without competition. For pest control, the vehicle monitors plant health and identifies areas affected by pests, applying treatments only where necessary. This targeted approach reduces pesticide use, contributing to eco-friendly and sustainable farming practices. With smart navigation and obstacle avoidance, the vehicle operates seamlessly in large and complex agricultural environments. By integrating automation into farming practices, this vehicle not only enhances productivity but also reduces costs, making it an essential component of modern precision agriculture.

DOI: http://doi.org/10.5281/zenodo.16607776

Published by: vikaspatanker

Authors: Ms. Usha Dhankar, Ms. Srishty Goswami, Himanshu Sharma, Nikhil Tiwari, Vansh Gupta

Abstract: – As networks become more complex and expansive, traditional security monitoring methods often fall short in detecting and responding to fast-evolving threats. This is where visualization steps in—turning overwhelming amounts of raw data into clear, intuitive visuals that help security teams spot anomalies, recognize attack patterns, and make faster, more informed decisions. In this paper, we explore how visualization techniques are revolutionizing network security, from analysing traffic and detecting intrusions to correlating security events. We also address real-world challenges, such as information overload, false alarms, and the difficulties of integrating these tools into large-scale systems. Looking ahead, we examine the future of security visualization—AI-driven insights, immersive environments like VR, and dynamic dashboards that make threat detection more interactive. By shedding light on these advancements, we highlight how visualization isn’t just a helpful tool but a critical component of modern, proactive cybersecurity.

DOI: http://doi.org/10.5281/zenodo.16607552

Published by: vikaspatanker

Authors: Ms. Gurpreet Kaur, Mayank Gupta, Kanak Sharma, Sarthak Goel

Abstract: The use of artificial intelligence (AI) in medicine has created medical Chat – bots that supports real -time patients, symptom assessment, early diagnosis and supportive patient training. However, traditional Chat – bot models based on static database or pre-influensing reactions have problems with chronic information, reference upheaval and the possibility of incorrect information. Recovery-sized generation (RAG) is a sophisticated AI model that supports the chat bot capacity by integrating a recovery system with generative AI, and ensures that reactions are relevant sounds and most infected with today's medical knowledge. This article emphasizes the main elements of the theoretical base and the real application of Raga-based medical chat bots that enable better accuracy, flexibility and user interactions. We discuss architecture, recycling process and response generation mechanisms that distinguish rag from traditional NLP – based chat-bots. In addition, we explain in detail about the significant strength of Rag, such as medical accuracy, real -time flexibility and adapted patient interaction. While the possibilities are very good, the implementation of carpet -based medical chat-bots is accompanied by computational overhead, data security and difficulties with regulatory requirements. We discuss these boundaries in adding possible solutions to make chat bot more reliable and effective. Case studies of real implementation also give us a picture of how effective they are and practically how they are used in modern health care. Finally, we identify future research directions by integrating RAG-based medical chat bot with new techniques such as IOT, Block chain and Multi-model AI to further change the digital health service. By addressing these main areas, this research tries to contribute to continuous progress of AI-driven medical chat bot, so that they can become an integral part of both health care professionals and patients.

DOI: http://doi.org/10.5281/zenodo.16608602

Published by: vikaspatanker

Authors: Babu S, Preetish Majumdar, Devarenti Hemanth

Abstract: Career Compass is an AI-powered job recommendation and career development system aimed at optimizing university students’ job placement outcomes. This version integrates advanced ML models, user feedback loops, and dynamic APIs. Key features include university shortlisting via the Gemini API, AI-powered mock interviews, a resume generator with feedback, an ATS score calculator, real-time news API, and an AI assistant for career guidance. This paper explores the architecture, implementation, and performance of Career Compass, demonstrating improvements in precision, recall, and user satisfaction.

DOI: http://doi.org/10.5281/zenodo.16608494

Published by: vikaspatanker

Authors: Mr.Ayush, Mr.Aditya

Abstract: The rapid advancements in artificial intelligence (AI) and drone technology have revolutionized surveillance, enabling real-time, automated security solutions. This paper presents an AI-powered intrusion detection system (IDS) for drone-based surveillance, leveraging YOLO (You Only Look Once) deep learning models for real-time object detection. The system autonomously identifies potential threats, such as weapons, sharp objects, or unauthorized personnel, and triggers automated alerts. By integrating high-definition cameras and AI-driven decision-making, the proposed system enhances security while reducing human intervention. Experimental evaluations confirm its efficiency in detecting intrusions with high accuracy. Future enhancements include integrating thermal imaging and LiDAR for improved detection.

DOI: http://doi.org/10.5281/zenodo.16608428

Published by: vikaspatanker

Authors: Gautam Yadav, Sachin Rawat, Ayush Ranjan, Mehul Sharma, Aditya Singh

Abstract: The rapid evolution of technology has revolutionized the field of marketing, enabling businesses to engage with customers more effectively, optimize campaigns, and drive sales. This paper explores the transformative role of technology in modern marketing, focusing on key advancements such as artificial intelligence (AI), big data analytics, automation, augmented reality (AR), virtual reality (VR), and blockchain. Beyond a conceptual review, this study contributes original findings through experimental evaluation of leading AI-powered tools such as IBM Watson and HubSpot. The comparative analysis quantifies setup time, engagement rate, ROI, and computational cost, offering practical guidance for tool adoption. The paper also addresses challenges like data privacy and ethical concerns and discusses emerging trends such as the metaverse and IoT integration. This hybrid approach makes the study a valuable resource for both academic researchers and industry professionals.

DOI: http://doi.org/10.5281/zenodo.16608872

Published by: vikaspatanker

Authors: Dr. Prakash P, Kannagi L, Aakash K, Amizhdhan L, Pragathesh Kumar

Abstract: The intelligent autonomous vehicle utilizing GPS and camera technology has been programmed to avoid obstacles in its environment. Unlike normal vehicles, this car combines computer vision features with GPS technology. The technology helps the car recognize obstacles and make evasive maneuvers in real-time. A camera is fixed on the car's chassis, and it is constantly recording the environment around it. The video frames are then analyzed using machine learning techniques to detect obstructions like walls, cars, pedestrians as well as anything else that may prevent the car from moving. The car is able to gather accurate visual information which helps it identify the obstacles shape, size, and position. In addition, the automobile contains a GPS module that provides adequate positioning to pinpoint the exact location of the car. The GPS module picks up signals from satellites to ascertain the car's current position with a high degree of accuracy. Likewise, the car also uses a decision-making algorithm that takes into consideration visual data from the camera, GPS data, and predefined permissible routes. The algorithm processes the relevant data and helps the system determine the most optimal route while avoiding obstacles. When an obstacle is detected in its path, the car automatically alters its trajectory to avoid the obstacle while maintaining its intended route. In summary, the obstacle-avoiding car that utilizes a camera and GPS module offers a promising approach to autonomous navigation [1]. By integrating computer vision methods with GPS positioning, the car can sense and react to its surroundings, ensuring safe and efficient travel in complex and ever-changing environments.

DOI: http://doi.org/10.5281/zenodo.16606351

Published by: vikaspatanker

Authors: Danish Khan, Professor Ar. Dilip Jade, Professor Ar. Gulfam Shaikh

Abstract: In the ever-evolving landscape of innovation, architecture is no longer just a backdrop; it is an active participant in shaping human thought, behavior, and performance. Neuroarchitecture—a discipline at the intersection of neuroscience and architectural design—explores how spatial environments influence brain function and cognition. As startup culture thrives and the demand for incubation centers increases, understanding the neurological impact of spatial design becomes vital. This research investigates the principles of neuroarchitecture and their application in designing incubation centers that foster creativity, productivity, collaboration, and psychological well-being. The paper highlights the science behind neuroarchitectural strategies and presents design guidelines and case studies that illustrate how spaces can be programmed to "think" with their users.

Published by: vikaspatanker