Authors: Professor Kavita Singh
Abstract: The electronic monitoring of biomedical devices has revolutionized healthcare delivery by enabling real-time diagnostics, continuous physiological tracking, and rapid response mechanisms. This study reviews the technological advances underpinning electronic biomedical monitoring systems, describes key classes of devices, and discusses engineering challenges and prospects for future developments
Authors: M. Venkataravanappa, K.N. Venkatachalaiah
Abstract: Combustion synthesis method was used to prepare Europium doped Barium silicate nanophosphors. The crystalline structure from PXRD profiles showed that the fabricated sample have orthorhombic phase [JCPDS Card No. 78-1371] with a face group Pb nm- 62, with no variation in the diffraction profiles because of the inclusion of the Eu2+ions. The images are regular and irregular shapes with smooth surface were observed from SEM. The photometric spectra were studied for optimized nanophosphor displays green emission at ~ 505 nm due to the presence of Eu2+ ions corresponds to 5D07F2 transition. The CIE arrangement was green spread, which are basically near to the standard characteristics and Correlated Color Temperature (CCT) was acquired 12236K. These outcomes showed that the fabricated NPs can be viably utilized as green color part in the fabrication of white light emitting diodes.
Authors: Sagar Gupta, Vikas kumar
Abstract: Hospitals often face immense challenges related to resource utilization and managing these resources efficiently in light of increasing demands from patients and the volume of data. The use of traditional centralized healthcare computing systems introduces latency and inefficiencies related to real-time decision-making. This chapter reviews how Edge AI can transform hospital resource utilization. By processing data closer to its source using edge devices, Edge AI allows for real-time analytics, proactive resource allocation, and responsiveness of operations. This chapter details the current challenges in hospital resource management, the architecture of an Edge AI-driven resource management system, and also discusses the case studies for their implementation. Quantitative evaluation regarding improved performances such as reduced wait time for patients and improvement in the bed occupancy rate is discussed. Integration of Edge AI with IoT and other emerging technologies such as 5G and federated learning is also considered as future work. Our analysis further shows that Edge AI increases not only hospital efficiency but also better patient outcomes through intelligent and timely interventions.
Authors: Kashish Aggarwal, Mr.Vikas kumar
Abstract: One of the most powerful and ethically debatable technologies of the 21 st century are Facial Recognition Tech- nology (FRT), which is also driven by Artificial Intelligence (AI). FRT can be used to automate the identification and verification of individual persons under different conditions, such as law enforcement, border control, and digital authentication, by relying on machine learning and deep neural networks. Even though the technology has a positive impact on reducing safety and efficiency, it poses significant ethical issues concerning privacy, data protection, bias, consent, and responsibility. The paper is a thorough overview of the ethical aspects of AI-driven facial recognition, the benefits that it has, and the vulnerabilities of this technology. It studies the problem of algorithmic bias, data governance, and ethical dimensions of surveillance-based applications. Global regulatory reactions to the subject, including the European Union General Data Protection Regulation (GDPR), the suggested AI Act, and other upcoming data protection regulations in the United States and India are discussed to point out differences in regulation. In addition, the paper explains mitigation measures such as fairness-conscious algorithms, transparency, and privacy- sensitive methods to encourage the responsible use of AI. This research highlights the importance of balancing between innova- tion and accountability as a means of seeking to fulfill societal needs without infringing on human rights by critically analyzing and conducting case-based reviews that conclude that facial recognition can be used to the benefit of society without taking away the rights of the people.
Authors: Reeva Rawat, Ronit Roy, Vivek Sharma, Soham Andhyal, Dr. Ravi Rai Chaudhari
Abstract: The UNI Verse platform is designed to create a more intelligent, engaged campus and digital learning environment. It provides a single platform for all organizational, informational, and engagement needs of students and faculty to facilitate their learning and academic experience. It gives students a centralized location to learn about their academic engagement, course schedule, office hours of professors, assignment due dates, and upcoming campus activities. UNI Verse will even offer reminders, to help keep students informed about deadlines & updates on campus. In addition to the academic purpose, UNI Verse allows students and faculty to have more substantive communications with the real-time direct messaging and meeting scheduling options. It even provides tools to navigate campus or even journal or share notes digitally, all to foster collaboration with classmates and engage as a learning community outside and inside of the classroom. Ultimately, UNI verse is a hub and usable system for productive, engaged, interactive, and organized campus life.
DOI: https://doi.org/10.5281/zenodo.17657734
Authors: Saransh Khanna, Mr. Ritesh Kumar Chandel
Abstract: This research investigates the development of an intelligent expense tracking system powered by Retrieval-Augmented Generation (RAG). Traditional expense tracking tools rely on structured inputs and static categorizations, creating friction for users who log expenses in natural language. The proposed system uses a hybrid architecture of vector retrieval and generative reasoning to extract accurate financial insights from unstructured text, improving reliability while reducing hallucinations commonly seen in standalone LLMs.
Authors: Yash Sharma, Mr. Ritesh Kumar Chandel
Abstract: Travel planning requires synthesizing large and diverse datasets including destinations, transportation, budgets, accommodations, user preferences, and seasonal variations. Traditional tools depend on static rules and manual inputs, making them inefficient for personalized planning. This research introduces an AI-powered RAG-based trip planner that integrates vector retrieval with generative reasoning. The system mitigates hallucinations, enhances real-world grounding, and produces optimized, personalized itineraries.
Authors: Carl Angelo S. Pamplona, Menchie A. Dela Cruz
Abstract: The web-based strategic performance management system for President Ramon Magsaysay State University (PRMSU) was developed to provide a more streamlined and centralized way of handling performance management–related tasks. The main purpose of the study was to evaluate the developed system in terms of software quality (using ISO/IEC 25010:2011 metrics), level of acceptability, and level of readiness of PRMSU. The study also identified significant differences between the evaluations of PRMSU staff and supervisors on (a) software quality of the system, (b) acceptability of functionality and performance, and (c) readiness for implementation in terms of information system facility and technical personnel. Based on the respondents’ evaluation, the developed system’s software quality was “Excellent,” its level of acceptability was “Highly Acceptable,” and its readiness for implementation was “Very Ready.” There was no significant difference between staff and supervisor evaluations on software quality or on any of the measured domains (Functional Suitability, Performance Efficiency, Compatibility, Usability, Reliability, Security, Maintainability, and Portability). Finally, the researcher provided recommendations including full implementation of the system, periodic re-evaluation and maintenance, user training, and ongoing studies to align with evolving trends such as implementation of data analytics and artificial intelligence functionalities
DOI: http://doi.org/10.5281/zenodo.17657287
Authors: Rajeshkumar S. A, Vishrut Nath Jha
Abstract: The rapid evolution of artificial intelligence in chem- istry has enabled autonomous systems capable of exploring vast chemical spaces and identifying novel compounds with potential pharmacological value. We introduce MoleculAR, an autonomous agentic framework that integrates molecular relationship discov- ery, quantum-level stability analysis, and cheminformatics-based novelty verification. Given a set of input molecules, MoleculAR predicts potential co-functional partners using hybrid structural and functional similarity analysis, followed by energetic and stability evaluation through quantum chemical computations. Subsequently, the system performs compound novelty verification via cross-referencing with the ChEMBL database. Molecules that are predicted to be both chemically stable and absent from ChEMBL are shortlisted for further investigation. By combin- ing agentic reasoning, computational chemistry, and database- driven validation, MoleculAR establishes a closed-loop discovery pipeline that enhances efficiency in de novo compound identifica- tion. Experimental evaluations demonstrate MoleculAR’s ability to autonomously identify stable and novel molecular candidates across diverse chemical classes.
