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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Kumar, Sujit Kumari, Alka Nandani, Durgesh Kumar, Manish Electrical Engineering Department Government Engineering College Banka Bihar Engineering University Science Technology and Technical Education Department Bihar Patna India Electrical Engineering Department Government Engineering College Banka Bihar Banka India Computer Science and Engineering Department Government Engineering College Banka Bihar Banka India Electrical Engineering Department Indian Institute of Technology Patna Bihar Patna India

ISBN: (纸本)9789819791118

Rolling bearings are the main components of rotating machines which are mostly damaged. Therefore, correct and quick fault diagnosis of rolling bearings is very necessary for maintenance. Nowadays, machine learning has emerged as a very effective artificial intelligence technique for fault diagnosis. The Naive Bayes classifier is one of the machine learning techniques that effectively classifies faults. In this work, intelligent fault classification of bearing faults based on Naive Bayes is proposed. The proposed model correctly classifies the different types of fault conditions of rolling bearings. The proposed model has achieved the best results and has been compared with existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Condition based maintenance

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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture 1

Optimization of Neural Networks using Swarm Intelligence Tec...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches. © 2024 IEEE.

关键词： Energy efficiency

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Design of Fuzzy Logic Control Framework for QoS Routing in MANET

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Intelligent Automation & Soft Computing 2023年第3期35卷 3479-3499页

作者： M.Vargheese S.Vanithamani D.Stalin David Ganga Rama Koteswara Rao Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Master of Computer Applications M.Kumarasamy College of EngineeringKarur639113Tamil NaduIndia Department of Information Technology Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062Tamil NaduIndia Department of Computer Science and Information Technology Koneru Lakshmaiah Education FoundationVaddeswaram522502Andhra PradeshIndia

Wireless networks with no infrastructure arise as a result of multiple wireless devices working *** Mobile Ad hoc Network(MANET)is a system for connecting independently located Mobile Nodes(MNs)via wireless links.A MANET is self-conﬁguring in telecommunications,while MN produces non-infrastructure networks that are entirely *** the MAC and routing layers of MANETs take into account issues related to Quality of Service(QoS).When culling a line of optical discernment communication,MANET can be an effective and cost-saving route cull *** maintain QoS,however,more or fewer challenges must be *** paper proposes a Fuzzy Logic Control(FLC)methodology for specifying a probabilistic QoS guaranteed for *** framework uses network node mobility to establish the probabil-istic quality of *** Logic(FL)implementations were added to Network Simulator-3(NS-3)and used with the proposed FLC framework for *** have found that for a given node’s mobility,the path’s bandwidth decreases with time,hop count,and *** is resolutely based on this fuzzy rule that the priority index for a packet is ***,by avoiding sending pack-ets(PKT)out of source networks when there are no beneﬁcial routes,bandwidth is not *** FLC outperforms the scheduling methods with a wide range of *** improve QoS within MANETs,it is therefore recommended that FLC is used to synchronize ***,using these performance metrics,the QoS-responsible routing can opt for more stable *** on network simulation,it is evident that incorporating QoS into routing protocols is meant to improve traf-ﬁc performance,in particular authentic-time trafﬁc.

关键词： MANET quality of service fuzzy logic mobile node network trafﬁc

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Smart Agriculture: Explainable Deep Learning Approach with Gradient-Weighted Class Activation Mapping

Smart Agriculture: Explainable Deep Learning Approach with G...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Shaik, Mohammad Khaja Prasad, Mudarakola Lakshmi Sowmya Reddy, Y. Asif, S. Kalpana, D. Reddy, Pundru Chandra Shaker Electronics and Communication Engineering St. Ann's College of Engineering and Technology Andhra Pradesh Chirala India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Dundigal India CVR College of Engineering Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad India Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (纸本)9798350388534

In light of the pressing need to guarantee food security in the face of threats posed by climate change, an increasing global population together with rising incomes, worsening soil erosion, and dwindling water resources, crop yield forecasting has assumed more significance in recent years. A relatively new area of study, 'Explainable AI' seeks to provide context for the findings produced by Deep learning models. Nevertheless, there is a pressing need for more comprehensive evaluations of deep learning models for object classification in images. Due to this, the research suggests two steps for implementing Explainable AI: (1) assessing the deep-learning(DL)-strategies accuracy utilizing evaluation methods, and (2) evaluating the feature recognition skill of an scan when recognized by DL utilizing Grad-CAM for model construal. Compared to EfficientNetV2S and Xception, the results reveal that MobieNet model has lower dependability but higher accuracy. When taking the ratio match rate into account, though, MobileNetV2 achieves the best accuracy. Building intelligent agriculture support systems (with the help of automated fruit-picking robots that discard bad fruits) is one application of the study's findings. Another is the utilization of the Explainable AI model's output to determine the best deep learning model for processing. © 2024 IEEE.

关键词： Deep reinforcement learning

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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The Burn Grafting Image Reclamation Redefined with the Peak-Valley Approach

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Critical Reviews in Biomedical engineering 2025年第2期53卷 21-35页

作者： Pradeep Kumar, B.P. Naresh, E. Ashwitha, A. Reddy, Kadiri Thirupal Srinidhi, N.N. Department of Computer Science and Design Atria Institute of Technology VTU Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of CSE Malla Reddy Engineering College for Women Telangana Hyderabad India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Burn injuries constitute a significant public health challenge, often necessitating the expertise of medical professionals for diagnosis. However, in scenarios where specialized facilities are unavailable, the utility of automated burn assessment tools becomes evident. Factors such as burn area, depth, and location play a pivotal role in determining burn severity. In this study, we present a classification model for burn diagnosis, leveraging automated machine learning techniques. Our approach includes an image reclamation system that incorporates the peak and valley algorithm, ensuring the removal of noise while consistently delivering high-quality results. By using skewness and kurtosis, we demonstrate substantial improvements in diagnostic accuracy. Our proposed system sources key features from enhanced grafting samples using peak valley transformation, enabling the computation of BQs and a unique bin analysis to enhance image reclamation. Our experimental results highlight efficiency gains, notably growing the matching features of graft samples for 14 matching images. The intended work involves the creation of a burn classification reclamation model. The proposed approach utilizes a support vector machine (SVM). The evaluation of the model will be conducted using an untrained catalogue, with a specific focus on its effectiveness in reclaiming images that necessitate grafts and distinguishing them from those that do not. Our approach holds promise in grafting sample reclamation in emergency settings, thereby expediting more accurate diagnoses and treatments for acute burn injuries. This work has the latent to save lives and improve patient upshots in burn traumas. © 2025 by Begell House, Inc.

关键词： Support vector machines

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Multi-Source Medical Data Integration and Mining for Healthcare Services 7

Multi-Source Medical Data Integration and Mining for Healthc...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Jeyaranjani, J. Prakash, Kodali Gnana Cheemakurthi, Sai Pavan Reddy, Banda Anoop Aashray, Lagishetty SRM Madurai College of Engineering and Technology Pottapalayam Computer Science and Engineering Sivagangai District India Kalasalingam Academy of Research and Education Anandnagar Computer Science and Engineering Krishnankoil India

ISBN: (纸本)9798350397253

As the Internet of Health (IoH) era has come into being, health services are progressively moving online and have been creating a sizable amount of health-related data related to patients, physicians, medical infrastructure, and other related topics. The effective integration and analysis of this IoH data have good implications for the provision of medical treatment and scientific disaster diagnosis. IoH data, however, are frequently dispersed throughout several departments and only partially protect user privacy. Therefore, efficiently integrating or mining the critical IoH data, while maintaining user privacy, is sometimes a difficult issue. Thus, this study provides IoH as a solution to this problem, allowing the IoH to submit medications to a specific patient without disclosing patient information and the admin to access the medications upon IoH's approval. Patients can watch physicians and consult them only once admin and the doctor have given their approval. © 2023 IEEE.

关键词： Data integration

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Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

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Energy engineering 2023年第8期120卷 1747-1761页

作者： Nisha Vasudevan Vasudevan Venkatraman A.Ramkumar T.Muthukumar A.Sheela M.Vetrivel R.J.Vijaya Saraswathi F.T.Josh Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Computer Science Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Electrical and Electronics Engineering Kongunadu College of Engineering and TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Kongu Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Narasu’s Sarathy Institute of TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Panimalar Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Karunya Institute of Technology&SciencesTamil NaduIndia

MigroGrid(MG)has emerged to resolve the growing demand for *** because of its inconsistent output,it can result in various power quality(PQ)*** is a problem that is becoming more and more important for the reliability of power systems that use renewable energy ***,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply ***,this research focuses on power quality enhancement in the MG using hybrid shunt ***,the performance of the filter mainly depends upon the design,and stability of the *** efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)*** performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this ***,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in *** the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

关键词： Artificial intelligence resistive inductive load shunt hybrid filter smart grid adaptive fuzzy back-stepping power factor

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Digital Guardians: Enhancing Women’s Security with Artificial Intelligence and IoT 23th

Digital Guardians: Enhancing Women’s Security with Artifici...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Arya, Leena Sharma, Yogesh Kumar Kumar, Ramakrishna Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India College of Engineering National University of Science and Technology Muscat Oman

ISBN: (纸本)9783031646492

In the contemporary global context, women encounter various challenges, notably harassment. While numerous devices and methods have been proposed to improve women’s security, many still require manual operation. These devices rely on human intervention, making it crucial to ensure women’s online safety and security. This paper explores innovative approaches to enhance women’s digital security by combining Artificial Intelligence (AI) and the Internet of Things (IoT). The goal is to empower women in their online spaces and bolster their digital experiences. The paper begins by highlighting the unique cybersecurity challenges that women face, including online harassment, stalking, privacy breaches, and gender-based digital threats. These challenges have real-world consequences for women’s well-being and personal safety, extending beyond the digital realm. Concurrently, IoT devices such as wearables, intelligent surveillance systems, and environmental sensors provide valuable context-aware data. This data enhances the accuracy and depth of threat detection, providing a comprehensive understanding of the digital landscape. The paper underscores the ethical and legal aspects of deploying AI and IoT technologies to safeguard women’s security. Responsible practices encompass a range of measures, including robust data protection protocols, privacy safeguards, and a dedicated commitment to transparent technology implementations. The effectiveness of these proposed AI and IoT-powered security solutions is demonstrated through practical implementations and vivid case studies. These real-life examples illustrate how women can regain control over their online interactions and personal data, creating a safer and more empowering digital environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Latex Equation Sequencer using Vector Mask Overlay and Localized Composition Analysis 15

Latex Equation Sequencer using Vector Mask Overlay and Local...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deore, Shalaka Mishra, Ranu Mapar, Aishwarya Shitole, Sakshi Mandani, Pranav Kulkarni, Shriniwas Department of Computer Engineering Modern Education Society's Wadia College of Engineering Maharashtra Pune India TextMover Labs LLC MesaAZ United States

ISBN: (纸本)9798331300579

LaTeX finds extensive application in report generation across diverse sectors. Predominantly, LaTeX code is incomprehensible without extensive training and compilation into vector or raster images. Complex LaTeX code blocks are somewhat difficult to interpret without code level comments at a glance. To address this, step-by-step animations of LaTeX components offer an interactive experience for readers, facilitating a more intuitive approach to learning different aspects of LaTeX syntax. By dissecting labyrinthine code segments, these animations resemble a debugging process in programming languages, allowing users to grasp each step more effectively. Thus, the proposed system overcomes the disadvantage of the existing system, which is that it can animate latex code. © Grenze Scientific Society, 2024.

关键词： Animation

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