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1st International conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Varsha, K.S. Mohan, Anju Ramu, Murugan Saveetha School of Law Chennai India

ISBN: (纸本)9798331529246

Quick growth of e-waste poses a substantial risk environment and health concerns;thereby, there is an imminent need for efficient management solutions. This paper explains the utilization of ML-based algorithms in improving e-waste collection and recycling capabilities. It employs superior techniques like CNN and YOLOv8 along with image recognition to identify e-waste in real time. The CNN-VGG model uses deep convolutional layers to extract features in the E-Waste dataset to classify e-wastes. YOLOv8 assists in the location and detection of e-waste objects. The new approach is developed in Python and demonstrates an accuracy of nearly 99%, better than other models such as FrHHGO-based ShCNN and SNN by a margin of 10.44%. This shows how CNN-VGG is good for the classification of e-waste. Adding these ML models into automation systems in recycling helps recycle more material and sustain the environment in e-waste management. The work published here advances more effective, ecologically sound e-waste recycling practice. © 2025 ieee.

关键词： data-driven solutions e-waste management image classification machine learning object detection

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Six Sigma Approach to Developing a Microgrid Station for Research and Teaching

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ieee ACCESS 2025年 13卷 23209-23219页

作者： de Doile, Gabriel Nasser Doyle Castilla, Miguel Balestrassi, Pedro Paulo de Souza, Antonio Carlos Zambroni Miret, Jaume Fed Univ Itajuba UNIFEI Inst Elect Syst & Energy ISEE BR-37500903 Itajuba Brazil Univ Politecn Catalunya EPSEVG UPC Escola Politecn Super Engn Vilanova Geltru i Barcelona 08800 Spain Fed Univ Itajuba UNIFEI Prod Engn & Management IEPG BR-37500903 Itajuba Brazil

Design for Six Sigma (DFSS) is a statistical methodology for improving the quality of processes and their organizational areas worldwide used since the 80's. However, most of the existing laboratories for obtaining experimental results, which validate theoretical proposals that are being studied in the microgrids research field, have been designed without following any roadmap. The purpose of this study was to develop a microgrid station for research and teaching at the Power Electronic Laboratory of Escola Polit & egrave;cnica Superior d Enginyeria de Vilanova i la Geltr & uacute;(EPSEVG) based on a DFSS tool called IDDOV, which stands for Identify, Define, Develop, Optimize, and Verify. An IDDOV roadmap to conduct a laboratory design development is provided and applied to design a laboratory at EPSEVG in this study. The results show that a well-structured roadmap, such as IDDOV, promotes a culture of continuous improvement for the microgrid research bed, encouraging feedback from users and stakeholders to identify opportunities for improvement. The conclusion of this paper highlights the benefits of using the DFSS conceptual methodology to develop a microgrid station for research and teaching, such as adaptation to educational needs;enhancement of the learning experience;refutation of experimental errors;cost-effective, flexible, and modular design;collaborative learning environment;data-driven teaching;and continuous improvement. As electrical grids are dynamic systems in constant evolution, a new IDDOV roadmap should be carried out in future studies, to enhance the test bed recommended here.

关键词： Microgrids Laboratories Education Six sigma Terminology Power electronics Process control Power system reliability Voltage control Reliability engineering Design for six sigma DFSS house of quality IDDOV laboratory microgrids roadmap test bed

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Revolutionizing Face Recognition with IIoT: Smarter, Faster, Connected

Revolutionizing Face Recognition with IIoT: Smarter, Faster,...

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2025 International conference on Computer, Electrical and Communication Engineering, ICCECE 2025

作者： Grover, Yash Sharma, Pulkit Kansal, Nishtha Vivekananda Institute of Professional Studies School of Engineering & Technology Technical Campus New Delhi India

ISBN: (纸本)9798331513894

The integration of facial recognition technology with Industrial Internet of Things (IIoT) systems offers significant potential to enhance operational efficiency, security, and safety within industrial environments. Automating processes like access control and attendance tracking minimizes human intervention, reduces errors, and streamlines workflows. From a security perspective, facial recognition strengthens access management, reduces unauthorized entries, and enhances real-time surveillance capabilities. In safety-critical industries, this technology can enforce compliance with protective protocols and monitor workers' well-being by detecting signs of fatigue or stress. Additionally, the use of artificial intelligence (AI) and machine learning facilitates real-time data analysis, providing insights to optimize operations and prevent system failures. This paper further examines the broader applications of IoT-driven facial recognition, highlighting promising research areas such as intelligent security systems, biometric authentication, automated surveillance, and smart access control. Practical implementations span smart homes, healthcare monitoring, financial transactions, and public safety, where real-time facial identification enhances both security and operational performance. Despite these advantages, challenges like data privacy concerns and the need for robust technological infrastructure remain critical. Strategies such as data encryption, edge computing, and adherence to privacy regulations like GDPR are essential to protect personal information. As advancements in AI, machine learning, and 5G networks continue, facial recognition systems integrated with IIoT will become increasingly accurate and efficient, paving the way for smarter, more secure, and highly efficient industrial and public environments. © 2025 ieee.

关键词： Access control

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Dynamic Equilibrium in Robotics:Techniques and Applications for Self Balancing Robots

Dynamic Equilibrium in Robotics:Techniques and Applications ...

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

作者： Kumar, Nitin Arora, Ujjawal Chaudhary, Neelu Manav Rachna University Computer Science & Technology Faridabad India

ISBN: (纸本)9798331529833

Self-balancing robots have become a key focus in robotics and control system studies since they are capable of standing on two wheels as an upright system. In the implementation of these systems, PID control, Linear Quadratic Regulation, fuzzy logic as well as reinforcement learning in machine learning is applied to control the balance and motion of the robots in the robots in different environmental conditions. This article reviews literature on self-balanced robots over the last half century moving beyond mechanical and electrical designs to control strategies including how double loop control incorporated PID control and how this has now shifted to machine learning. It also describes sensor fusion techniques where a Kalman filter or complementary role filters is employed sensor's accelerometer data and gyroscope data towards the assistance of estimating how tilted the robot is to the ground in degrees. Practical examples are also analyzed from personal devices such as the Segue, to education and robotics research. The paper also investigates the current challenges in the field including sensor drift , battery life, and especially the need for real time adjustment to varying environmental conditions such as bumpy terrain or perturbations. A number of ways are suggested such as using better sensors, control strategies and low power actuators which all improve balance robot systems. The study finishes with the discussion of the optimal developments, particularly focusing on the use of overcome strategies of controls using artificial intelligence for bringing about more flexibility and independency to self-balancing robots. © 2025 ieee.

关键词： Three term control systems

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Reinforcement learning Based data-driven control Method for Dynamic systems and Its Application to FLUIDMechatronix Platform 16th

Reinforcement Learning Based Data-Driven Control Method for...

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16th International conference on Modelling, Identification and control, ICMIC 2024

作者： Zou, Zonghan Gao, Jingjing Yang, Xu Huang, Jian Zhang, Tao Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789819617760

With the increasing complexity of industrial process, accurate models of automatic control systems are difficult to be established, which makes model-based control methods become less effective. On this premise, a reinforcement learning (RL) based data-driven control method for dynamic systems is proposed in this paper. For our purposes, a model-free RL controller with an Actor-Critic structure is developed by using neural networks (NN), combining the merits of both value-based and policy-based RL algorithms. Policy of the actor is assessed by the critic, updating parameters of RL controller in real-time for online optimization. The effectiveness of the RL controller is demonstrated by an experimental study on FLUIDMechatronix platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

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22nd International learning and Technology conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 ieee.

关键词： Satellite communication systems

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Trajectory Following controller Design with Physicsbased data-driven Vehicle Dynamics Modelling 3

Trajectory Following Controller Design with Physicsbased Dat...

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3rd International conference on Mechatronics, control and Robotics, ICMCR 2025

作者： Lu, Yong Tian, He Yang, Diange Jiang, Kun Lu, Ziwang Li, Runfeng School of Vehicle and Mobility Tsinghua University Beijing China DIAS Automotive Electronic Systems Co. Ltd Beijing China

ISBN: (纸本)9798331544546

Accurate modeling of vehicle dynamics plays a crucial role in accelerating algorithm development and reducing associated costs. This study presents a trajectory-following controller that integrates physics-based and data-driven approaches for vehicle dynamics modeling. A hybrid model, combining a two-degree-of-freedom (2DOF) physical model with a data-driven long short-term memory (LSTM) framework, is developed to capture vehicle dynamics. Using this hybrid model, the feedforward steering angle is determined by solving an optimization problem. The dataset is specifically designed to align with the operational design domain of advanced driver assistance systems (ADAS). Simulation results indicate that the hybrid model achieves higher accuracy than traditional physics-based models and demonstrates superior generalization compared to purely data-driven models. Furthermore, the findings validate the enhanced performance of the proposed control algorithm. © 2025 ieee.

关键词： Advanced driver assistance systems

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Intelligent Algorithm System for Security Governance driven by Big data in Autonomous systems 2

Intelligent Algorithm System for Security Governance Driven ...

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2nd International conference on Machine learning and Autonomous systems, ICMLAS 2025

作者： Wang, Yiran Shanghai University of Political Science and Law Shanghai201500 China

ISBN: (纸本)9798331505745

With the advancement of big data encryption and decryption technology, the role of data in national governance and social governance has become increasingly prominent. In order to effectively deal with the increasingly complex security governance issues, this study proposes the novel big data-driven intelligent algorithm system. The purpose of this study is to optimize the challenges of the network attack identification, protection measures, and encryption processing of information leakage in security governance through real-time monitoring and predictive analysis of data. The core of the designed system includes two modules: network attack detection algorithm and information leakage modeling encryption algorithm. From a methodological perspective, this study proposes a model for predicting abnormal network traffic based on chaos dynamics. This groundbreaking model uses time series analysis to identify and locate potential DDoS attack traffic. At the same time, combined with information entropy theory, a cascade network attack chain analysis model for DDoS attack protection is designed. At the data security level, an encryption algorithm based on chaos theory is used to effectively protect against information leakage. Through experimental verification, it is known that the proposed encryption algorithm can effectively retain the original data during the information encryption and decryption process. At the same time, through encryption algorithm verification, the security and anti-attack ability of information are improved. © 2025 ieee.

关键词： Information leakage

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data Analytics for Decision Support systems in Techno-driven Enterprises 1

Data Analytics for Decision Support Systems in Techno-Driven...

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1st International conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rakshitha Priya, S. Namjoshi, Asmita Mane, Mahesh Mohammed, Irshadullah Asim Shettigar, Rashmitha NMAMIT NITTE Department of AIML Karnataka Udupi India SRM Institute of Science and Technology TamilNadu Chennai India Tilak Maharashtra Vidyapeeth Pune India Symbiosis School for Online and Digital Learning Automotive Design and Development/Engineering Maharashtra Pune India FuelCell Energy Inc Syed Ali Chabutra Hyderabad India Thakur Ramnarayan college of arts and commerce Department of IT/CS Maharashtra Mumbai India

ISBN: (纸本)9798331518578

In the digital transformation era the emergence of data analytics plays a significant role in enhanced decision making process in Techno driven Enterprises. The study examines about the impact of data analytics in decision support system to emphasise the role of data analytics on operational tactical and strategical decision making. The big data artificial intelligence and machine learning leverages modern DSS to process large amount of unstructured and structured data optimised performance and extraction of actionable insights in the businesses. The decision support system involves visualisation techniques real time processing of data and predictive analytics that empower the enterprise to make agile and informed decisions. Moreover, the study examines the challenges in security integration and data quality to offer solutions for enhanced and effective decision making. The findings of the study necessitate data driven culture in techno driven enterprises for maintaining competitive edge in the technological landscape. © 2025 ieee.

关键词： data Analytics

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MEL Frequency Features driven Deep learning for Speech Emotion Detection: A Novel Model

MEL Frequency Features Driven Deep Learning for Speech Emoti...

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2025 ieee International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Rani, Rashmi Tiwari, Kalyani Ramaiya, Manoj Kumar Nagar, Rajesh Kumar Shrivastava, Neeraj Hardia, Garima Indore India IPS Academy Institute of Engineering and Science Department of Computer Science & Engineering India Indore India Indore India

ISBN: (纸本)9798331512965

Speech Emotion Recognition (SER) is a critical area of research in Human-Computer Interaction (HCI), aiming to develop systems capable of identifying and responding to human emotions conveyed through speech. This paper focuses on training a Deep Neural Network (DNN) model using Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, a widely used and effective technique in this domain. To address the complexity of speech data, we utilized a deep learning algorithm, specifically employing the Long Short-Term Memory (LSTM) network, in conjunction with MFCC features. © 2025 ieee.

关键词： Emotion Recognition

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