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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Luo, Huan Hunan University of Medicine Hunan China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

As a very popular teaching resource in recent years, "micro video" has the characteristics of highly focused learning themes, highly focused learning content and repeatable learning. When applied to life science teaching, it can refine essence content for knowledge teaching, provide students with repeatable learning, and improve the memory strength and mastery of new knowledge according to the reality of students' short attention time. Artificial intelligence technology can effectively help online course teachers solve this problem. The use of facial recognition, emotion detection, and behavior analysis technologies in artificial intelligence technology can conveniently monitor and analyze the learning behavior of online course students, achieve multidimensional evaluation of the teaching process, and more efficiently achieve precise teaching. © 2025 SPIE.

关键词： Contrastive learning

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Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC

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international JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第7期35卷 2835-2859页

作者： Pohlodek, Johannes Morabito, Bruno Schlauch, Christian Zometa, Pablo Findeisen, Rolf Tech Univ Darmstadt Control & Cyber Phys Syst Lab Darmstadt Germany Yokogawa Insil Biotechnol GmbH R&D Dept Stuttgart Germany TU Dortmund Res Ctr Trustworthy Data Sci & Secur Darmstadt Germany German Int Univ Fac Engn Berlin Germany

Model-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model-based approaches. Thanks to recent technological advancements in digitalization, machine-learning methods such as deep learning, and computing power, there has been an increasing interest in using machine learning methods alongside model-based approaches for control and estimation. The number of new methods and theoretical findings using machine learning for model-based control and optimization is increasing rapidly. However, there are no easy-to-use, flexible, and freely available open-source tools that support the development and straightforward solution to these problems. This article outlines the basic ideas and principles behind an easy-to-use Python toolbox that allows to solve machine-learning-supported optimization, model predictive control, and estimation problems quickly and efficiently. The toolbox leverages state-of-the-art machine learning libraries to train components used to define the problem. Machine learning can be used for a broad spectrum of problems, ranging from model predictive control for stabilization, set point tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick generation of code for embedded model predictive control applications. HILO-MPC is flexible and adaptable, making it especially suitable for research and fundamental development tasks. Due to its simplicity and numerous already implemented examples, it is also a powerful teaching tool. The usability is underlined, presenting a series of application examples.

关键词： estimation model-based optimal estimation and control machine learning model predictive control python open-source toolbox optimization

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Effectiveness of Fostering Innovation and Talents in Automation engineering Education through engineering Practice Innovation Project (EPIP) teaching Model

Effectiveness of Fostering Innovation and Talents in Automat...

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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Alemu, Tola Tsegaye Che, Yanqiu Mi, Jing School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China School of Vocational Education Tianjin University of Technology and Education Tianjin300222 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

In recent years, the field of automation engineering is rapid advancements and technological breakthroughs, resulting increasing demand for qualified and innovative professionals. To meet these requirements, higher education and vocational education in engineering education sector are constantly seeking effective teaching models that foster innovation and nurture talent in automation engineering and other engineering fields. One such teaching model that has gained prominence is the engineering Practice Innovation Project (EPIP) teaching model. This paper aims to promote and evaluate the effectiveness of the EPIP teaching model in fostering innovation and nurturing talents in automation engineering education. The study investigates the impact of the EPIP model on the trainee's ability to apply theoretical knowledge to practical projects, on their learning outcomes, and the development of their creativity and innovative skills. Preliminary findings indicate that the EPIP teaching model positively impacts on students' learning outcomes and innovation skills. Moreover, the collaborative nature of the EPIP model encourages teamwork, thinking out of box, and increase engineering practice skills, essential for success in the automation engineering industry. Furthermore, the EPIP model promotes innovation by providing students with opportunities to explore novel ideas and develop creative solutions and also plays a crucial role in talent development in automation engineering. The EPIP teaching model is designed to bridge the gap between theory and practice by providing trainees with practical experience in solving real-world problems. The results shows that EPIP teaching model for fostering innovation and talent in automation engineering education is effective. Trainees feedback is used to analyses the effectiveness fostering innovation and talents in automation engineering education through engineering Practice Innovation Project (EPIP) teaching model. More than 90% of the

关键词： Students

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Conversational and generative artificial intelligence and human-chatbot interaction in education and research

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international TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第3期32卷 1251-1281页

作者： Akpan, Ikpe Justice Kobara, Yawo M. Owolabi, Josiah Akpan, Asuama A. Offodile, Onyebuchi Felix Kent State Univ Dept Informat Syst & Business Analyt Kent OH 44242 USA Univ Windsor Odette Sch Business Windsor ON Canada Natl Open Univ Nigeria Fac Educ Abuja Nigeria Ibom Int Ctr Res & Scholarship Res & Dev Windsor ON Canada

Artificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human-like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human-chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006-2018, while 2019-2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI;32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human-computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self-diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline-based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

关键词： generative artificial intelligence quantum computing disruptive technology conversational chatbot ChatGPT human-robot interaction big data analytics technological transformation

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Multiple-objective optimization resource scheduling algorithm for edge computing based on Deep Q-Network 24

Multiple-objective optimization resource scheduling algorith...

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Kan, Jicheng Duan, Xu College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

To resolve the tension between edge computing service providers who aim to reduce energy use and users who prioritize enhanced service quality, we introduce an innovative edge computing resource allocation model utilizing a Deep Q-Network (DQN). This model dynamically reconciles energy usage with task completion time by modifying the emphasis placed on various optimization objectives. Experimental findings reveal that the proposed model successfully harmonizes these competing priorities, achieving substantial improvements over the benchmark algorithm in both energy efficiency and task execution time. © 2024 Copyright held by the owner/author(s).

关键词： Deep reinforcement learning

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engineering Education in a New Era: A Case Study in Saudi Arabia 22

Engineering Education in a New Era: A Case Study in Saudi Ar...

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

作者： Hassan, Maha M. Askool, Sanaa University of Prince Mugrin College of Engineering Madinah Saudi Arabia University of Prince Mugrin College of Computer and Cyber Sciences Madinah Saudi Arabia

ISBN: (纸本)9798331532215

COVID-19 Pandemic had impacted educational systems worldwide demanding major reforms to teaching strategies. Educators were forced to quickly adapt to remote learning and online teaching methods. This shift has highlighted the need for flexible and adaptable teaching strategies that can be implemented in traditional and virtual classrooms. Moreover, grasping attention of new generations requires more efforts in delivering knowledge and skills. With the rise of technology and social media, younger generations have become accustomed to receiving information in a fast-paced and visually engaging manner. This necessitates that educators implement ways to make learning more interactive, engaging, and relevant to students' lives. Experiential learning is a type of learning that involves hands-on experiences and active participation in the learning process taking many forms, such as internships, field trips, simulations, augmented reality and role-playing activities. AR/VR technology is recently playing a role in different educational activities. This study examines achievement levels and feedback reports of architectural and civil engineering students upon applying different experiential learning techniques. Students from these two main engineering disciplines worked together in a multidisciplinary project framework. Augmented reality activities were implemented using EON AR/VR Reality platform to create an engaging learning environment. The study is conducted by employing surveys to gauge students' perception. Positive feedback was reported in addition to enhancement of understanding of topics. © 2025 IEEE.

关键词： engineering education

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Improving Federated learning Efficiency with PruneFL: A Healthcare Perspective 24

Improving Federated Learning Efficiency with PruneFL: A Heal...

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Shahid, Alvina Arshad, Muhammad Muneeb Azam, Farooque Anwar, Muhammad Waseem Shafi, Imran Department of Computer and Software Engineering EME College National University of Sciences and Technology Punjab Islamabad Pakistan School of Interdisciplinary Engineering and Sciences National University of Sciences and Technology Punjab Islamabad Pakistan

ISBN: (纸本)9798400717482

In healthcare, accessing diverse and large datasets for machine learning poses challenges due to data privacy concerns. Federated learning (FL) addresses this by training models on decentralized data while preserving privacy. We propose PruneFL, a FL paradigm that leverages model pruning to reduce complexity and enhance efficiency. PruneFL strikes a balance between model accuracy and efficiency by reducing parameters, accelerating convergence, and minimizing communication overhead. Our approach addresses critical FL challenges and demonstrates efficacy in resource-constrained environments like healthcare. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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teaching Reform of New engineering Artificial Intelligence Course Oriented to OBE Concepts 24

Teaching Reform of New Engineering Artificial Intelligence C...

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

作者： Liao, Yuan Gao, Jianbo Zhao, Min Qiu, Tian School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China

ISBN: (纸本)9798400710650

In order to enhance the practical innovation ability of new engineering students, this paper, based on the educational policy of innovation driven development, combines the current situation of artificial intelligence, analyzes in-depth the problems of artificial intelligence course in terms of course content, theoretical teaching, practical teaching and teaching methods, then proposes a teaching reform plan for artificial intelligence course of new engineering oriented to OBE concepts. The reform and practical research of artificial intelligence course teaching is carried out in terms of updating theoretical content continuously, optimizing practical content abidingly, playing students' subjectivity fully, integrating industry, academia, research and application deeply and applying decision tree evaluation methods, etc., so as to provide reference for universities to improve the teaching quality of artificial intelligence course. Through competency assessment system, student final scores, student feedback results and academic competitions achievements, it is shown that the reformed teaching can stimulate students' interest in learning, which not only improves students' independent learning ability and practical hands-on ability, but also helps to improve students' innovation ability and overall qualities. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Identification and Classification of Medicinal Plants using Machine learning Algorithms 3

Identification and Classification of Medicinal Plants using ...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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Design of a university English teaching system based on artificial intelligence technology 2

Design of a university English teaching system based on arti...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Wang, Qi Shandong Vocational College of Science and Technology Shandong Province Weifang261053 China

ISBN: (纸本)9781510689053

The rapid development of artificial intelligence (AI) technology has introduced extensive potential applications in the field of education. Currently, university English teaching often faces challenges due to large class sizes, where teachers find it difficult to provide personalized guidance and feedback tailored to the specific needs of each student, thus hindering improvements in teaching quality and efficiency. To address this issue, this paper designs and develops a university English teaching system based on AI technology. The system employs advanced machine learning algorithms to intelligently assess student assignments, generate personalized learning suggestions, and automatically correct grammar and pronunciation. Its functional modules include learning task analysis, assignment evaluation and feedback, and voice learning assistance, aiming to comprehensively enhance students' learning experience and teachers' instructional efficiency. Experimental results indicate that the system demonstrates robust performance and high stability under high-concurrency user scenarios, with consistent operational behavior at different time points, effectively adapting to the practical teaching demands of higher education environments. This research provides theoretical support and practical reference for the application of AI technology in university English teaching, offering significant contributions to advancing educational informatization. © 2025 SPIE.

关键词： Adversarial machine learning

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