Category Archives: Uncategorized

Authors: Dr. Jermiah Anand Jupalli, Dr. Kiran Koduru

Abstract: The rapid evolution of artificial intelligence (AI) and digital transformation has significantly influenced the domain of human resource management (HRM), enabling the development of intelligent and data-driven systems. This paper proposes an Intelligent Human Resource Management System (IHRMS) framework designed to enhance organizational efficiency and decision-making through AI-driven analytics, automation, and predictive modeling. The study integrates multiple HR functions, including recruitment, performance evaluation, employee engagement, and attrition prediction, into a unified intelligent system. A synthetic dataset is utilized to evaluate the performance of the proposed model, and comparative analysis is conducted with traditional machine learning approaches such as Support Vector Machine and Decision Tree. The results demonstrate that the proposed IHRMS model achieves higher accuracy, improved prediction consistency, and better decision support capabilities. Furthermore, the study addresses ethical considerations such as fairness, transparency, and data privacy in AI-based HR systems. The findings indicate that intelligent HR systems can significantly contribute to organizational excellence by improving workforce management, enhancing employee experience, and enabling strategic decision-making.

DOI: http://doi.org/10.5281/zenodo.20179023

Published by: vikaspatanker

Authors: Ms.Pharande Harshada Sudhir, Dr.Dhaigude.N.B

Abstract: In working environment, the toxic gas leakage accidents are the main reason for workers health and also causes death. The Toxic gas can be detected and monitored by recent technologies using Internet of things. This project is mainly used to reduce the industrial accidents and hazardous. This process is monitored by Internet of things. Arduino Micro controller board is connected with gas sensor, Flame sensor and Temperature senor. The alert message is display by LCD through Arduino. The alert signal arises when the gas level increases above the normal gas level. This can be done by internet receiver channel. The sensor will receive the information about the gas level and it is stored in internet. This will used for analyzing and processing the safety regulations in industrial environment.

DOI: https://doi.org/10.5281/zenodo.20177739

Published by: vikaspatanker

Authors: Vivek Mishra, Om Prakash Sondhiya

Abstract: Rice husk is a common lignocellulosic agricultural by-product produced in huge amounts across the world, with nearly 150 million tons generated every year. This work examines the preparation and assessment of bioethanol obtained from rice husk waste as an eco-friendly second-generation biofuel. The rice husk was collected, dried, powdered, and treated with 4% NaOH at 90°C for 2 h, followed by steam explosion at 121°C for 30 min to remove lignin and hemicellulose components. Enzymatic saccharification was carried out using cellulase (30 FPU/g) and xylanase (10 FPU/g) at pH 5.0 and 50°C for 72 h, producing 68.4 g/L reducing sugars. Fermentation was performed with Saccharomyces cerevisiae (MTCC 178) at 32°C for 96 h and resulted in 32.6 g/L bioethanol with 95.2% fermentation efficiency. The produced bioethanol was purified through double distillation and molecular sieve dehydration to reach 99.5% purity, and the product was analysed using GC-MS, FTIR, and NMR techniques. The physicochemical parameters, including density (789 kg/m³), calorific value (26.8 MJ/kg), and octane number (108), matched ASTM D4806 requirements. Engine testing on a 4-stroke, single-cylinder SI engine (5.2 kW, 1500 rpm) with E10, E20, E50, and E85 blends revealed that E20 decreased CO emissions by 38% and HC emissions by 32% relative to gasoline, with only a 3.5% decline in brake thermal efficiency. CFD analysis using ANSYS Fluent confirmed the experimental findings with an error lower than 6%. The results demonstrate that rice husk can serve as an effective feedstock for large-scale bioethanol manufacturing while supporting waste utilisation and renewable energy production.

Published by: vikaspatanker

Authors: Shubhangi Baghel, Om Prakash Sondhiya

Abstract: Bioethanol has emerged as one of the most promising renewable energy sources for reducing greenhouse gas emissions and decreasing dependence on fossil fuels. However, conventional bioethanol production systems face significant challenges, including low conversion efficiency, process instability, high operational costs, and limitations in feedstock utilization. Recent developments in artificial intelligence (AI) and machine learning (ML) have introduced advanced computational approaches capable of transforming industrial bioethanol production through predictive analytics, process automation, and intelligent optimization. This paper examines the role of AI and ML technologies in enhancing fermentation efficiency, optimizing biomass pretreatment, predicting ethanol yield, and improving overall sustainability in bioethanol production systems. The study also discusses key machine learning algorithms, including artificial neural networks, support vector machines, random forests, and deep learning frameworks, alongside their industrial applications. Furthermore, the paper evaluates challenges associated with data quality, computational complexity, scalability, and ethical considerations. The findings indicate that AI-driven systems significantly improve process accuracy, reduce waste generation, and enhance economic feasibility. Future research directions involving digital twins, autonomous biorefineries, and explainable AI are also explored.

Published by: vikaspatanker

Authors: Sahil Arun Bodke, Devika Deepak More, Samruddhi Mahendra Pansare, Prof. P. A. Mande, Prof. Bangar A.P., Prof. Bhosale S.B.

Abstract: Industrial workplaces continue to pose significant hazards to workers, including toxic gas exposure, thermal stress, mechanical injuries, and fatigue-related accidents. Conventional safety systems have largely remained reactive, responding to incidents after they occur rather than preventing them proactively. The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and advanced wearable sensor technologies has opened transformative opportunities for proactive occupational safety. This paper presents a comprehensive literature review of existing research on AI-integrated industrial safety wearable devices, covering sensor technologies, machine learning algorithms, edge computing strategies, cloud-based analytics, and alert mechanisms. We synthesize findings from over 25 peer-reviewed studies published in IEEE, Springer, and Web of Science indexed journals between 2019 and 2025. Key research gaps identified include the lack of multi-modal sensor fusion with real-time edge AI, insufficient datasets for industrial fatigue prediction, limited ergonomic wearable designs for harsh environments, and the absence of Explainable AI (XAI) in safety-critical decision making. Based on the review, we propose an integrated four-layer system architecture combining physiological and environmental sensing, edge-level AI inference, MQTT-based cloud communication, and a multi-level alert mechanism.

DOI: https://doi.org/10.5281/zenodo.20176697

Published by: vikaspatanker

Authors: Swati Singh

Abstract: The River Ganga, one of India's most significant rivers, plays a major role in domestic, agricultural, industrial, ecological, and religious activities in Northern India. However, over the past few decades, its water quality has significantly declined due to increasing urbanization, industrial discharge, untreated sewage, and agricultural runoff. This study performs a temporal and seasonal assessment of turbidity and chlorophyll-a between 2019 and 2024 using Sentinel-2 satellite imagery and Google Earth Engine (GEE). The research covers the entire stretch of the Ganga from Uttarakhand to West Bengal and analyzes four seasons (pre-monsoon, monsoon, post-monsoon, and winter). Sentinel-2 imagery was processed using cloud-based geospatial analysis techniques. Results show that turbidity increases during the monsoon due to sediment transport, while chlorophyll-a is found to be higher in urban areas like Kanpur and Varanasi due to nutrient enrichment. This study proves that remote sensing techniques are an effective and cost-effective tool for large-scale river management.

DOI: https://doi.org/10.5281/zenodo.20176566

Published by: vikaspatanker

Authors: Samriddha Sharma, Om Prakash Sondhiya

Abstract: The increasing environmental and economic concerns associated with fossil-fuel dependency have intensified global interest in renewable transportation fuels. Among alternative biofuels, bioethanol has emerged as one of the most commercially viable and widely adopted options because it can be produced from renewable biomass resources and integrated into existing fuel infrastructures. This study presents a comparative process-design assessment of a compact bioethanol production system utilizing three abundant lignocellulosic agricultural residues: rice straw, sugarcane bagasse, and corn stover. A literature-informed process model was developed for a small-scale educational bioethanol unit comprising feedstock preparation, dilute-acid pretreatment, enzymatic hydrolysis, yeast fermentation, and reflux-assisted distillation. The investigation evaluates the influence of biomass composition on fermentable sugar recovery, ethanol yield, process efficiency, and energy demand. The modeled analysis indicates that sugarcane bagasse demonstrates the most favorable conversion performance under the selected operating assumptions, yielding approximately 74 g/L fermentable sugars and 34.5 g/L ethanol prior to separation. Corn stover exhibited intermediate performance, whereas rice straw produced comparatively lower ethanol concentrations because of its elevated ash and silica content, which reduce carbohydrate accessibility during pretreatment. The results further reveal that pretreatment and distillation account for the majority of the process energy requirement, highlighting the importance of heat integration, solids management, and process optimization in improving system efficiency. The study concludes that a modular small-scale bioethanol system can serve as an effective educational and research platform for demonstrating biomass-to-fuel conversion technologies. Furthermore, transparent presentation of modeled assumptions and calculation procedures strengthens the academic reliability of design-stage biofuel studies intended for instructional and comparative analysis.

Published by: vikaspatanker

Authors: Mulay tanuja suresh, Dr.N.A.Doshi, Shaikh Aslam Amir

Abstract: Number plate recognition is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. The system can be implemented on the entrance for security control of a highly restricted area like military zones or area around top government offices e.g. Parliament, Supreme Court etc. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is then converted into grayscale. The number plate is then extracted. Then, using KNN (K- Nearest Neighbours) algorithm is used to recognize the digits and the alphabets. This data can be used to find vehicle’s owner, place of registration, address, etc. The system is implemented using Python, and its performance is tested on real images. It is observed from the experiment that the developed system successfully detects and recognize the vehicle number plate on real images.

DOI: https://doi.org/10.5281/zenodo.20176415

Published by: vikaspatanker

Authors: Dr. Jonathan Clarke, Emily Dawson, Michael Bennett, Sophie Reynolds, Daniel Foster, Jeji Krishnan

Abstract: Enterprise collaboration platforms such as Zimbra operate in highly distributed and mission-critical environments where system stability and rapid incident resolution are essential for uninterrupted communication. Traditional escalation mechanisms often rely on generic operational workflows that lack alignment with underlying system architecture, leading to delays in diagnosis and resolution of critical issues. This paper proposes an architecture-led escalation engineering framework that integrates deep architectural knowledge with incident management processes to improve system reliability and operational efficiency. The approach emphasizes backend ownership, where each core component—such as Mail Transfer Agents (MTA), mailbox servers, LDAP directory services, and proxy layers—is assigned to dedicated experts responsible for performance, troubleshooting, and continuous optimization. Through evidence-based analysis of real-world Zimbra deployments, the study demonstrates how mapping system architecture to escalation paths enables faster root cause identification, reduces mean time to resolution (MTTR), and enhances cross-team collaboration. The framework also incorporates proactive monitoring, architecture-aware diagnostics, and structured escalation workflows to minimize downtime and prevent recurring incidents. Results indicate that organizations adopting this model achieve improved system stability, stronger accountability, and more efficient incident handling. This research contributes a scalable and practical strategy for stabilizing enterprise collaboration platforms by bridging the gap between system design and operational response.

DOI: http://doi.org/10.5281/zenodo.20157620

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Authors: Dr. Jonathan Reed, Emily Carter, Michael Thompson, Dr. Sarah Williams, David Anderson, Jeji Krishnan

Abstract: Modern enterprise email platforms operate at global scale, where even minor changes can introduce widespread failures if correctness and backward compatibility are not rigorously maintained. This paper presents a structured framework for designing and validating safe changes in globally deployed email systems, with a focus on minimizing risk while enabling continuous evolution. The proposed approach integrates correctness-driven engineering practices, backward compatibility validation mechanisms, and a governance model centered on trusted reviewer roles. Through evidence mapping of real-world operational scenarios, the study highlights how architecture-aware validation, staged rollouts, and reviewer-guided decision-making significantly reduce incident rates and improve system resilience. The framework emphasizes proactive testing strategies, dependency impact analysis, and controlled deployment pipelines to ensure seamless integration of changes across distributed environments. Results demonstrate that incorporating reviewer expertise into the change lifecycle enhances accountability, improves validation quality, and accelerates safe delivery. This research contributes a practical and scalable model for organizations seeking to balance innovation with stability in large-scale email platforms.

DOI: http://doi.org/10.5281/zenodo.20157740

Published by: vikaspatanker