Authors: Dipikaben Solanki

Abstract: Skilled labor migration has proven to be one of the major economic issues of the world in terms of the workforce distribution, productivity increase and the stability of the labor market in different countries. The trend of growing globalization has increased mobility of professionals especially since developing economies are losing their talent and the advanced economies are experiencing mounting cases of labor shortages. This paper will discuss the economic factors that drive skilled migrants with emphasis on the wage differentials, unemployment rates, and the level of skill factor that determine international migration choices. There is quantitative cross-national secondary data approach that has used comparative evidence of great labour-exporting and labour-importing economies within G20 framework. The analysis indicates that rational economic assessment plays a significant role in the determination of the migration decisions in which the skilled professionals migrate to exploit the maximum income potential, employment security and long-term productivity gains. The results show that the microeconomic instability in the country of origin plays a great role in enhancing the propensity of migration whereas technological progress and structured demand of labor draws highly skilled workers into the developed economies. The paper also indicates that the unmanaged migration also leads to workforce imbalance which further strengthens inequalities in the global labor markets. Policy implications point at the significance of bilateral labour agreements, domestic human capital investments, and controlled migration regimes that facilitate sustainable circulation of skills instead of permanent loss of talent. The study offers a comprehensive economic and policy framework that promotes a balanced mobility of workforce that will assist G20 countries to harmonize migration governance to long-term economic competitiveness and labor market sustainability.

DOI: https://doi.org/10.5281/zenodo.19250248

Published by: vikaspatanker

Authors: Dipikaben Solanki

Abstract: The world service industry has had a psychological strain on it due to economic unpredictability, technological growth and the augmented performance standards. The banking, information technology and healthcare industries are some of the high-pressure sectors where cases of burnout and high employee turnover have been reported, posing serious organisational and economic problems. This study has reviewed how strategic management of stress influences retention of employees in these industries using a comparative cross-national study. By assuming an interpretivist and an inductive philosophical approach, the research has employed secondary qualitative data in order to draw comparisons among the prevalence of stress, turnover patterns, and organisational reactions to these patterns in the various national settings. The results have shown that there is a close correlation between stress at work and turnover. It has been demonstrated that emotional exhaustion, heavy workload and lack of managerial support have decreased organisational commitment and out-of-organisation intentions. Nevertheless, organized stress management solutions such as supportive leadership behaviour and workload modification have resulted in a positive change in short-term retention. The cross-national differences have also brought out the fact that organisational design, economic stress and sectoral characteristics determine the intensity of burnout and workforce stability. The analysis has found that sustainable management of human resources should consider including mental health strategies as an organisational priority and not a response welfare intervention. Some policy implications are stress audits, inclusion of mental health performance indicators and creation of crisis responsive HR structures. Strategic stress management has hence gained prominence as an economic stability mechanism and a sustainability tool to the workforce that is in the global service industries.

DOI: http://doi.org/10.5281/zenodo.19251151

Published by: vikaspatanker

Authors: Dr. S. Sheeja, Bavani. G, Dhanish Ahamad. M

Abstract: In a modern financial world, investors are faced with various challenges such as market volatility, a large volume of financial information, and a lack of personalized investment advice. In this context, existing investment systems involve processing financial information and employing decision-making techniques. These techniques are no longer sufficient in today's changing market environment.In this paper, a new concept is introduced to develop an "AI Supported Investment Portfolio Management System." This system will help users make intelligent investment decisions using machine learning and financial analytics. In this project, financial information is used to analyze the stock market using various financial parameters such as "Compound Annual Growth Rate," "Volatility," and "Maximum Drawdown." Machine learning algorithms such as K-Means clustering are used to classify assets based on various risk levels. In this project, regression algorithms are used to predict stock price trends. In addition, a recommendation system is also incorporated in this project to make intelligent investment decisions. In this project, a SIP planner is used to analyze long-term investments. In this project, an interactive interface is developed using Streamlit to better understand financial information.The above system demonstrates the effective application of Artificial Intelligence in the field of finance and creates a data-driven and user-centric approach towards the development of the financial strategy

Published by: vikaspatanker

Authors: Dr.G.Vani,, Mr.Sakthi Vinayagam

Abstract: The rapid growth of urbanization and digital transformation has significantly influenced the way people search for rental properties. Traditional rental systems are often inefficient, involving manual communication, reliance on brokers, lack of transparency, and limited access to reliable information. These challenges create inconvenience for both tenants and property owners, resulting in delays, miscommunication, and increased costs. Smart Rental Hub is a comprehensive web-based platform designed to address these challenges by providing an efficient, transparent, and user-friendly digital solution for property rental management. The system connects property owners and tenants through a centralized interface, enabling seamless interaction and streamlined processes. Property owners can list their properties with detailed descriptions, pricing, and images, while tenants can search and filter properties based on their preferences such as location, budget, and property type. The application incorporates secure user authentication, real-time booking management, and an administrative control system to ensure smooth operation and data integrity. By leveraging modern web technologies, the platform enhances user experience, reduces manual intervention, and promotes a more organized rental ecosystem. Furthermore, the system lays the foundation for future enhancements such as artificial intelligence-based recommendations, online payment integration, and mobile application development, making it a scalable and forward-looking solution.

DOI: https://doi.org/10.5281/zenodo.19248424

 

Published by: vikaspatanker

Authors: Aliya Sianne Elijah Camporedondo, Eugene Blase, Brian Laraga, John Andrew Lopez, Eman Noel Reclusado

Abstract: The objective of this study is to develop a manually controlled surface water prototype vehicle, which is designed to assist the rescuer in search and rescue operation in detecting submerged individuals in a flooded area. The components and modules of the prototype consists of Arduino Uno R4 Wi-Fi as the main microcontroller, JSN-SR04T for water depth measurement and underwater object detection, HC-SR04 for obstacle avoidance, Neo-6M GPS module for location tracking, and the Blynk IoT for a real-time data dashboard. The system integrates tracking mechanisms within its navigation and detection components to ensure accurate data monitoring and successful retrieval on the given coordinates. Results show the effectiveness of the detection accuracy, manual navigation, and the reliability in transmitting real-time data in the IoT dashboard during flood situations. This study concludes that Tactical Intervention Device for Emergencies in Flood (TIDE) design highlights the great potential to improve search and rescue operations in post-flood situations, particularly in areas where visibility and communication access are limited.

 

 

Published by: vikaspatanker

Authors: Nimaful N Samuel, Hanyabui Augustine

Abstract: Distributed energy resources (DERs)—including distributed photovoltaics, behind-the-meter storage, flexible demand, and electrified end uses—are transforming U.S. distribution systems while exposing a persistent planning and interconnection constraint: hosting capacity. Hosting capacity is commonly defined as the amount of DER that can be accommodated without adversely impacting power quality or reliability under specified control configurations and without requiring infrastructure upgrades. Yet hosting capacity is not an immutable feeder attribute; it is strongly sensitive to analytical methods (snapshot vs. time-series; deterministic vs. probabilistic), modeling assumptions (e.g., inverter settings), data quality, and governance choices regarding what constitutes an acceptable violation or mitigation. This article provides a secondary analysis synthesizing peer-reviewed research, national laboratory reports, interconnection standards resources (IEEE 1547 family implementation guidance), and public regulatory/utility records to develop an integrated technical–economic–regulatory framework for expanding hosting capacity through complementary strategies: targeted grid reinforcement and non-wires alternatives (NWAs). Comparative case evidence from New York’s Brooklyn-Queens Demand Management program, California’s integration capacity analysis ecosystem, and Hawaii’s hosting-capacity mapping and inverter experience is used to extract transferable mechanisms and failure modes. Synthesized findings indicate that hosting capacity should be communicated as a scenario-dependent range; that advanced inverter functionality and flexible demand can expand feasible DER penetration but require validated settings, telemetry, and verification; and that integrated distribution planning linking hosting capacity analytics to locational value and benefit-cost screening improves comparability between wires and non-wires portfolios while strengthening transparency for interconnection stakeholders. (Electric Power Research Institute [EPRI], 2018; Jain et al., 2020; Narang et al., 2021).

DOI: https://doi.org/10.5281/zenodo.19235202

Published by: vikaspatanker

Authors: Dr.M.Suganthi(Ap/Cse), K.Niranjana, T.Nisha, S.Prarthana

Abstract: College food courts often struggle with long waiting queues, overcrowding during peak hours, inefficient order management, and the absence of real-time order tracking; these challenges result in increased waiting time for students and difficulty for administrators in managing multiple food orders effectively, especially during busy lunch and break hours. This paper presents a Smart Food Court Ordering and Management System, a web-based platform designed to simplify food ordering and improve food court management within a college environment. The proposed system allows students to view the food menu, which includes food name, image, price, availability status, waiting time, and quality information, and place orders through an online or offline mode. The system also displays the current food court crowd level as high, medium, or low to help students decide the best time to place their orders. An admin management module enables administrators to monitor student orders, update order status such as waiting, preparing, or ready, manage food availability, and update crowd levels through an interactive dashboard. All system data, including student login details, food menu information, order records, order status updates, and food availability, are stored in a MySQL database using phpMyAdmin within the XAMPP control panel. The system operates as a web application without requiring additional hardware and aims to improve efficiency in food ordering, reduce waiting time, and enhance the overall food court experience for both students and administrators within the campus environment.

DOI: https://doi.org/10.5281/zenodo.19235190

Published by: vikaspatanker

Authors: Kishore A, Nawfees MI, Dr. S. Thilagavathi

Abstract: Early and accurate disease prediction is a major challenge in modern healthcare systems. Delayed diagnosis often leads to higher treatment costs and lower patient survival rates. Artificial Intelligence (AI) and Machine Learning (ML) techniques are widely used to help with medical decision-making by analyzing complex healthcare datasets. However, traditional machine learning models often face issues with inefficient feature selection, poor hyperparameter tuning, and slow convergence during optimization. This is especially true when working with high-dimensional medical data. To tackle these challenges, this paper presents an AI-based disease prediction framework that uses quantum-inspired optimization techniques. This approach combines classical machine learning classifiers with optimization strategies based on quantum computing principles, such as probabilistic state representation and superposition-based search. These quantum-inspired methods allow for efficient exploration of the solution space, which leads to better feature selection and optimized model parameters. We evaluate the proposed framework using a publicly available healthcare dataset from Kaggle. We compare the performance of traditional machine learning models and quantum-inspired optimized models using accuracy, precision, recall, and F1-score metrics. The experimental results show that the quantum-inspired optimized model consistently performs better than conventional approaches. This study demonstrates that quantum-inspired optimization provides a practical and scalable solution for improving AI-driven disease prediction systems without the need for actual quantum computing hardware.

DOI: https://doi.org/10.5281/zenodo.19232740

Published by: vikaspatanker

Authors: Muneeswaran B, Shanmuga Eswari M

Abstract: This research presents an innovative hybrid machine learning framework that amalgamates density-based clustering with ensemble regression and logistic classification to improve the precision of student performance prediction. We use DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering on the StudentPerformanceFactors dataset to find hidden student behavioural phenotypes. These phenotypes are then used as engineered features for supervised learning models. An automated hyperparameter tuning system uses silhouette score maximisation to systematically test different DBSCAN settings and find the best density parameters (eps=1.0, min_samples=5) without any human input. The final cluster assignments are used in both a RandomForestRegressor to predict test scores and a Logistic Regression model to classify performance into categories. This creates a hybrid framework that captures both clear academic metrics and more subtle behavioural patterns. Experimental validation shows performance gains that are statistically significant. The hybrid RandomForest gets an MSE of 4.45 on test data that wasn't used to train it, and the hybrid Logistic Regression gets an accuracy of 82.3%. Feature importance analysis shows that Attendance (33.4%), Hours_Studied (23.9%), and Previous_Scores (9.8%) are the most important predictors. DBSCAN_Cluster also adds useful discriminative power. Five-fold cross-validation verifies model robustness (CV-MSE=4.88±0.12). This study enhances educational data mining by implementing unsupervised learning for supervised improvement, providing interpretable student groupings that uncover density-based behavioural phenotypes affecting academic performance. The proposed framework shows that it can be used in real life for early intervention systems by giving teachers useful student types based on regular academic data.

Published by: vikaspatanker

Authors: Omkar Ganesh arnikar, Siddhi Rupesh Datar, Ishwari Sanjay Karad, Kiran bapu karhe

Abstract: The IoT-based full-range audio system with gesture control is a smart audio system that allows users to control music using hand gestures without physical touch. An ESP32 microcontroller works as the main controller, while an APDS9960 gesture sensor detects hand movements such as up, down, left, right, and near to perform functions like play/pause, next track, previous track, and volume control. The audio signal is processed using a 3-way active crossover and amplified by TPA3116D2 class-D amplifiers to drive a subwoofer, midrange speaker, and tweeter, producing clear full-range sound. The system is powered using a 12-0-12 transformer and voltage regulation circuits. This project combines IoT technology, gesture-based control, and high-quality audio output to create a modern and user-friendly sound system.

DOI: https://doi.org/10.5281/zenodo.19229877

Published by: vikaspatanker