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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Authors: Nadiia Kudriashova, Alexander Mirza

Abstract: In recent years, generative artificial intelligence has gained traction, resulting in incredibly realistic synthetic multimedia content that can disseminate misinformation and mislead society. Deepfakes pose serious national security vulnerabilities since they enable sophisticated disinformation operations, foreign meddling, financial crime, and the erosion of faith in institutions. Deepfake detection and legal prosecution became an important agenda for contemporary nation-states. However, serious consequences of deepfakes for national security are still are not properly realized by legislative and regulatory establishment even in the countries of Five Eyes Alliance, known for its advanced cybersecurity awareness and policies. With this in mind, the article makes an attempt of integrating technological and legal domains of combating deepfake technology usage which causes serious consequences, within a single analytical model. Based on a combination of descriptive and exploratory research design, involving comprehensive literature review and semi-structured interviews with the experts across the fields of cybersecurity, machine learning, digital forensics, law, and ethics in the countries of Five Eyes Alliance (sample size 12 participants), the article outlines current landscape of deepfakes creation and detection technologies, as well as institutional awareness and legislative environment in the field of deepfakes law prosecution. The findings allowed making conclusion about scattered landscape of deepfakes identification and, at the same time, the evident lack of legal instruments to prevent deepfakes danger for national security even in the most developed countries, recently especially concerned with national security issues. The integration of findings allowed summarizing the essence of deepfakes serious consequences and developing integrative analytical model, based on Agile Security paradigm, implying predictive analysis of deepfake technology evolutive implications and options of appropriate criminal liability. The novelty of study lies in ‘organic’ combining of technological and legal planes of combating security danger of deepfakes, and the suggested integrative analytical model, based on Agile Security paradigm can become a starting point for further studies and developments in the field.

DOI: http://doi.org/10.5281/zenodo.17894064

 

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Authors: Vikram Chauhan, Anika Deshpande, Priya Nair, Vasudev Sharma

Abstract: Understanding the drivers of workforce outcomes requires analytical methods capable of distinguishing correlation from true causal influence. Traditional predictive models commonly used in HR systems can forecast attrition, performance, or engagement shifts, yet they offer limited visibility into the underlying mechanisms that produce these changes. This paper introduces a causal AI approach that integrates SAP SuccessFactors operational data, SAP Analytics Cloud workforce metrics, and diverse multi source HR signals to estimate the effects of organizational interventions on measurable employee outcomes. The proposed framework combines structural causal modeling, treatment effect estimation, mediation analysis, and counterfactual reasoning to evaluate how learning pathways, compensation adjustments, managerial behaviors, mobility opportunities, and work environment conditions contribute to changes in performance, retention, and development trajectories. A unified data architecture harmonizes information from SuccessFactors modules with analytical layers in SAP Analytics Cloud to construct causal ready datasets that isolate confounders and quantify both direct and indirect effects. Empirical evaluation across representative HR scenarios demonstrates that causal models provide more actionable insight than conventional predictive methods by clarifying which interventions meaningfully influence workforce outcomes and under what conditions. The study argues that embedding causal AI within enterprise HR ecosystems supports evidence informed decision making, strengthens workforce planning accuracy, and enhances the strategic value of people analytics in complex organizational environments.

DOI: http://doi.org/10.5281/zenodo.17895700

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Authors: Rohan Mehta, Arvind Sethi, Nisha Kulkarni, Vasudev Sharma

Abstract: Enterprises operating in complex digital ecosystems face accelerating growth in data volume, reporting demands, and governance obligations. Traditional rule-based automation remains insufficient for interpreting ambiguous business semantics, harmonizing heterogeneous information assets, or sustaining consistent reporting logic across distributed platforms. This study introduces a large language model powered semantic automation engine designed to unify enterprise reporting, knowledge extraction, and end-to-end data lifecycle governance. The research focuses on the central challenge of operationalizing generative models, retrieval-augmented reasoning, and dynamic semantic alignment to automate high-stakes analytical and compliance workflows while maintaining auditability, accuracy, and policy adherence. Using a mixed methodological approach that combines empirical prototyping, workflow instrumentation, and qualitative validation with enterprise architects, the study develops a layered architecture integrating semantic parsers, governance ontologies, vector-indexed knowledge repositories, and automated lineage reasoning. Findings show that LLM-driven inference strengthens metadata completeness, reduces manual reconciliation cycles, enhances cross-system reporting consistency, and improves lifecycle visibility from ingestion to archival. The study contributes a scalable framework for semantic automation, a reference ontology for enterprise reporting logic, and a set of design principles supporting trustworthy, context-aware automation across data-intensive environments.

DOI: http://doi.org/10.5281/zenodo.17895633

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Authors: Buya Lekha, Pramani Kota, Nallireddy Anu, Vasudev Sharma

Abstract: Advances in sensor miniaturization, pervasive connectivity, and scalable cloud architectures have accelerated the adoption of Internet-of-Things solutions in healthcare, enabling continuous physiological monitoring, early disease detection, and remote clinical interventions. Yet, the complexity of heterogeneous sensor data, variable patient contexts, and unpredictable network conditions still limit reliability and predictive accuracy in real-world deployments. This study develops a hybrid deep-learning pipeline that integrates convolutional neural networks, bidirectional recurrent architectures, and attention-based temporal encoders with cloud-native event stream processing to enable real-time interpretation of multimodal physiological signals. The research examines how edge-assisted inference, micro-batch stream analytics, and distributed message brokers collectively enhance detection latency, anomaly classification, and model robustness. A mixed-method methodology combines simulation-driven performance evaluation with empirical analysis of IoT device logs and consumable EHR-derived datasets. Results demonstrate significant improvements in prediction accuracy, event-processing throughput, alert precision, and resilience against noisy sensor streams. The findings highlight the potential of hybrid AI pipelines to strengthen remote patient monitoring, chronic disease management, and population-health surveillance while addressing operational barriers tied to privacy, scalability, and interoperability.

DOI: https://doi.org/10.5281/zenodo.17920642

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