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Bees Swarm Optimization Based Prediction of Machined Fatigue Failure 3

Bees Swarm Optimization Based Prediction of Machined Fatigue...

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3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Sumithra, M. Sahebzathi, S. Parveen, Nikhat Arun, K.K. Dahane, Ganesh Madhukar Nagabalan, U. School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Velammal College of Engineering and Technology Department of Cse Madurai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Kumaraguru College of Technology Department of Mechanical Engineering TamilNadu Coimbatore India Dr. Vithalrao Vikhe Patil College of Engineering Maharashtra Ahmedhnagar India R.M.D. Engineering College Department of Science and Humanities - General Engineering Chennai India

ISBN: (纸本)9798331542375

Artificial intelligence approach is now able to anticipate fatigue failure of machined components based on elementary key numbers without the need for professional knowledge of evaluation processes. In this paper, we develop a meta-heuristic approach for the prediction of defects using Bees Swarm Optimisation (BSO) in machined components. The study mainly predicts the fatigue failure by training the BSO experimentally with the training datasets. The three most essential input variables are the load stress, the hardness of the material, and the size of the fatal flaw (KD). In the context of fatigue testing, the only outcomes that may be determined are failure or non-failure. In this investigation, a single data set is generated for each specimen that is examined. When a full fracture has occurred at a given stress level, fracture surface analysis estimates the size of the deadly flaw that has formed. The architecture and hyperparameters of the neural network are cross-validated using a K-fold technique in order to get the highest possible prediction accuracy. After all is said and done, a cautious mean fatigue failure prediction accuracy of 92% is achieved. © 2025 ieee.

关键词： Fatigue testing

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Automated Detection of Tomato Leaf Diseases Using Convolutional Neural Networks 3

Automated Detection of Tomato Leaf Diseases Using Convolutio...

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3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Yadav, Hemant Kumar Srivastava, Varsha Banoth, Sreenu Rohini Rawat, Daksh Rawat, Priyansha Iimt College of Engineering Uttar Pradesh Greater Noida201310 India School of Computer Science and Engineering Iilm University Uttar Pradesh Greater Noida201306 India Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science and Engineering Jhanjeri Punjab Mohali140307 India Graphic Era Hill University Dehradun India

ISBN: (纸本)9798331542375

In this study, an automated system to detect tomato leaf diseases with Convolutional Neural Networks (CNNs) is introduced to aid farmers in early disease identification to avert crop losses and promote yield. The dataset is multiple classes of tomato leaf diseases, and the model was trained and tuned extensively for its performance. In particular, a high training accuracy of 95.73%, and validation accuracy of 89.85% was achieved by CNN. Furthermore, the system's precision, recall, and F1 metric were around 90%, indicating the balanced system performance. The proposed CNN model is shown to be more reliable and faster than the traditional image processing techniques and hence is an appropriate choice for real time agricultural applications. The integration of modern varieties technologies in AI based approaches in agriculture which will feed productivity while maintaining Sustainability. The results indicate that the proposed system could provide an effective means for disease detection for farmers and may alleviate some of the need for chemical treatments. © 2025 ieee.

关键词： Convolutional neural networks

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LEUSIM: A Lightweight Extendable User SIMulator for Testing Commercial Task-Oriented Dialogue System

LEUSIM: A Lightweight Extendable User SIMulator for Testing ...

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2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Li, Xiaoqian Guan, Wenbo Liang, Lingyu Zhang, Huanming School of Computing Science and Engineering South China University of Technology Guangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China Artificial Intelligence Technology Co. Ltd China Southern Power Grid Guangzhou China

ISBN: (纸本)9798331506797

We propose LEUSIM (Lightweight Extendable User SIMulator), an automatic testing and assessing framework for commercial Chinese task-oriented dialogue systems. LEUSIM contains four modules, namely, natural language parser, business flow controller, user utterance imitator and system performance recorder, which can be directly obtained from the existing telephone intelligent customer service (TICS) system without or with a little adaptation. Having dependency parsing theory as its theoretical basis, LEUSIM can fully imitate the users' behavior and make it an applicable testing framework in real world scenario. © 2025 ieee.

关键词： Commercial task-oriented dialogue system dependency parsing theory testing framework

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Future Trends in HRM: The Role of Artificial Intelligence and automation in Shaping the Workplace

Future Trends in HRM: The Role of Artificial Intelligence an...

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2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Panicker, Aneesya Krishna, Somanchi Hari Garg, Shaifali Biswas, Prasanta Chatterjee Lourens, Melanie Kumar, K. Siva Gla University Institute of Business Management Uttar Pradesh Mathura India Vignana Bharathi Institute of Technology Department of Business Management Telangana State 501301 India Amity University Amity Business School Madhya Pradesh India Parul University Cdoe Vadodara India Durban University of Technology Deputy Dean Faculty of Management Sciences South Africa Bapatla Engineering College Computer Science and Engineering Andhra Pradesh Bapatla India

ISBN: (纸本)9798331508685

Artificial intelligence-based applications are integrated in the human resource management of firms for managing people within the region as well as international organisations. In the past years the growth of AI based applications proliferates the function of HR M that triggers to exciting new stream to study the effect of AI adoption on business level and individual outcomes. It evaluates the AI enabled HRM practises. The technology examines the firms with problem solving, decision making and utilisation of resources and opportunities for employees. The growing interest on AI based technologies for HRM for diffusion of innovation in large multinational enterprises. AI has the capability for reduction of administrative burden experienced by the HR Professional that allows them for precise decision making based on the data patterns. Adoption of artificial intelligence helps in effective employee management and decision making. So that employee of HR management can overcome the challenges. The study aims to analyse the future trends in HRM and the role of artificial intelligence and automation in shaping the workplace. © 2025 ieee.

关键词： Resource allocation

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High-Performance Synchronization Control of Multi-Axis Motion System Based on Fully-Actuated System Approach 26

High-Performance Synchronization Control of Multi-Axis Motio...

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26th International conference on Industrial Technology, ICIT 2025

作者： Zhang, Dufei Wang, Yao-Wei Gu, Caoyuan Jia, Yu Wu, Qi Wu, Xiang School of Information Science and Engineering Wuhan University of Science and Technology Wuhan30064 China Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430064 China Zhejiang University of Technology Department of Automation China Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou310023 China

ISBN: (纸本)9798331521950

This paper investigates a class of synchronization control problem of multi-axis motion systems(MAMSs) with uncertainty and external disturbances. Firstly, a system model that satisfies the full-actuation condition is obtained by performing differential decomposition on each axis(subsystem). The uncertainty and external disturbances are treated as the lumped disturbances of the system. Secondly, a fully-actuated system approach based controller is designed to obtain a linear closed-loop system, which the parameters of the system are determined by parametric approach. Furthermore, the synchronization error model is established and a generalized extended state observer(GESO) is designed to estimate the system state while also estimating the lumped disturbances of the system. Then, the controller with disturbance compensation is designed to achieve high-performance synchronization control of the MSMSs, and system stability analysis is provided. Finally, the effectiveness and superior performance of the proposed approach is verified by simulation experiments. © 2025 ieee.

关键词： Closed loop control systems

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Transfer Learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation 3

Transfer Learning based Analysis of Chest X-rays for Accurat...

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3rd International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Perla, Someswari Veledendi, Sudhakar Jaiswal, Pooja Muppidi, Satish Mandala, Jyothi Maram, Balajee Gmr Institute of Technology Department of Cse - Ai&ml Andhra Pradesh Rajam India Sr University School of Computer Science and Artificial Intelligence Warangal India Galgotias University India Gmr Institute of Technology Departnent of Computer Science and Engineering Rajam India University Department of Computer Science and Engineering Banglore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798331515911

This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease. © 2025 ieee.

关键词： Lung cancer

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System Design and Development of a Personnel Training Platform Based on Distributed Storage Technology 5

System Design and Development of a Personnel Training Platfo...

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5th Asia conference on Information engineering, ACIE 2025

作者： Wu, Wenting Sun, Liming China Communications Information Technology Group Beijing China School of Computer Science and Technology Shandong University of Technology Zibo China

ISBN: (纸本)9798331528409

With the intensification of corporate competition and the rapid development of technology, the continuous improvement of employee skills and career development has become a core focus for businesses. Employee training platform systems, by efficiently integrating training resources and enhancing employees' overall competence and skills, provide crucial support for talent cultivation and competitiveness enhancement. However, traditional employee training platform systems face challenges such as poor system flexibility, low data storage efficiency, and a lack of automation and intelligence, which make it difficult to meet the needs of modern enterprises. This paper designs and develops an employee training platform system based on distributed storage technology, microservice architecture, and process service technology. The proposed system effectively addresses the scalability and efficiency issues of traditional training platforms, offering enterprises an efficient and scalable employee training management solution, which holds broad practical application value. © 2025 ieee.

关键词： Storage efficiency

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Non-Invasive Measurement and Multi-Channel Attention Network for Fault Diagnosis of Rotating Machinery Under Variable Operating Conditions 26

Non-Invasive Measurement and Multi-Channel Attention Network...

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26th International conference on Industrial Technology, ICIT 2025

作者： Wu, Qi Mou, Binbin Li, Zhijian Zhang, Wen-An Wang, Yao-Wei Wang, Zhenbin College of Information Engineering Zhejiang University of Technology Hangzhou China Department of Automation Zhejiang University of Technology Hangzhou310023 China Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou310023 China College of Information Science and Engineering Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430064 China Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology Wuhan430064 China

ISBN: (纸本)9798331521950

Rotating machinery is critical in industries such as wind power, aerospace, and manufacturing. These machines often operate under high loads and intense conditions, leading to frequent failures in critical components like bearings and gears. Such failures can result in significant economic losses and safety concerns. Fault diagnosis technology, which uses operational data to analyze and identify fault types, is essential for maintaining reliable operation. Traditional vibration signal analysis is effective but costly and invasive, while acoustic analysis is non-invasive and cost-effective but vulnerable to environmental noise. In contrast, three-phase current signals, measured using current transformers, offer a low-cost, minimally invasive alternative with excellent noise resistance. This paper proposes a method for fault diagnosis using three-phase current signals. The method employs Butterworth filtering to improve the signal-to-noise ratio, followed by advanced data analysis techniques such as multi-scale feature extraction and a multi-channel attention mechanism to realized fault diagnosis of rotating machinery. Experimental results demonstrate the effectiveness of this approach in achieving accurate fault detection, with a diagnostic accuracy of 96.11%. The proposed method shows robust diagnostic capabilities across various operating conditions. © 2025 ieee.

关键词： Bearings (machine parts)

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Next-Generation Embedded Systems with Synergies VLSI with Deep Learning: Design Methodologies, Optimization Techniques, and Real-World Applications

Next-Generation Embedded Systems with Synergies VLSI with De...

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2025 ieee International Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Kalarani, G. Mohanapriya, M. Pattunnarajam, P. Sasikala, G. Sivakumar, J. Suresh, M. Jaya Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Ece Tamil Nadu Sriperumbudur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Mathematics Tamil Nadu Thiruvallur India

ISBN: (纸本)9798331529833

New generation of embedded systems with superior intelligence, energy efficiency, and performance have emerged as a result of the merging of deep learning with Very-Large-Scale Integration (VLSI) technology. Methodologies for design, optimisation strategies, and practical uses of next-generation embedded systems are the foci of this study, which investigates the ways in which VLSI and deep learning might work together. These systems have the potential to transform several industries, such as transportation, medicine, robotics, and the IoT, by harnessing the processing power of deep neural networks with the improvements in semiconductor fabrication. Prior to delving into the advantages of bespoke hardware design for deep learning inference and training, we trace the history of very large scale integration (VLSI) technology and its incorporation with deep learning algorithms. Investigated here are the design techniques that, when applied to very large scale integration (VLSI) architectures like FPGAs and ASICs, allow for the efficient mapping of deep learning models onto these devices. We show case studies that show how these methods work and talk about the trade-offs between performance, power consumption, and adaptability. The development of next-generation embedded systems relies heavily on optimisation approaches. Model compression, quantisation, and pruning are some of the optimisation strategies that we examine;they lessen the memory and computational demands of deep learning models without drastically altering their accuracy. For embedded devices with limited resources, these methods are crucial for implementing deep learning models. Additionally, we explore the practical uses of embedded systems augmented with VLSI and deep learning. By capitalising on the complementary strengths of VLSI and deep learning, applications like autonomous driving, medical imaging, and smart home automation are revolutionising entire industries. In this paper, we examine the design

关键词： Integrated circuit design

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Rethinking B-ϵ tree Indexing Structure over NVM with the Support of Multi-write Modes 25

Rethinking B-ϵ tree Indexing Structure over NVM with the Su...

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30th Asia and South Pacific Design automation conference, ASP-DAC 2025

作者： Luo, Hui-Tang Chen, Tseng-Yi Department of Computer Science and Information Engineering National Central University Taoyuan City Taiwan

ISBN: (纸本)9798400706356

The Bϵ tree is an essential indexing structure in modern file and database systems, renowned for its high read and write performance. However, constructing a Bϵ tree involves substantial write overhead due to repetitive key writing during flushing. This study presents the mw-Bϵ tree, a novel approach leveraging multi-write modes in persistent memory to reduce Bϵ tree construction costs. By dynamically adapting write modes based on node update frequencies, the mw-Bϵ tree outperforms traditional fixed-mode indexing schemes. Our research demonstrates that integrating multi-write mode support in non-volatile memory (NVM) can significantly enhance the efficiency of Bϵ tree indexing. Experimental results using real-world workloads show that the mw-Bϵ tree reduces power consumption by up to 43.5% and energy usage by 27.6%, while also improving write latency. This work is the first to investigate and address power consumption challenges in Bϵ tree construction, providing a compelling case for the adoption of multi-write modes in NVM technologies for indexing structures. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Nonvolatile storage

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