Authors: Prof. Shraddha Tiwari, Prof. Mayanka Roy Mandal, Prof. Ankita Fouzdar

Abstract: The integration of solar photovoltaic (PV) systems into the electrical grid requires efficient power electronic interfaces to ensure reliable operation and maximum energy extraction. This study focuses on the design and performance analysis of a power electronic interface for grid-connected solar PV systems incorporating Maximum Power Point Tracking (MPPT) techniques. A DC–DC converter controlled by MPPT algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC) is employed to optimize the PV output under varying irradiance and temperature conditions. The conditioned DC power is subsequently converted into synchronized AC power through a voltage source inverter (VSI) with appropriate grid synchronization and control strategies. The proposed system enhances the efficiency, stability, and power quality of PV-grid integration while minimizing harmonic distortion and ensuring compliance with grid codes. Simulation and experimental results validate that the implementation of an optimized MPPT-based power electronic interface significantly improves energy harvesting capability and supports sustainable and reliable integration of renewable energy into the power grid.

Published by: vikaspatanker

Authors: Prof. Ankita Fouzdar, Prof. Mayanka Roy Mandal, Prof. Shraddha Tiwari

Abstract: The rapid growth of renewable energy sources (RES) such as solar and wind has created new opportunities for sustainable power generation, while also posing significant challenges due to their intermittent and unpredictable nature. Smart grids, equipped with advanced communication and control technologies, offer a promising platform for efficiently integrating these variable energy resources. This study explores the optimal integration of renewable energy into smart grids using artificial intelligence (AI)-based forecasting and optimization techniques. Machine learning and deep learning models are employed to accurately predict renewable generation and demand patterns, reducing uncertainty and enabling proactive grid management. Furthermore, advanced optimization algorithms such as genetic algorithms, particle swarm optimization, and reinforcement learning are applied to achieve optimal scheduling, load balancing, and energy storage utilization. The proposed framework enhances grid stability, minimizes energy losses, reduces reliance on fossil fuels, and ensures cost-effective and reliable power delivery. Simulation results validate the effectiveness of the AI-driven approach in improving renewable energy penetration and overall smart grid performance. This work highlights the potential of AI-enabled forecasting and optimization as key enablers for achieving sustainable, resilient, and intelligent energy systems

Published by: vikaspatanker

Authors: I.C Emeto, B.P Gbaranwi, A.A. Galadima, A.C Okoloegbo, S. Kwaghbee, E.C Ochuba

Abstract: Cloud computing has emerged as a dominant paradigm for delivering scalable, on-demand computing resources, yet efficient load balancing remains a critical challenge in modern data centers. This paper presents a novel Hybrid Bee Ant Colony (HBAC) Algorithm that synergistically combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) metaheuristics to address the inherent limitations of existing load-balancing approaches. The proposed HBAC algorithm leverages ABC's robust exploration capabilities to identify underutilized virtual machines (VMs) and ACO's pheromone-driven exploitation mechanism to optimize task allocation, thereby achieving superior performance in dynamic cloud environments. Through extensive simulations using CloudSim with Google Cluster Data traces, we demonstrate that HBAC significantly outperforms standalone ACO and ABC algorithms across key performance metrics. Experimental results show 15.7% reduction in makespan, 22.3% improvement in response time, and 18.9% better resource utilization compared to conventional approaches. The hybrid model particularly excels in maintaining balanced VM workloads (degree of imbalance reduced by 27.4%) while demonstrating exceptional scalability under varying workload conditions (from 1,000 to 10,000 tasks). The algorithm's innovative two-phase architecture – where ABC scouts first identify high-potential VMs and ACO ants then optimize task placement – effectively overcomes the slow convergence of pure ACO and the excessive exploration of pure ABC. Energy efficiency analysis reveals 13.2% reduction in power consumption, making HBAC particularly suitable for sustainable cloud operations.

DOI: http://doi.org/10.5281/zenodo.17264412

Published by: vikaspatanker

Authors: Samiksha R Hajare, Prof. S. V. Raut

Abstract: Integration of blockchain technology into healthcare systems, particularly within telehealth and telemedicine frameworks, represents a paradigmatic shift aimed at resolving persistent challenges in digital health infrastructures. These challenges primarily encompass the secure exchange of medical data, achieving interoperability between disparate health information systems, and empowering patients with greater control over their personal health information. Blockchain enabled paradigms in healthcare are presented as transformative frameworks designed to address persistent issues such as secure medical data exchange, interoperability, and patient-centric control. The theoretical discussions around blockchain in healthcare highlight its potential complexities, with influence its research and practical use. The growth of telehealth and telemedicine has changed how healthcare is delivered, allowing for remote consultations and better resource management. However, current telemedicine systems often use centralized architectures, making them vulnerable to security threats like data breaches and fraud. This paper suggests incorporating blockchain technology into telemedicine platforms to improve security, transparency, and data integrity. By using a decentralized and tamper-resistant ledger, the proposed system aims to protect patient records and increase trust among healthcare providers and patients. Key features include secure appointment scheduling and reliable management of electronic health records within a user-friendly interface. This research helps advance telemedicine by addressing key security challenges and proposing a scalable, secure platform, especially useful in areas with limited access to traditional healthcare.

DOI: http://doi.org/10.5281/zenodo.17304779

Published by: vikaspatanker

Authors: Ms. Chanchal Sachin Bedse, Ms. Vaishnavi Santosh Thorat, Mr. Niranjan Yogesh Borse, Ms. Bhagyashri Vishnu Gosavi, Mr. A. P. Patil

Abstract: E-Gram Panchayat is a digital governance platform designed to modernize rural administration by providing villagers with seamless access to essential services, including property tax payment, certificate issuance, census management, emergency assistance, and government schemes. By digitizing records and workflows, the platform reduces paperwork, minimizes dependency on intermediaries, and enhances transparency, efficiency, and accountability. This initiative contributes toward building self-reliant, digitally empowered villages, aligning with the objectives of Digital India.

DOI: https://doi.org/10.5281/zenodo.17256909

 

Published by: vikaspatanker

Authors: Nahid Almasov

Abstract: This paper outlines how mathematical modeling and artificial intelligence can be used to support scientific administration and educational policy-making. By supplementing the algorithmic capabilities of AI and analytical capabilities of quantitative models, the research facilitates better strategic planning, performance evaluation, and resource management. The proposed approach promotes data-driven, open, and responsive management practices. The article also touches on urgent issues such as model interpretability, ethics, and human-AI collaboration. It underscores the need for responsible innovation to bring about good governance and sustainable development of science and education institutions

DOI: http://doi.org/10.5281/zenodo.17256654

Published by: vikaspatanker

Authors: Chung Hyok Pak, Un Sim Ri, Se Hyon Kim

Abstract: It is the important global trend to use the unmanned production lines in order to meet the demand of customers and improve the efficiency for industrial processes. The automated storage and delivery system (ASDS) is one of the main components of the unmanned production line. It consists of many shields and several automata and complex control systems for loading and unloading, so its cost is so high. For the tradeoff of the cost and performance of cargo handling, forklift is a best alternative to the lack of financial ability enterprises/factories. In this paper, we propose a pallet detection method to allow forklifts to engage the pallet autonomously using only a monocular vision on the forklift in the harsh industrial environment. To reduce the number of features and increases the detection efficiency, we describe the pallet features by combining the Haar-like features and multi-block local binary pattern (MBLBP). 8 sets of Haar-type encoding models make the LBP feature better to encode the local structure. Adaboost classifier that use distribution information of features in training set, allows to detect pallet candidates with high accuracy and efficiency in harsh industrial environments. In particular, improved feature to maximize the margin when pattern classes are projected onto the classification hyperplane is used to enhance the discriminate ability of classifier and reduce the computational cost. The analysis of the geometric features of the pallets using integral-sum-difference (ISD) excludes the wrong candidates with high efficiency. The experimental results demonstrate that our proposed algorithm could detect the pallet with average rate of more than 98% and is robust to environmental changes.

DOI: http://doi.org/10.5281/zenodo.17255035

Published by: vikaspatanker

Authors: Shikha Goel

Abstract: Cloud computing has transformed IT service delivery by offering scalable, on-demand resources over the internet. Among its service models—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)—PaaS provides a robust platform for developing, running, and managing applications without the complexity of maintaining the infrastructure. However, PaaS introduces a unique set of security concerns due to its multi-tenancy, abstraction layers, and reliance on third-party services. This paper explores the key security issues in PaaS environments, including data isolation, insecure APIs, platform vulnerabilities, insider threats, and compliance challenges. We also discuss mitigation strategies and emerging trends to enhance PaaS security.

DOI: https://doi.org/10.5281/zenodo.17249277

 

Published by: vikaspatanker

Authors: Tarun Krishna Mahajan

Abstract: This comprehensive review examines the current state and future prospects of artificial intelligence AI) in healthcare, with particular emphasis on implementation strategies, challenges, and outcomes. The global AI healthcare market, valued at $26.57 billion in 2024, is projected to reach $187.69 billion by 2030, growing at a CAGR of 38.62% [^62]. This study analyzes AI applications across clinical decision support systems, predictive analytics, telemedicine, and population health management. Key findings indicate that 94% of healthcare providers currently use AI in some capacity, with clinical decision support systems demonstrating significant improvements in diagnostic accuracy and patient outcomes [^65]. Machine learning approaches, particularly random forest algorithms 42% of studies) and logistic regression 37% of studies), show greatest effectiveness in disease prediction and management 1 . However, implementation faces substantial barriers including data quality issues 47% of leaders cite this concern), regulatory compliance challenges 39% , and workflow integration difficulties [^73]. The review presents the HealthWise ecosystem as a case study of comprehensive AI integration, demonstrating potential for government-scale deployment across 130 crore Aadhaar cardholders in India. Privacy and security considerations under HIPAA and GDPR regulations require careful attention, with end-to-end encryption and privacy-by-design approaches being essential for compliance [^82]. This analysis concludes that successful AI implementation requires integrated approaches combining technological innovation, regulatory compliance, stakeholder engagement, and sustainable business models to realize the transformative potential of AI in healthcare delivery.

Published by: vikaspatanker

Authors: Manish Kumar, Ishan Sethi2

Abstract: Distributed Generators (DG) embody a multi-source microgrid amalgamated within a unified framework. These DGs are meticulously designed to calibrate voltage, current, and frequency in accordance with the load terminal’s observed power demand. Constructing an optimal control paradigm for these systems amplifies their functional efficacy. This study simulates a DG control architecture within MATLAB/Simulink, integrating photovoltaic (PV) arrays, a proton-exchange membrane fuel cell (PEMFC), and an ultra-capacitor to ensure a steady and dependable output for the grid. The PV component within this configuration utilizes a Maximum Power Point Tracking (MPPT) mechanism, which optimizes power transmission to the grid. To address PV’s inherent variability, an ultra-capacitor and PEMFC are employed, ensuring stable output. Here, the ultra-capacitor counterbalances the PEMFC’s thermodynamic fluctuations, enhancing reliability. A power-electronics-based interfacing circuit, paired with advanced control configurations, upholds power quality by regulating the grid's voltage and frequency within permissible thresholds.

DOI: http://doi.org/10.5281/zenodo.17248263

Published by: vikaspatanker