Authors: Mrs.V.Maria Subaitha, Dr. R. Vijayalakshmi

Abstract: The fishing industry in Rameshwaram, a coastal town in Tamil Nadu, India, is facing significant challenges, including declining fish stocks, rising operational costs, and regulatory restrictions. This study aims to identify and analyze the existing livelihood options available to the fishermen community in Rameshwaram, with a focus on understanding the socio-economic implications of these options. A mixed-methods approach was employed, combining surveys, interviews, and focus group discussions with fishermen and other stakeholders. The study found that fishermen in Rameshwaram have diversified their livelihood options beyond traditional fishing, including fish processing and marketing, tourism-related activities, and alternative livelihoods such as agriculture and small-scale industries. However, these options are often characterized by low incomes, limited job security, and inadequate social protection. The study highlights the need for targeted interventions to promote sustainable livelihoods for fishermen in Rameshwaram, including vocational training, credit facilities, and social protection programs. The findings of this study have important implications for policymakers, development practitioners, and researchers working on livelihood promotion and poverty reduction initiatives in coastal communities.

 

 

Published by: vikaspatanker

Authors: Mr. K.Karthick, Pradeepkumar K, Prathap P, Surendar S

Abstract: Efficient waste collection plays a crucial role in maintaining clean urban environments while reducing operational costs. Traditional garbage collection systems follow fixed schedules, which often lead to unnecessary trips to halfempty bins or delayed pickups of overflowing bins. This project proposes a Smart Waste Monitoring and Collection System that uses ultrasonic sensors to continuously measure the fill level of garbage bins and transmit the data to a cloud platform. The collected data is visualized on a webbased dashboard that enables administrators to monitor the status of each bin in real time and assign optimized routes to garbage collection vehicles. The system includes an intelligent route optimization module that prioritizes bins requiring urgent attention, reducing fuel consumption and travel time. A key enhancement in this project is the integration of a predictive overflow feature. By analyzing historical filllevel patterns, the system forecasts when a bin is likely to reach its capacity. This prediction enables proactive scheduling of collection before overflow occurs, which improves cleanliness and resource utilization. The proposed solution enhances overall waste management efficiency through datadriven decision making. The system is scalable, costeffective, and suitable for implementation in smart city initiatives.

DOI: https://doi.org/10.5281/zenodo.18832421

 

Published by: vikaspatanker

Authors: Priyanshu Singh, Priyanshu Soni, Manmohan Singh Yadav

Abstract: One of the significant resources used in agriculture is water whose availability is extremely low. Most people use traditional irrigation techniques, which are manual, and these techniques tend to cause wastage of water. The current paper has introduced a smart irrigation system, which is founded on soil moisture detection to enhance water management. The type of system employed in the specified project in this paper is based on a soil moisture sensor and an ultrasonic sensor that are linked to an Arduino UNO microcontroller. It constantly measures soil conditions and regulates automatically a water pump with a relay module. When the soil gets dry the pump is switched ON and when the moisture potential is achieved accordingly the pump is switched OFF. The system also offers real time updates to the farmers via the mobile notification and also show the information on an LCD screen. The solution saves water, less human labour, and enhances crop output. It is also affordable, simple to operate and can be applied in small and medium-scale farmers hence is an effective solution to current day farming.

 

 

Published by: vikaspatanker

Authors: Priyanshu Singh, Priyanshu Soni, Manmohan Singh Yadav

Abstract: One of the significant resources used in agriculture is water whose availability is extremely low. Most people use traditional irrigation techniques, which are manual, and these techniques tend to cause wastage of water. The current paper has introduced a smart irrigation system, which is founded on soil moisture detection to enhance water management. The type of system employed in the specified project in this paper is based on a soil moisture sensor and an ultrasonic sensor that are linked to an Arduino UNO microcontroller. It constantly measures soil conditions and regulates automatically a water pump with a relay module. When the soil gets dry the pump is switched ON and when the moisture potential is achieved accordingly the pump is switched OFF. The system also offers real time updates to the farmers via the mobile notification and also show the information on an LCD screen. The solution saves water, less human labour, and enhances crop output. It is also affordable, simple to operate and can be applied in small and medium-scale farmers hence is an effective solution to current day farming.

 

 

Published by: vikaspatanker

Authors: R.B.Dhayanandhan, N.Naresh, Mrs.A.Gowri/Ap

Abstract: The Smart City Information Portal is a centralized backend application developed using Spring Boot and MongoDB to manage smart city information efficiently. It provides RESTful APIs for handling user accounts, public services such as hospitals and schools, and city administrative data. The system includes a complaint management and resolution module that allows citizens to register complaints, track their status, and receive updates from city authorities, improving transparency and participation. Secure access is ensured through a role-based access control system with roles such as regular users, city administrators, and super administrators. MongoDB supports scalable and flexible data storage, while Spring Boot ensures a secure, modular, and maintainable backend architecture. The system is extensible and can be integrated with web or mobile front-end applications, supporting digital governance and improved public service delivery in smart cities. The application reduces manual effort by automating administrative workflows and ensures consistent data handling across services. It also provides a reliable foundation for future enhancements and smart city integrations.

DOI: https://doi.org/10.5281/zenodo.18832025

 

Published by: vikaspatanker

Authors: Thenmozhi P, Abarna M, Mahalakshmi D, Malini S

Abstract: Hybrid and nearshore outsourcing paradigms are becoming more popular in order to strike a balance between cost-effectiveness availability of talent and flexibility in operations nevertheless the problem of time- zone lack can affect geographically distributed teams and some of the issues include an uneven distribution of workload and infrequent monitoring of performance on the team the traditional project management tools use a static method of coordination and are not smart in terms of decision making in this project a smart platform to manage nearshore and hybrid outsourcing teams with an agentic ai based multi-agent architecture is introduced the platform automatically breaks down project goals into tasks and allocates them based on the knowledge availability time-zone coverage and historical outcomes specialized ai agents are involved in the organization of tasks time management forecasting performance and assessing risks the system developed based on an event-driven architecture with real time synchronization and continuous learning provides better accuracy in task allocation early risk identification and productivity in a distributed outsourcing setting.

 

 

Published by: vikaspatanker

Authors: Abhinav Pandey, CO. Dr. Preeti Devi

Abstract: This research report provides an exhaustive analysis ofi the intersection between digital governance and financial transparency within the municipal administrations ofi the BRICS nations—Brazil, Russia, India, China, and South Afirica. Utilizing a robust comparative firamework, the study evaluates how digital platforms, legal mandates, and institutional capacity influence the disclosure ofi fiscal infiormation to the public. The findings demonstrate a complex landscape: while national-level digital maturity is high across the bloc (evidenced by Group A and B rankings in the World Bank’s GovTech Maturity Index 2025), the actual translation into municipal transparency is hindered by over-centralization in Brazil, restricted access in Russia, localized “refiorm islands” in India, selective disclosure in China, and severe capacity constraints in South Afirica. Through an examination ofi the Open Budget Survey 2023 data, the report identifies that while transparency has increased globally by 24% since 2008, significant gaps remain in public participation and legislative oversight. Recommendations fiocus on decentralizing digital implementation, institutionalizing public engagement modules, and bridging the skill gap at the local level to ensure that technological advancements yield tangible improvements in fiscal accountability.

DOI: https://doi.org/10.5281/zenodo.18835347

Published by: vikaspatanker

Authors: N. Gokul Krishnan, M. Gokulnath, S.Manoj, Mrs.P.G.Gayathri

Abstract: In modern manufacturing, moving from an Engineering Bill of Materials (eBOM) to a Manufacturing Bill of Materials (mBOM) is still a manual, slow, and error-prone task. This problem often results in data inconsistencies, production delays, and higher manufacturing costs. To address these issues, we propose an AI-powered BOM Converter that automatically converts eBOM into improved mBOM for production workflows. The system uses a mix of machine learning and rule-based logic to examine eBOM structures, identify component connections, and produce an accurate mBOM, complete with manufacturing details like process steps, work centers, tooling, and procurement information. It integrates with existing ERP/PLM systems to ensure smooth data exchange and real-time updates with production planning. By automating the conversion from eBOM to mBOM, this system reduces manual labor, improves data consistency, cuts conversion time, and lowers operational costs. This intelligent converter seeks to transform the digital manufacturing workflow, allowing for quicker product launches and better overall production efficiency.

DOI: https://doi.org/10.5281/zenodo.18831453

 

Published by: vikaspatanker

Authors: Punit Chauhan, Aakash Chouhan, Sunny Maurya, Siddhesh Mundhe, Prof. Shilpa Doke

Abstract: In today's highly competitive job market, candidates often struggle to optimize their resumes for Applicant Tracking Systems (ATS) and lack access to realistic interview preparation environments. This paper presents ResuMentor, a full-stack, AI-driven web platform designed to bridge this gap by providing intelligent resume analysis and real-time mock interview simulation. The system accepts user-uploaded resumes in PDF or DOCX format alongside a specified job role or description, and leverages the OpenAI GPT-4o API via Spring AI to generate ATS compatibility scores, keyword gap analysis, and actionable improvement suggestions tailored to the target job profile. For interview preparation, ResuMentor deploys an AI voice agent that conducts a structured, 30-minute mock interview session, dynamically generating questions ranging from beginner to advanced level based on the parsed resume content. The platform employs the Web Speech API for real-time speech-to-text transcription, providing a live transcript visible to the user during the session. Post-session, a detailed feedback report evaluates the clarity, conciseness, and relevance of the candidate's responses with specific examples drawn from the transcript. The backend is developed using Java Spring Boot 3.3 with Spring Security and OAuth2 for Google-authenticated login, MySQL as the relational database, and Apache Tika for resume parsing. The frontend is built with plain HTML, CSS, and JavaScript, featuring a responsive dark/light theme toggle. A personalized dashboard tracks historical ATS scores and interview performance trends using Chart.js visualizations, enabling users to monitor their growth over time. ResuMentor demonstrates that integrating large language models into career development tools can significantly improve candidate preparedness and resume quality.

 

 

Published by: vikaspatanker

Authors: Victor Petrov, Kenji Nakamura, Thomas Bauer, Elena Garcia, Ananya Kulkarni

Abstract: Healthcare systems operate in highly dynamic environments where patient demand, workforce availability, and clinical resource utilization fluctuate continuously, creating significant challenges for effective talent coordination and resource planning. Traditional workforce management approaches within enterprise resource planning (ERP) systems often rely on historical reporting and static scheduling mechanisms that struggle to respond to real-time operational changes. The growing adoption of Internet of Things (IoT)–enabled medical devices and hospital telemetry infrastructure has created opportunities to capture continuous streams of operational data across healthcare environments. This study proposes a real-time healthcare talent orchestration framework that integrates IoT-driven telemetry, scalable big data pipelines, and artificial intelligence–based forecasting models within enterprise ERP architectures. Telemetry data generated from clinical monitoring systems, hospital infrastructure sensors, and workforce management platforms are processed through distributed big data pipelines capable of handling high-velocity operational information. Machine learning algorithms analyze these data streams to forecast patient inflow, anticipate staffing requirements, and identify potential operational bottlenecks before they impact service delivery. By embedding predictive insights directly into ERP-driven workforce coordination systems, healthcare organizations can dynamically adjust staffing allocations, optimize resource utilization, and support proactive decision-making. The proposed approach demonstrates how combining IoT telemetry, big data engineering, and AI-based forecasting can significantly improve workforce agility, operational efficiency, and service continuity in modern healthcare environments.

DOI: https://doi.org/10.5281/zenodo.18813979

 

Published by: vikaspatanker