Category Archives: Uncategorized

Authors: Sujay Dey, Shrey Jaiswal, E Hemasabari

Abstract: The rapid growth of social media platforms has transformed digital communication, content sharing, and online collaboration. This project presents the development of a full-stack social media application using Spring Boot for the backend, React.js for the frontend, and Cloudinary for cloud-based media storage and management. The system is designed to provide core social networking features such as user authentication, profile management, post creation, image and video uploads, likes, comments, and real-time interaction. Spring Boot is utilized to build secure and scalable RESTful APIs, ensuring efficient handling of business logic and database operations. React.js enables the creation of a responsive and dynamic user interface, enhancing user experience through component-based architecture and state management. Cloudinary is integrated to handle media uploads, storage, and optimization, reducing server load and improving performance. Security mechanisms such as JWT-based authentication and role-based access control are implemented to protect user data and ensure authorized access. The proposed application demonstrates how modern full-stack technologies can be effectively integrated to build a scalable, secure, and user-friendly social media platform. This project highlights practical implementation strategies and serves as a foundation for further enhancements such as real-time notifications, chat functionality, and advanced analytics.

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Authors: Abhinay Gour, Prof. Geeta Santosh

Abstract: In the evolving landscape of digital communication, ensuring the authenticity and reliability of email addresses and domains is critical for maintaining security, optimizing deliverability, and mitigating fraud. This paper presents an integrated architecture for an AI-driven email verification and domain intelligence system, combining real-time validation, domain reputation assessment, and a dynamic risk scoring engine. The proposed system leverages machine learning algorithms to detect syntactic anomalies, validate domain existence, and assess historical engagement patterns, while incorporating threat intelligence to evaluate potential risks such as phishing, spam, and disposable addresses. By unifying these components, the architecture not only enhances email deliverability but also provides actionable insights for cybersecurity and marketing strategies. Experimental results demonstrate that the AI-enhanced approach significantly outperforms traditional rule-based verification methods in accuracy, response time, and risk detection, offering a scalable solution for organizations requiring robust email and domain trustworthiness assessment.

 

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Authors: Hachimenum Nyebuchi Amadi, Happy Prince Nwokoegi, Richeal Chinaeche Ijeoma

Abstract: Distribution efficiency in developing power systems is often undermined by excessive reactive power demand, poor voltage regulation, and high technical losses. The Port Harcourt mainstream 33 kV distribution network in Nigeria, a critical urban supply corridor, is particularly vulnerable to these inefficiencies due to its radial structure, overloaded transformers, and weak reactive power support. Such conditions result in low power factor, under voltage problems, and distribution losses that exceed international performance standards, thereby threatening supply reliability and quality of service. In this study, the Port Harcourt 33 kV distribution network was modeled in MATLAB/Simulink to evaluate its operational performance and investigate the effectiveness of advanced power factor correction (PFC) using a Distribution Static Synchronous Compensator (D-STATCOM). Baseline simulations revealed progressive voltage deterioration along the feeder, with the weakest bus falling to 0.910 p.u., well below the operational limit of 0.95 p.u. Furthermore, the system recorded active power losses of 1.604 MW, equivalent to 9.5% of peak demand, substantially higher than the 2–6% technical loss benchmark recommended by IEEE for efficient distribution systems. Following the integration of D-STATCOM into the network, remarkable improvements were observed. All bus voltages were restored within 0.989-0.999 p.u., with the weakest bus improved from 0.910 p.u. to 0.989 p.u., thereby ensuring compliance with the 0.95-1.05 p.u. standard. In addition, total technical losses decreased sharply to 0.283 MW, equivalent to 2.0% of peak demand, placing the network well within international best-practice thresholds. The findings confirm D-STATCOM as an effective and sustainable solution for improving voltage stability, minimizing technical losses, and enhancing reliability in urban distribution networks.

DOI: https://doi.org/10.5281/zenodo.17918646

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Authors: Aakib Beg, Prof. Geeta Santhosh HOD

Abstract: The rapid digital transformation of the automobile service industry has highlighted the inefficiencies of traditional workshop management methods, especially in multi-brand environments. Manual billing, fragmented inventory tracking, and inconsistent labor pricing frequently lead to revenue losses and customer dissatisfaction. Motor Car Hub is developed as a comprehensive Enterprise Resource Planning (ERP) system built on the MERN stack—MongoDB, Express.js, React.js, and Node.js—to address these challenges. Untitled document (2)This study expands on the original system architecture, presenting a deeper analysis of module interactions, database workflows, performance benchmarks, and the measurable operational improvements achieved in real-world simulations. The enhanced paper discusses MERN-driven scalability, the significance of schema flexibility, automated GST-compliant billing, technician performance tracking, inventory forecasting, and role-based access governance. The system shows an 80% reduction in invoice processing time, complete elimination of manual billing discrepancies, and substantial gains in accountability. These expanded insights provide strong evidence that MERN-based ERP solutions can revolutionize automotive workshop management, making operations more transparent, accurate, and data-driven.

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Authors: B.SathiyaSivam, S.Sriram

Abstract: This paper presents a novel smart wireless electric vehicle (EV) charging station that integrates solar photovoltaic (PV) and piezoelectric road-energy harvesting with a smart-grid-connected common DC bus architecture. The proposed system employs bidirectional Wireless Power Transfer (WPT) for Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) operation and an intelligent Energy Management System (EMS) for real-time power optimization. Maximum Power Point Tracking (MPPT), adaptive load balancing, and predictive renewable forecasting enhance overall energy efficiency. Advanced coil-alignment sensors enable high coupling efficiency, while a bidirectional DC/AC interface ensures stable grid interaction. The system aims to provide eco-friendly, contactless, and efficient EV charging suitable for smart cities, highways, and autonomous transportation networks.

 

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Authors: Manvir Kaur, Gurpreet Singh, Varuna Tyagi

Abstract: The rapid advancement of digital technologies has transformed healthcare, demanding intelligent and automated health monitoring systems. Traditional healthcare infrastructures often face challenges such as limited medical staff, delayed diagnosis, and lack of real-time monitoring. This research proposes an Internet of Things (IoT)-based advanced health monitoring system using the MAX30100 sensor for continuous measurement of heart rate (HR) and blood oxygen saturation (SpO₂). The system leverages microcontrollers such as Arduino and ESP32 for sensor interfacing and wireless data transmission to cloud platforms for real-time visualization, processing, and storage. Signal processing techniques, including noise filtering, peak detection, and smoothing, ensure accurate measurement. The proposed system addresses limitations of existing commercial devices by providing a low-cost, reliable, and scalable solution for remote patient monitoring, early disease detection, and data-driven healthcare management.

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Authors: Mohammad Zaid Khan, Dr. Arvind Jaiswal

Abstract: The rapid growth of digital health platforms has created an urgent need for intelligent clinical decision-support tools that can interpret patient-reported symptoms and streamline care navigation. This work presents an enhanced architecture for MediTrack, a healthcare management platform, through the integration of a BioBERT-powered symptom triage and specialist recommendation engine. Leveraging domain-specific language representations, the system processes free-text symptom descriptions, identifies likely clinical categories, and recommends appropriate medical specialties with improved accuracy and contextual relevance. The proposed architecture combines natural-language preprocessing pipelines, BioBERT inference modules, probabilistic triage scoring, and a rule-augmented recommendation layer. Furthermore, the integration design emphasizes scalability, interoperability with existing MediTrack services, and compliance with healthcare data-protection standards. Experimental evaluation using benchmark clinical-symptom datasets demonstrates significant gains in classification performance and user-experience efficiency. This enhancement positions MediTrack as a more responsive, intelligent, and patient-centric digital health orchestration platform.

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Authors: Aqsa Almas Sheikh, Eram Shamim Ur Rehman Khan, Naseem Husain, Krishna Prasad Pal

Abstract: The concept of metaverse represents an innovative change in digital interaction by combining virtual reality and expanded reality with a common experience. There is an integrated virtual common space created by combining virtually expanded physical reality with normal physical virtual reality. This content examines various aspects of metaverse, including underlying technologies (such as blockchain and AI) and applications in a variety of fields such as entertainment, education and business. It also examines the possibilities of new social and business forms, such as issues of privacy and digital justice, and the impact of businesses on society. A combination of findings from research and practice should provide this content with a better understanding of the impact of metabar on future digital ecosystems and community interventions. This is a rapidly evolving digital limit that transforms physical and virtual reality into an immersive, interactive, and persistent environment. For progress in Virtual Reality ( VR), Augmented Reality (AR), Blockchain and Artificial Intelligence (AI), users can create contacts, work, play and handles in the 3D room. It promises transformative impacts in a variety of sectors, including education, healthcare, entertainment, real estate, and long-distance work. This article examines the fundamental technologies behind metaverse, their potential socioeconomic implications, privacy, data security, digital identity and interoperability challenges. Metaverse can still interact with digital systems and cooperation during the development stage, but it shows the next important development in the use of the Internet.

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Dr.Ratnakaram Raghavendra
Affiliation	Academic Consultant to teach M.Sc., Mathematics,SRI KRISHNADEVARAYA UNIVERSITY, ANANTHAPURAMU
Email-Id:	raghuratnakaram@gmail.com
Publication:    Mr. Ratnakaram Raghavendra & Dr. A. Saila Kumari two Dimensional (2-D) Convection & Diffusion Transport equation of Galactic Cosmic Rays by Linearization with cole Hopf transformation and Conservative Diffusion Form international of Creative Research ThoughtsJournal (IUCRT) page Nos., c401 to c407; Volume 10; Issue 8;Page 18/08/202 ISSN No: 2320-2882 Mr. Ratnakaram Raghavendra & Dr. A. Saila Kumari Diffusion-Convection Equationof Galactic Cosmic Rays (GCR) inthe atmosphere and its Analytical,Numerical solutions by usingFinite Elements Method using Parkers Transport Equation Journal of Advances in Mathematics and Computer Science page Nos. 133-145;Volume 38; Issue 7;AIP 17/05/2023 ISSN No. 2231-0851 Mr. Ratnakaram Raghavendra & Dr. A. Saila Kumari transport Equation of GalacticCosmic Rays (GCR) in the atmosphere using differential & partial Differential Equation AIP conference Asian Proceedings AIP conference proceedings 2649, 020005(2023) 21/06/2023 ISSN No. 1551-7616 Dr. Anna Reddy Saila Kumari $ Mr.Ratnakaram Raghavedra , Cosmic ray detector using geiger tubes and coincident pulses asian research journal of mathemmatics page nos.25-37; volume 19;issue 10 07/08/2023 ISSN No.2456-477X Mr. Ratnakaram Raghavendra $ Dr.A. Saila Kumari,Analysing Cosmic Ray density distribution using varible separable method in diverse spatial domains Indian journal of physics (springer nature-(scie) page no.01-10 volume 98 issue 10 published on 01-08-2024 ,EISSN:0974-9845 PISSN:0973-1458.

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Authors: Priti shivaji Birajdar, Ambika Ganesh Kshirsagar, Shravani Hanumant Raut, Harshada Machindra Raykar

Abstract: Early detection of brain-related diseases plays a crucial role in improving treatment outcomes, reducing mortality, and enhancing the quality of patient care. However, traditional diagnostic methods—such as manual MRI/CT scan interpretation and neurological assessments—are time- consuming, error-prone, and highly dependent on specialist expertise. To address these limitations, this study presents an Artificial Intelligence (AI)-based tool designed for the early detection and classification of multiple brain disorders, including brain tumors, stroke indicators, Alzheimer’s disease patterns, and abnormal EEG activity. The proposed system integrates advanced deep learning techniques, including Convolutional Neural Networks (CNNs), hybrid feature extraction, and medical imaging analytics, to automatically identify subtle abnormalities that may be overlooked by human observation. A comprehensive dataset comprising MRI scans, CT images, and EEG signal recordings was used to train and validate the model. The images were preprocessed using noise reduction, skull stripping, normalization, and region-of-interest extraction to improve diagnostic accuracy. The model was trained using supervised learning and evaluated using performance metrics such as accuracy, sensitivity, specificity, precision, recall, and F1-score. Experimental results demonstrate that the AI tool achieves high accuracy in early-stage detection, outperforming conventional diagnostic methods and providing faster, consistent, and automated analysis. The system holds significant potential for use in hospitals, rural clinics, telemedicine platforms, and large-scale screening programs. It can support neurologists by acting as a decision- support tool, reduce diagnostic delays, and contribute to improved patient outcomes. Future work will focus on expanding the dataset, integrating real-time monitoring, and enhancing the system’s capability to detect additional neurological disorders using multimodal data. Overall, the proposed AI tool demonstrates that artificial intelligence can be a transformative technology in the field of brain disease diagnosis and early prediction.

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