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.

Published by: vikaspatanker

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

Published by: vikaspatanker

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.

Published by: vikaspatanker

Authors: prof. Dharmaraj s kumbar

Abstract: Artificial Intelligence (AI) has definitely revolutionised the world of science and technological era. Specifically, the introduction of AI has taken place in a revolutionary shift in the field of education. AI's capabilities are transforming education in the future, from automated assessment systems to personalised learning experiences. But even with this technological development, there is still an important issue with teachers and AI not working together. One of the greatest obstacles to fully deploying AI is this coordination gap. The complex interactions that result from integrating AI into the educational system are examined in this study report. It mostly concentrates on the difficulties in attaining harmony between educators, learners, and educational institutions. While AI has the ability to offer data-driven insights in the area of education, it currently falls short of meeting expectations of human interaction and educator's experience-based judgements. Additionally, this study explores the different impacts of applying AI on teacher performance. AI has the ability, on the one hand, to expand educational chances, lessen administrative tasks, and enhance educational standards. It can improve productivity for educators. However, there are questions about how AI will impact educator classroom management and professional ability. This study looks at how AI is transforming the job of educators and how it affects student-teacher engagement. Finding a balance between artificial and human intelligence is essential for the future of education. In order to make full use of AI in education, this study examines strategies for strengthening collaboration between educators and technology.

Published by: vikaspatanker

Authors: Sharayu Anap, Divyanshu Rajhans, Atharva Naik, Srividhya Achanta, Vedant Jadhav

Abstract: The global logistics and transportation industry faces persistent challenges related to inefficient container utilization, manual space booking, and communication delays among stakeholders. The proposed system, Smart Shared Container Space Management Platform with AI-Based Container Matching and Real-Time Booking System, addresses these issues through an integrated, cloud-enabled digital platform that connects Container Providers and Traders in a shared, intelligent ecosystem. The platform leverages Artificial Intelligence (AI) for smart container- space recommendations, enabling efficient matchmaking based on trader requirements such as size, cost, duration, and proximity. The system ensures seamless, real-time operations through automated booking workflows, live availability tracking, and AI-assisted approvals—monitored via a centralized Admin Dashboard. Developed using a modern web technology stack, the system employs Next.js / React for an interactive frontend, Supabase for structured data storage, and Firebase for authentication and real-time synchronization. The AI chatbot is powered by the OpenAI API (or a custom GPT endpoint), providing users with intelligent assistance for queries and bookings. Tailwind CSS and ShadCN UI deliver a clean, responsive, and modern interface, while payment modules such as Stripe or Razorpay enable secure transaction processing. By integrating AI intelligence, automation, and real-time cloud connectivity, the proposed platform significantly enhances container space utilization, reduces idle storage time, and improves coordination between traders and providers. This system sets the foundation for a next-generation logistics management ecosystem that is scalable, data-driven, and powered by intelligent automation.

Published by: vikaspatanker

Authors: Dr. Rishu Agarwal

Abstract: Penetration power of water is a different quality that makes universal solvent much more useful. There are various factors affecting penetration of water including pressure, velocity, temperature, present minerals in water. It also depends on the surface of stone on which water is thrown. Present study is focused on study of various factors affecting penetration power of water.

Published by: vikaspatanker

Authors: Suraj Yadav, Girdhari Lal

Abstract: Electronic Medical Records contain extremely private medical data, electronic medical records are frequently shared between authorized parties. Maintaining the integrity and security of that procedure is still very difficult. Blockchain technology provides a promising basis for creating and systems that are resistant to tampering with EMR data management and sharing. The blockchain-driven strategy for safe EMR sharing between healthcare organizations and research institutes is covered in this paper. We offer a framework created in collaboration with Stony Brook University Hospital to support data exchange for cancer patient care scenarios. A The implementation of the prototype confirms that the suggested system improves data availability, increases privacy, and permits precise, role-based access control.

Published by: vikaspatanker

Authors: Kartavy Gupta

Abstract: Artificial Intelligence (AI) is rapidly transforming the Indian education system, offering personalized learning, improved accessibility, and efficient administration. This paper examines the role of AI in Indian education, combining secondary research with a small-scale student survey. The survey, conducted among 50 senior secondary students from urban and semi-urban schools in India, explored awareness, usage, and perceptions of AI in learning. Results indicate that while 82% of students use AI-powered tools for study support, only 38% receive any formal school-based AI education. The findings highlight both enthusiasm for AI and the need for structured training, equitable access, and ethical guidelines. The study concludes that AI can revolutionize Indian education if implemented inclusively and responsibly.

Published by: vikaspatanker

Authors: Lingaraju, Sathish kumar B N

Abstract: Utility-scale solar park siting in semi-arid regions requires balancing physics-driven energy yield with engineering, environmental, and operations constraints under substantial uncertainty. This paper presents a transparent fuzzy multi-criteria framework that (i) maps raw criteria to physically motivated desirability memberships-monotone linear functions for benefit/cost attributes (global horizontal irradiance, slope, grid distance, water distance, dust days, protected-area buffer, land-use suitability) and a bell-shaped (Gaussian-like) function for module-temperature effects-(ii) derives criterion weights using fuzzy AHP from linguistic pairwise judgements, and (iii) aggregates via a TOPSIS-like closeness measure in the desirability space. A realistic 12- site semi-arid screening dataset is used to demonstrate the workflow. The resulting normalized weights emphasize energy yield (GHI, 0.377) and host-land compatibility (land-use, 0.132; temperature, 0.115), while accounting for terrain (slope, 0.092), network access (grid distance, 0.081), O&M logistics (water distance, 0.052; dust days, 0.058 ), and biodiversity safeguards (protected buffer, 0.092). The final closeness coefficients rank sites S12," " S10, and S 02 as top candidates, driven by high irradiance ( ≥6.2kWhm^(-2) day ^(-1) ), gentle slopes ( <3% ), reasonable grid proximity, suitable land cover, and adequate environmental buffers. Sensitivity checks indicate ranking stability under modest weight perturbations, reflecting strong physical signal in the data. The framework is minimal, auditable, and readily swappable with GIS-derived layers, making it suitable for early-stage planning, stakeholder dialogue, and policy screening in semiarid contexts.

DOI: http://doi.org/10.5281/zenodo.17646055

Published by: vikaspatanker

Authors: Md. Abdul Momin, M.M. Musharaf Hussain, Md. Ezharul Islam

Abstract: This research presents a comprehensive exploration of diverse microservices decomposition techniques. This research identifies the sequential steps integral to each decomposition method through a meticulous study and analysis of multiple techniques. Moreover, the paper integrates insights gleaned from a select group of experts. These experts offer valuable perspectives on software characteristics and elucidate the types of example software ideally suited for distinct decomposition types. They also validate the time and cost implications associated with each decomposition technique. Drawing from these multifaceted insights, the paper culminates in creating an algorithm. This algorithm is intricately designed based on collective knowledge and discussions surrounding software traits, such as suitability, time, and cost considerations linked to various decomposition techniques. This algorithm helps developers choose the most effective decomposition approach for microservices.

DOI: https://doi.org/10.5281/zenodo.17645806

Published by: vikaspatanker