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International Conference on Innovation in Smart and Sustainable Infrastructure, ISSI 2022

作者： Soni, Sweta Patel, Vinodkumar dixit, Sudhanshu Shantilal Shah Engineering College Bhavnagar India L.D. College of Engineering Ahmedabad India

ISBN: (纸本)9789819739936

In India, groundwater is a primary source of freshwater and is used mainly for agriculture and irrigation purposes. Approximately 80% of the population is dependent on groundwater for household and agricultural purposes in India. The pace of urbanization and industrialization in Gandhinagar and Mehsana has been accelerated since the capital of Gujarat state was formed;hence, a study is carried out to identify the status of groundwater quality in Gandhinagar, Kadi, Kalol, and Mehsana talukas of Gujarat on the basis of 5-year data from 2017 to 2022. In this study, MOdFlOW and MT3dMS software are used to identify contaminated groundwater status in particular study areas. MOdFlOW software helped in simulating groundwater quality under various hydrogeological conditions. After the initial simulation, the area was assigned to MT3dMS, a three-dimensional multi-species solute transport model where arsenic and fluoride concentrated chemicals are figured out above the permissible limit authorized by WHO and BIS for drinking purposes. The analysis showed the concentrations of arsenic and fluoride in unconfined and confined aquifers, and it was anticipated that the study area's groundwater quality would be controlled as a consequence. Groundwater quality simulation will assist stakeholders, government agencies, and users in determining the current state of groundwater quality for future consideration. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2024.

关键词： Quality control

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Integrating deep learning and Meta-Heuristics for Healthcare: A Survey 6

Integrating Deep Learning and Meta-Heuristics for Healthcare...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Pathmapriya, K. Joe Prathap, P.M. Department of Computer Science and Engineering R.M.D Engineering College Kavaraipettai India

ISBN: (纸本)9798331522667

data mining is used in healthcare organizations for surveillance, diagnosis, and prognosis. According to Health Catalyst, deep learning (dl) is the game-changing technology that will revolutionize healthcare. deep learning algorithms are far more successful than regular computing algorithms when used in the healthcare industry. deep learning models do, however, have several drawbacks that impact their overall performance. These include choosing proper model parameter values, choosing the best architecture and feature representation, and figuring out the ideal weight and bias values. The study has brought up a number of concerns about the top deep learning models for a variety of illness datasets and how to solve the issue by improving feature selection for improved performance. Therefore, the present level of current practice in this subject may be further optimized by deep learning models that include computer concepts inspired by biology. New recently, deep learning models have gained attention in the medical field. To improve algorithm visualization, a dataset compiled from the surveyed research publications is compared. An improved description of upcoming work in sustainable healthcare sectors is given by this survey. Better and quicker results will be obtained by combining dl approaches with meta-heuristic algorithms. © 2025 IEEE.

关键词： Electronic health record

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Person retrieval in surveillance videos using attribute recognition

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 291-303页

作者： Galiyawala, Hiren Raval, Mehul S. Patel, Meet School of Engineering and Applied Science Ahmedabad University Gujarat Ahmedabad380009 India L. D. College of Engineering Gujarat Ahmedabad380015 India

In person attribute recognition (PAR), an individual is described by his or her appearance. PAR-based person retrieval is a cross-modal problem where the input is a textual description of the person’s appearance and the output is an image of the person. The paper introduces PAR model development by merging a large-scale RAP dataset with the person retrieval benchmark dataset of AVSS 2018 challenge II. It uses a single deep network to detect a person’s attributes. The proposed approach uses five attributes;age, upper body (uBody) clothing color, uBody clothing type, lower body (lBody) clothing color, and lBody clothing type. Mask R-CNN is used for person detection, and the approach weighs each attribute to generate a ranking score for every detected person. Unlike the existing approaches, the proposed method uses a single deep network and fewer attributes to achieve state-of-the-art average intersection-of-union (IoU) of 66.7%, retrieval with IoU ≥ 0.4 is 85.6%, and an average true positive rate (TPR) of 85.30%. It is better by 10.80% average IoU, 5.94% IoU ≥ 0.4, and 3.85% TPR than the existing state-of-the-art person retrieval using attributes recognition. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： large dataset

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Predicting Solar Energy Generation with Machine learning based on AQI and Weather Features 2

Predicting Solar Energy Generation with Machine Learning bas...

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2nd International Workshop on Artificial Intelligence: Empowering Sustainable development, AISd 2024

作者： Shah, Arjun Viswanath, Varun Gandhi, Kashish Patil, Nilesh Synapse Computer Engineering D.J. Sanghvi College of Engineering Mumbai India

This paper addresses the pressing need for an accurate solar energy prediction model, which is crucial for efficient grid integration. We explore the influence of the Air Quality Index and weather features on solar energy generation, employing advanced Machine learning and deep learning techniques. Our methodology uses time series modeling and makes novel use of power transform normalization and zero-inflated modeling. Various Machine learning algorithms and Conv2d long Short-Term Memory model based deep learning models are applied to these transformations for precise predictions. Results underscore the effectiveness of our approach, demonstrating enhanced prediction accuracy with Air Quality Index and weather features. We achieved a 0.9691 R2 Score, 0.18 MAE, 0.10 RMSE with Conv2d long Short-Term Memory model, showcasing the power transform technique’s innovation in enhancing time series forecasting for solar energy generation. Such results help our research contribute valuable insights to the synergy between Air Quality Index, weather features, and deep learning techniques for solar energy prediction. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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disaster Management Systems: Utilizing YOlOv9 for Precise Monitoring of River Flood Flow levels Using Video Surveillance

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SN computer Science 2025年第3期6卷 1-14页

作者： Shankar, G. Geetha, M. Kalaiselvi Ezhumalai, P. Department of Computer Science and Engineering FEAT Annamalai University Chidambaram 608002 India Department of Computer Science and Engineering R.M.D. Engineering College Chennai 601206 India

Flood disasters pose significant threats to human lives and infrastructure, necessitating advanced methods for the timely and accurate monitoring of water levels in rivers. This study introduces an innovative approach utilizing the recent YOlOv9 (You Only look Once, version 9) deep learning model to detect and monitor river water flow levels effectively. By leveraging YOlOv9’s robust object detection capabilities, the proposed system achieves precise segmentation and real-time analysis of river water regions. The method not only identifies river water areas with high accuracy but also quantifies flow levels, providing critical data for flood prediction and management. Comprehensive testing was conducted using diverse datasets representing various river conditions and flow scenarios. The results demonstrate that YOlOv9 significantly outperforms traditional methods in terms of detection speed and accuracy, making it a valuable tool for enhancing flood disaster response and management strategies. This work contributes to the development of more resilient and proactive flood disaster management systems through the integration of state-of-the-art machine learning techniques, offering significant improvements in predictive capabilities and response effectiveness. The proposed system was trained and evaluated using real-time river flood datasets, achieving a notable accuracy rate of 98%, a mean average precision (mAP) of 97.5%, precision of 98%, and recall of 98% in predicting flood level characteristics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte ltd. 2025.

关键词： deep learning Flood disaster management Machine learning Object detection River water flow detection Water level segmentation YOlOv9

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An Approach for Real Time Implementation of Charging of Shunt Reactor Using Point on Wave Technique with Variable Pre-Insertion Resistor

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Journal of The Institution of Engineers (India): Series B 2025年 1-12页

作者： Shivani, S.N. Vyas, Bhargav Bhatt, K.A. Bhavsar, Meet I. Mistry, Vrushank M. Gujarat Technological University Gujarat Ahmedabad India Department of Electrical Engineering Lukhdhirji Engineering College Morbi India Department of Electrical Engineering L. D. College of Engineering Gujarat Ahmedabad India H. V. Cell Torrent Power Ltd Ahmedabad India

Nowadays, rapid pace growth in the field of solid-state devices and smart grid technologies, it is possible to minimize the Switching Transients (STs) very effectively. Recently, Point on Wave device (PoWd) is used to limit the level of Charging Current (CC) and Asymmetrical dC Component (AdCC) during charging of Shunt Reactor (SR). For successful implementation of the PoW Switching (PoWS) technique, it is essential to minimize the effect of Operating Time Variations (OTV) which is obtained using union of Pre-Insertion Resistor (PIR) and PoWd. In this paper, a novel techniqueis suggested which minimizes the energy consumption (EC) in PIR using variable PIR along with PoWd. Variable PIR is framed using Solid-state Switches (SS). Here, suggested algorithm proposes utilization of variable PIR instead of fixed PIR to minimize the magnitude of CC and AdCC, and EC in PIR during charging of SR. The suggested technique is tested by precise modeling of real-time power system. The comparison of the suggested algorithm with past published paper [1] suggests that the suggested algorithm minimizes the level of CC, AdCC, and EC in PIR during charging of SR. At last, suggested algorithm is tested on hardware model. The validation of the suggested algorithm has been proved using results obtained from hardware model,and performing sensitivity analysis such as testing using different strength of sources and considering effect of various system capacitances is also presented. © The Institution of Engineers (India) 2025.

关键词： Sensitivity analysis

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Parameters for cladding using plasma transfer arc welding –A critical

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Materials Today: Proceedings 2023年 77卷 614-618页

作者： Momin, Altafhussain G. Khatri, Bharatlal C. chaudhari, Mrunalkumar V.Shah, U. Valaki, Janak Gujarat Ahmedabad 380015 India Mechanical Engineering Government Engineering College Gujarat Modasa India Mechanical Engineering L.D College of Engineering Gujarat Ahmedabad 380015 India Mechanical Engineering Government Engineering College Gujarat Bhavnagar India

Arc welding is a type of welding in which there is an electric arc formed joining the base material and the electrode, as well as metals are melted at the welding spot with the help of a welding power source. Arc welding is commonly used because of its low capital and operational costs. due to cost concerns, it is typically not viable to fabricate components from solid high alloyed materials. As a result, non-alloyed or low-alloy base materials must be encased with high-alloy cladding. In high-tech welding technique processes, Strip electrodes are used to apply welded deposits over large surface *** most important contribution of this article is to optimize plasma arc welding process parameters for dissimilar metal welding and other quality factors such as bead shape, microstructure, hardness, ferrite measurement, and tensile *** to its exceptional features, low dispersion, and high coatings efficacy, Welding with plasma transmitted arc (PTAW) is a broadly applied overlay coating method. The impacts of transferred welding current, welding travel speed, powder flow rate, welding oscillation speed, and stand-off distance on weld bead form parameters such as width of deposition and reinforcing are given and examined using experimental data and a developed model. Reduced current (100–120 A), intermediate travel speed (120–140 mm/min), intermediate powder feed rate (12–14 g/min), oscillation speed (450–550 mm/min), and stand-off distance (6–8 mm) would result in superior deposition characteristics with less distortion, residual stresses, and no surface defects. © 2022

关键词： Welds

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Architecture Based Classification for Intrusion detection System Using Artificial Intelligence and Machine learning Classifiers 7th

Architecture Based Classification for Intrusion Detection Sy...

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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Gondalia, Archana Shah, Apurva Computer Engineering Department L.D. College of Engineering Ahmedabad India Computer Science and Engineering Department The Maharaja Sayajirao University of Baroda Vadodara India

ISBN: (纸本)9789819940394

The study of intrusion detection systems (IdSs) has drawn a lot of interest pertaining to computer science due to the increasing network load and security concern. In addition to arbitrary intrusion categories, current IdSs also require a lot of processing capacity. despite the fact that there is a wealth of literature on IdS issues, we aim to provide a more detailed picture for a thorough review. We provide the taxonomy to delineate contemporary IdSs based on the large survey and sophisticated arrangement. Also, the architecture we provided in the material help the reader quickly understands the big picture of IdSs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Intrusion detection

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Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge Computing

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Tsinghua Science and Technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui dhelim liming Chen Huansheng Ning luigi Atzori Tahar Kechadi Information Engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland School of Computing Ulster UniversityBelfastBT151EDUK School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic Engineering University of CagliariSardinia 09124Italy School of Computer Science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3d physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing

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EPdTNet + -EM: Advanced Transfer learning and SubNet Architecture for Medical Image diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel deep Transfer SubNet+-based Explainable Model (EPdTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer learning (En-ETl) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETl accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer Science+Business Media, llC, part of Springer Nature 2025.

关键词： Transfer learning

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