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Machine learning Approaches for Process Automation: A Case Study Using Q-learning and Supervised learning 4

Machine Learning Approaches for Process Automation: A Case S...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Azad, S.M.A.K. Suresh, P. Sireesha, R. Department of Electrical and Electronics Engineering Sri Venkanteswara College of Engineering India

ISBN: (纸本)9798331530402

Process automation is critical in modern industries, providing systems for precise control of variables such as temperature, pressure, and flow. Traditional control methods like PID controllers require manual tuning and often struggle with nonlinear dynamic systems. This paper presents a comparative study of two machine learning techniques namely Q-learning and Decision Trees applied to industrial process control, specifically temperature regulation. Through a Python-based simulation, we evaluate the effectiveness of both techniques in real-time optimization. The results demonstrate that Q-learning continuously adapts to system changes, optimizing control strategies, while Decision Trees provide reliable predictions based on historical data, reducing error margins. Our study shows that machine learning has the potential to revolutionize process automation by minimizing human intervention, enhancing efficiency, and improving system robustness. © 2025 IEEE.

关键词： Three term control systems

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15th international Scientific conference on Distance learning in Applied Informatics, DiVAI 2024

15th International Scientific Conference on Distance Learnin...

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15th international Scientific conference on Distance learning in Applied Informatics, DiVAI 2024

ISBN: (纸本)9783031812606

The proceedings contain 24 papers. The special focus in this conference is on Distance learning in Applied Informatics. The topics include: Possibilities and Limits of the Use of Extended Reality in Chemistry Education at Lower Secondary School;google Applications in the Position of learning Management System 2.0 in Comparison with Moodle;RespectNET Initiative: Respectful Communication Through Media Education Network;on Suitability of Renko Charts for Algorithmic Trading;low-Energy Attendance System Based on IoT Technology;Using an AI-Based Language Model to Generate Synthetic Statistical Data;Pillar 3: Did COVID-19 and the War in Ukraine Affect Interest in Mandatory Disclosure?;The Impact and Use of AI in Modern University Education;AI in Multicultural Dialogue Between Teachers and Parents of Refugee and Immigrant Children in Slovenia;exploring the Use of GeoGebra Software in teaching Stereometry at Primary Schools: An Action Research Study;increasing the Active Involvement of Students in Chemistry teaching at Lower-Secondary School;pre-service Preschool and Primary School Teachers’ Position on Artificial Intelligence;mining and Farming of Cryptocurrencies: Comparison of Economic and Environmental Demands;virtual Reality in (Physics) Education: Impact on Critical Spots;Impact of Redundant Link on Message Delivery in IQRF Networks;mobile Citizen Science Applications for teaching Biodiversity Within the Third Cycle of Elementary Education;Utilizing the UTAUT2 Model to Elucidate Student Perceptions of Chatbot Acceptance as a Communication Medium in Higher Education;digital and Reading Competency of Secondary School Pupils and Their Comprehension of Everyday and Educational Texts;application of Arduino Microcomputer in the Creation of Aeronautical Data Recorder.

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Energy-efficient edge based intrusion detection system using machine learning 2

Energy-efficient edge based intrusion detection system using...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sonker, Sanjay Kumar Kumar, Ashish Kumar, Manoj Kamini Agrawal, Prem Prakash Devi, Poonam Dept. CSA School of Engineering and Technology Sharda University Greater Noida India Dept. CSE School of Engineering and Technology Sharda University Greater Noida India Dept. CSE Government Girls Polytechnic Tilhar Shahjahanpur India

ISBN: (纸本)9798331530389

Edge based computing's primary goal is to enhance network performance by storing and processing data in proximity to the end user. However, the rapid development of edge computing often overlooks security risks in powerful applications and large-scale computer systems. Security and privacy are essential prerequisites for both edge computing and the ecosystems built around it. Additionally, intrusion detection systems (IDS) are used to detect and analyze network traffic, identify anomalies, trigger alarms, and automatically block the traffic according to the sets of rules design in IDS. This paper discusses the Edge based IDS designed specifically for edge computing with accuracy 99.77% to detect the attacks with 30% of malicious electric vehicle include in the simulation focusing on its relevant processes, features, and procedures. © 2025 IEEE.

关键词： Network intrusion

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Revolutionizing learning: The Role of AR/VR Technologies in Educational Delivery and Student Engagement in Higher Studies 2

Revolutionizing Learning: The Role of AR/VR Technologies in ...

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2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Patel, Krisha R. Brahmbhatt, Vishva B. Dubey, Nilesh Jayswal, Hardik Devang Patel Institute of Advanced Technology and Research Department of Computer Engineering Vadodara India Devang Patel Institute of Advanced Technology and Research Department of Computer Science and Engineering Vadodara India Devang Patel Institute of Advanced Technology and Research Department of Infromation Technology Vadodara India

ISBN: (纸本)9798331505745

Augmented Reality (AR) and Virtual Reality (VR) are emerging technologies that combine digital and physical worlds to provide immersive and interactive experiences. Their use in education has been revolutionary, particularly in terms of increasing student engagement, grasp of complicated concepts, and encouraging experiential learning. AR superimposes virtual things on real-world surroundings, whereas VR immerses viewers in a completely digital environment. These tools gained traction during the COVID-19 epidemic, demonstrating their potential for remote learning and increasing student enthusiasm and information retention. While AR and VR are commonly employed in higher education, particularly in sectors such as healthcare and engineering, there are still barriers to their complete adoption, including cost, accessibility, and ethical concerns. This study explores the impactful uses of AR and VR in education, shedding light on their potential to modernize traditional teaching methods and improve student outcomes. © 2025 IEEE.

关键词： Students

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The Impact of TV White Space (TVWS) and 5G Networks on Bimodal learning for Master's Programs in engineering Science at the Higher Institute of Technology, University of Bangui

The Impact of TV White Space (TVWS) and 5G Networks on Bimod...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Ndassimba, Edgard Zoh-Ponguele, Nathan Kossingou, Ghislain Mervyl Saint-Juste Faculty of Science University of Bangui Department of Computer Engineering Bangui Central African Republic

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

In this study, we presente digital solution for training master's students in engineering sciences, using a bimodal learning approach at the Higher Institute of Technology of Bangui's University. This proposal addresses the lack of master's-level programs in DevOps and Network Telecommunications, as well as the insufficient expertise of local educators in Information and Communication Technology (ICT). It also aims to strengthen the instruction of science, technology, engineering, and mathematics (STEM). The solution is based on remote learning platforms hosted in the public cloud, engaging students and teachers in a bimodal teaching model that delivers simultaneous instruction to both inperson and remote students via a high-speed TV White Space (TVWS) and 5G network. The expected impact is the training of competent engineers capable of successfully carrying out projects in the domain of information and communication technology, thereby contributing to the development of the Central African Republic. © 2025 IEEE.

关键词： Students

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Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

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Overview of Vehicle Edge computing and Its Security 6th

Overview of Vehicle Edge Computing and Its Security

引用

6th international conference on Security and Privacy in New computing Environments, SPNCE 2023

作者： Han, Shaodong Wang, Maojie Chen, Guihong Guangdong Polytechnic Normal University Guangzhou510635 China

ISBN: (纸本)9783031736988

The Internet of Vehicles (IoV) is a part of the Internet of Things (IoT). With the continuous development of the Internet of Things technology, the Internet of Vehicles technology has also made great progress. However, as more and more vehicles are connected to the Internet of Vehicles, the calculation and transmission of data in the Internet of Vehicles becomes more and more difficult, and at the same time, it is inevitable to face the pressure of data security and privacy protection. In order to solve the above problems, academia and industry have adopted many methods, combining mobile edge computing technology with the Internet of Vehicles to establish a vehicle edge computing network to solve the problems of data computing and transmission. Introducing blockchain technology and federated learning technology into vehicle edge computing. Therefore, the network addresses the issues of data security and privacy protection. Based on these, this paper summarizes the research results of many scholars in related fields, in order to provide reference and reference for subsequent related research. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Federated learning

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Deep learning-Powered Edge Analytics for IoT-based Sensor Networks 2

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Subramani, Ponnusamy Selvam, M. Panneer Sworna Kokila, M.L. Vijayalakshmi, R. Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Physics Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India School of Computing SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks. © 2025 IEEE.

关键词： Deep learning

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HeterSim: Enable Flexible Simulation for Heterogeneous Distributed Deep learning Platform

HeterSim: Enable Flexible Simulation for Heterogeneous Distr...

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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Tang, Yinan Zhang, Hongwei Wang, Li Guo, Zhenhua Zhao, Yaqian Li, Rengang IEIT SYSTEM Co. Ltd. Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Distributed deep learning has become an essential technique for accelerating deep learning, but its performance is often influenced by the heterogeneous computing nodes and heterogeneous communication networks within the distributed deep learning platform. Due to the high costs of practical deployment and running of distributed deep learning, it is almost impossible for researchers to optimize the training strategy of distributed deep learning tasks in real-world environments. In this paper, we propose HeterSim, a simulator specifically designed for heterogeneous distributed deep learning platforms. HeterSim enables flexible configuration of computing node performance and network connections, and supports to define and simulate distributed deep learning workloads using graph-based representations. HeterSim also allows for the modification of communication strategies in the distributed deep learning process, thereby assisting researchers in validating their designs for distributed deep learning. We verify the feasibility and flexibility of HeterSim, by generating a simulation platform at the scale of millions of nodes, and successfully simulate the distributed deep learning process of Resnet50. We aim to provide HeterSim as a flexible and user-friendly simulator for researchers, targeting heterogeneous distributed deep learning platforms, and helping researchers evaluate and optimize the strategy of distributed deep learning tasks at a lower cost. © 2025 SPIE.

关键词： Computer simulations Switches Deep learning Education and training Data modeling Modeling Computation time Computer architecture Manufacturing Mathematical modeling

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Exploring educational transformation and innovation pathways in the era of artificial intelligence 24

Exploring educational transformation and innovation pathways...

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2024 international conference on Artificial Intelligence and Future Education, AIFE 2024

作者： Sun, Weiwei Qin, Minwu Yin, Zhenyao Guangzhou Institute of Science and Technology Guangzhou Guangdong China Guangdong Baiyun University Guangzhou Guangdong China

ISBN: (纸本)9798400710650

This study analyzes the profound impact of artificial intelligence (AI) on education, exploring the applications of educational reform theory, technological innovation theory, and the theory of equal educational opportunities in the context of AI-driven educational transformation. The rapid advancement of AI technologies-particularly deep learning, intelligent image recognition, big data, and educational robotics-is driving education from traditional models toward personalization, lifelong learning, and intelligent approaches. Technological innovation not only revamps teaching tools and resources but also enables differentiated instruction through intelligent data analysis, promoting educational equity and enhancing students' self-learning abilities. AI demonstrates immense potential in supporting lifelong learning, optimizing educational processes, and enriching the educational ecosystem;however, it also raises ethical challenges, including privacy concerns and risks of educational alienation. Consequently, educators should focus on the responsible application of AI technologies, leveraging the intrinsic strengths of education, to improve teaching quality and foster harmonious development between humans and machines. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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