Daily Archives: July 26, 2025

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

Authors: Srinivasan K, Thri shanth S, Thameswer U, ELLingesh M*, ANGOVAN K

Abstract: This project presents the design and implementation of an Android based pick and place robotic arm vehicle aimed at improving automation in industrial and hazardous workspaces The system consists of a robotic arm with four degrees of freedom mounted on a mobile platform that allows accurate object manipulation and controlled movement A Raspberry Pi is used as the central controller to operate motor drivers servos and sensors while receiving commands from a custom Android application Communication between the app and the Raspberry Pi is achieved wirelessly through Bluetooth or WiFi which enables real time control and monitoring An optional camera module is included to support image processing and object recognition giving the robot the ability to identify and sort objects with minimal human input This addition enhances the system’s adaptability to more complex tasks The use of open source hardware and software ensures that the solution remains cost effective scalable and accessible for small and medium enterprises During testing the robot successfully picked and placed items weighing up to 500 grams and responded effectively to user instructions sent through the app The Android interface provided a smooth user experience with intuitive controls The system performed reliably on flat surfaces but encountered minor difficulties on uneven terrain and showed limited gripper flexibility when handling irregular shaped objects This project highlights the potential of integrating mobile technology with robotics to reduce manual labor increase precision and improve operational safety in various fields The modular build and open design allow for easy future upgrades including artificial intelligence based object recognition adaptive gripping mechanisms and enhanced navigation over different types of surfaces The outcome of this project demonstrates a practical and efficient approach to developing robotic systems using widely available components and modern mobile interfaces

DOI: http://doi.org/10.5281/zenodo.16606087

Published by: vikaspatanker

Authors: Kanan Bajaj, Savita Baerda, Manni Kumar

Abstract: This research paper aimed to find out various things ChatGPT could do. Especially in the areas of reasoning, healthcare, and education, the authors observed how ChatGPT’s learning was more personal. It adapted to student needs, offering a personalized learning experience. This truly drew students into the equation. They found that ChatGPT’s skills in logical reasoning were effective for solving problems and performing critical thinking tasks. However, a large part of their research focused on healthcare. Here, they placed ChatGPT alongside other AIs such as Gemini and Copilot. They discussed how each of them was managed in terms of diagnostic accuracy and interac-tion. There were also some differences in specialization ChatGPT performed well with general medical questions and maintained coherent conversations. Nevertheless, Gemini and Copilot generally worked better when it came to specific medical operations because those programs were designed specifically for that purpose. The paper further explored how exactly ChatGPT operated in detail. It employed modern NLP techniques and certain methods from the field of machine learning. The authors believed its evolving design made it capable of handling numerous topics. However, they also explained that it had some moderate limitations at the time, and how it could potentially be im-proved in the future. Finally, they offered a brief overview of the strengths and weaknesses of ChatGPT depending on the area of application. Hopefully, it provided some valuable insights into where it stood in the ever-expanding li-brary of AI solutions.

DOI: http://doi.org/10.5281/zenodo.16605610

Published by: vikaspatanker