Category Archives: Uncategorized

Authors: Mr. S. Kaushik Raj, Mrs. B. Shyamala Devi

Abstract: Diabetic retinopathy (DR) is one of the major causes of vision loss worldwide, making early and reliable detection extremely important. This work presents an advanced transformer-driven framework combined with a Multiple Instance Learning (MIL) strategy to classify DR using retinal fundus images. The transformer model effectively learns long-range relationships and contextual patterns, while the MIL approach analyzes image patches to highlight clinically significant areas. Together, this hybrid system delivers strong feature representation and stable classification performance, even with variations in image quality and resolution. Trained on a large and diverse dataset, the proposed model achieves higher sensitivity and specificity than many existing deep learning techniques. The system is designed to assist eye-care professionals by enabling accurate, timely assessments and providing a scalable solution suitable for extensive DR screening initiatives.

DOI: https://doi.org/10.5281/zenodo.17680366

Published by: vikaspatanker

Authors: Mr.S. Sathish, Ms. A. Sangeetha

Abstract: Object detection is a key area in computer vision with wide-ranging applications such as autonomous driving, surveillance, and augmented reality. YOLOv8, an advanced version of the YOLO series, stands out for its high accuracy and real-time performance. This study focuses on the analysis and implementation of YOLOv8 for real-time object detection, emphasizing its architecture that employs a deep neural network to perform a single forward pass for predicting bounding boxes and class probabilities simultaneously. The model’s main components—backbone network, detection layers, and anchor boxes—work together to achieve fast and efficient detection. Practical aspects such as model optimization, GPU acceleration, and post-processing are also explored to enhance speed and accuracy. Experiments conducted on benchmark datasets and real-world data demonstrate YOLOv8’s effectiveness, proving it to be a robust and adaptable solution for real-time object detection tasks. This research contributes to the advancement of computer vision and provides practical insights for deploying YOLOv8-based detection systems across multiple domains.

DOI: https://doi.org/10.5281/zenodo.17680246

Published by: vikaspatanker

Authors: Dr.P.Guhan, Mr.C.Barath

Abstract: Gesture-controlled computers and laptops have recently become increasingly popular, with Leap Motion technology leading this innovation. This technique enables users to control certain system functions simply by moving their hands in front of a camera. Compared to traditional slides or overhead projectors, computer-based presentations offer greater interactivity through audio, video, and programmable elements, though they can be more complex to use. As technology continues to evolve, finding new and affordable ways to interact with computers has become essential, especially since touchscreens are not feasible for all applications. To address this, a virtual mouse system based on object tracking and hand gestures is proposed as an alternative to physical mice and touch interfaces. The system employs computer vision techniques using Python and OpenCV, with a webcam detecting hand movements through HSV color segmentation. Users can wear colored caps or tapes on their fingers to move the cursor and perform actions like left-click, right-click, and double-click. The camera feed is processed in real time and displayed on the screen, allowing smooth, contactless interaction.

DOI: https://doi.org/10.5281/zenodo.17680123

Published by: vikaspatanker

Authors: Sabitha.K, Mohan Sundar.M, Santhosh. A. S, Sri Raghav. B. V, Supriya.A

Abstract: The Vehicle Accident Detection and Notification App is a mobile safety tool designed to identify vehicle accidents and automatically notify emergency services and family members. Utilizing the smartphone's accelerometer and gyroscope sensors, the app tracks sudden impacts, sharp turns, or rapid stops that suggest a potential collision. Upon detecting unusual movement, the GPS feature activates to pinpoint the exact location, ensuring that emergency responders can get to the scene quickly, even if the user is unconscious. The app automatically sends a comprehensive alert message that includes the user's information, timestamp, and precise coordinates to registered contacts. To minimize false alarms, it incorporates a brief confirmation timer that enables the user to cancel the alert. Additional functionalities such as a manual SOS button, cloud-based data storage, and automated calling improve reliability. Overall, the app provides an efficient, affordable way to enhance road safety and decrease fatalities.

Published by: vikaspatanker

Authors: Himanshu Kumar Rai, Harsh Goyal, Dr. Anshu Gupta

Abstract: This research proposes a Geo‑AI based smart attendance monitoring system that will integrate Artificial Intelligence (AI) with Global Positioning System (GPS) geofencing to provide accurate, contactless, and secure attendance tracking. The proposed system will address persistent challenges such as proxy attendance, location spoofing via mock‑GPS tools, and manual recording errors by combining AI‑based facial recognition (with liveness analysis) and geolocation verification. A mobile front end will enable frictionless check‑ins, while a cloud back end will ensure secure storage, auditability, and real‑time analytics. We detail the proposed architecture, algorithms, and implementation plan that will use a React Native application, Node.js/Express services, MongoDB storage, and Python micro‑services for inference and anomaly detection. If successfully implemented, in a controlled deployment across two office sites the system is expected to achieve 98% attendance‑marking accuracy, is projected to reduce administrative overhead by approximately 60%, and is anticipated to deliver alert latencies below five seconds. We will compare our approach with RFID, fingerprint biometrics, and GPS‑only mobile apps, and we will report an ablation analysis to quantify the benefits of liveness checks and geofence validation.’

DOI: http://doi.org/10.5281/zenodo.17679353

Published by: vikaspatanker

Authors: Manisha Rai, Vishal Rai

Abstract: Human emotions exhibit complex, nonlinear behaviors that traditional linear psychological models fail to capture adequately. This paper proposes a comprehensive framework for understanding emotional dynamics through the lens of chaos theory and nonlinear dynamical systems. We examine how concepts such as strange attractors, bifurcations, Lyapunov exponents, and phase space representations can illuminate the intricate patterns underlying affective processes. Empirical evidence from heart rate variability studies, electroencephalographic analyses, and longitudinal mood assessments supports the view that healthy emotional functioning corresponds to a bounded chaotic regime characterized by optimal complexity and adaptability. We further explore how deviations from this regime—manifesting as either excessive rigidity or instability—may underlie various mood disorders including depression, anxiety, and bipolar disorder. The paper presents mathematical formulations for emotional phase space dynamics, discusses computational approaches for reconstructing emotional attractor landscapes from empirical data, and outlines applications in clinical psychology, affective computing, and personalized mental health interventions. Understanding emotions as emergent properties of complex dynamical systems offers profound implications for diagnosis, treatment, and the development of emotionally intelligent technologies.

Published by: vikaspatanker

Authors: Mr. Mayank Arora, Nupur

Abstract: The rapid expansion of digital technologies has transformed the landscape of contemporary journalism, bringing data-driven reporting to the forefront of news production. In India, the rise of data journalism—an approach that integrates statistical analysis, visualization tools, and storytelling—has opened new possibilities for accuracy, depth, and transparency in media reporting. Yet, the adoption of data journalism remains uneven, complicated by structural issues within Indian newsrooms, limited technological expertise, and the pressures of fast-paced news cycles. This paper investigates how Indian news organizations understand, adopt, and implement data journalism practices. It explores the professional, infrastructural, and ethical challenges that constrain data-driven reporting, while also identifying opportunities created by digital literacy, open-data movements, and audience demand for evidence-based journalism. Through a review of scholarly literature, industry reports, and comparative perspectives, the study highlights how data journalism in India stands at a critical intersection of innovation and limitation. The paper argues that although data journalism has the potential to strengthen public discourse and democratic accountability, its growth depends on sustained investment in training, technological resources, and editorial vision. Ultimately, the study positions data journalism as an evolving journalistic paradigm that can contribute significantly to India’s media ecosystem if supported by a culture of transparency, collaboration, and professional development.

Published by: vikaspatanker

Authors: Sameer Agarwal, Vaibhav Kalukar

Abstract: Human–Robot Interaction (HRI) is an interdisciplinary field that examines how humans communicate, collaborate, and coexist with robotic systems. With major advancements in artificial intelligence, sensor technologies, and automation, robots are increasingly becoming interactive partners in healthcare, manufacturing, education, and personal assistance. This research investigates current trends in HRI, emphasizing the shift from traditional command-based systems to socially aware, adaptive, and collaborative robots. A qualitative review methodology is used to analyze studies published between 2015 and 2024, focusing on social robots, industrial cobots, healthcare and assistive robotics, and service-oriented systems. Findings reveal rapid growth in socially interactive robots, widespread adoption of collaborative robots in industry, and enhanced communication through AI-driven speech, gesture, and emotion recognition. Despite challenges such as limited emotional intelligence, ethical concerns, and high costs, the increasing integration of robots into human environments highlights significant potential for future development. The study concludes that HRI will play a crucial role in shaping intelligent, human-centric robotic systems, requiring continued research in transparency, trust, and ethical design.

DOI: https://doi.org/10.5281/zenodo.17670137

Published by: vikaspatanker

Authors: Mr. Karthiban R, Tharun S, Naveen B, Yuvaraj S, Pragathiswaran G

Abstract: Urban site visitors congestion has come to be one of the most critical challenges confronted via cutting-edge towns. Emergency automobiles, wi-fi ambulances, enjoy frequent delays due to traffic wireless indicators and roadblocks, which may be deadly in lifestyles-threatening conditions. This paper proposes a smart visitors control device that mixes automatic wide variety Plate recognition (ANPR) and ambulance signal detection to make sure actual-time prioritization of ambulances. The proposed system makes use of digicam-primarily based vehicle identity, an IoT-enabled site visitors manage network, and adaptive sign timing to create a “inexperienced corridor” for emergency automobiles. Experimental simulations imply that the device can reduce ambulance waiting time at intersections via extra than 60%, improving the wi-fi of emergency reaction services. the combination of ANPR era permits automatic detection of ambulances without counting on guide systems or wireless tags, making it scalable and price-effective for clever city deployment.

Published by: vikaspatanker

Authors: Dhyanesh M, Dharshini S, Deepak P, Aisha Amna A, Mr. S. Dhinakaran

Abstract: EduHelm is an intelligent AI-driven career and learning platform designed to personalize and optimize student growth through dynamic learning paths, adaptive projects, and real-time skill tracking. The system leverages machine learning and large language models to recommend personalized roadmaps, micro-projects, and certification-based goals aligned with each user’s academic profile and aspirations. EduHelm integrates AI mentorship, progress analytics, and gamified challenges to foster continuous improvement and engagement. It also enables students to upload learning journals and research patterns, automatically analyzing them for feedback and improvement suggestions. Developed using a React frontend, Flask backend, and PostgreSQL database, with AI modules powered by OpenAI and Hugging Face, EduHelm delivers a seamless and intelligent experience that empowers learners to take control of their career growth in the digital age.

Published by: vikaspatanker