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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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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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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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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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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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Cryptography Innovations for Securing Data in the Quantum computing Era: Integrating Machine learning for Enhanced Security

Cryptography Innovations for Securing Data in the Quantum Co...

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2025 international conference on Computer, Electrical and Communication engineering, ICCECE 2025

作者： Temara, Sheetal Bhagyalakshmi, L. Suman, Sanjay Kumar Shakunthala, M. Chitra, N. Thulasi Golla, Kishor University of the Cumberlands Department of Computer and Information Sciences United States Rajalakshhmi Engineering College Dept. of ECE TN Chennai India St. Martin's Engineering College Dept. of Ece and Dean R&d Telangana Secunderabad India R.M.D. Engineering College Dept. of ECE TN Thiruvallur India Mlr Institute of Technology Dept. of CSE Hyderabad India St. Martin's Engineering College Dept. of CSE Telangana Secunderabad India

ISBN: (纸本)9798331513894

The rise of quantum computing introduces substantial risks to traditional cryptographic protocols, which are vulnerable to quantum decryption methods such as Shor's algorithm. To address these emerging threats, this paper proposes an innovative cryptographic framework that integrates machine learning (ML) techniques to enhance data security in the quantum computing era. Our approach leverages ML-driven anomaly detection, adaptive key management, and predictive analytics to create a flexible and resilient cryptographic defense. The system's anomaly detection module utilizes neural networks to identify potential quantum-based decryption attempts, while reinforcement learning optimizes key generation and distribution in response to detected threats. Experimental results demonstrate that the proposed ML-augmented framework significantly improves anomaly detection accuracy and reduces vulnerability to quantum decryption attempts by dynamically adjusting cryptographic parameters. These findings underscore the potential of machine learning to strengthen cryptographic systems, making them adaptable to the advanced threats posed by quantum computing. © 2025 IEEE.

关键词： Predictive analytics

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Fast reinforcement learning 2nd

Fast reinforcement learning

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2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Sahni, Sartaj University of Florida United States

ISBN: (纸本)9781032738659

Reinforcement learning (RL) is a highly effective approach for teaching intelligent agents to make decisions in challenging and ever-changing contexts. With the increasing use of RL in real-world situations, there is a rising demand for rapid reinforcement learning to improve effectiveness, decrease training duration, and facilitate immediate decision making across many fields. Fast Reinforcement learning can be achieved through various methods, one of which is Parallelization. This involves splitting RL computations across numerous processors or GPUs to take advantage of parallel processing capabilities, resulting in a large reduction in training time. Another method involves utilizing specialized hardware such as GPUs and TPUs that can enhance the speed of RL training by executing parallel computations more effectively than conventional CPUs. Developing and executing RL algorithms that are designed to be more efficient and have lower computational complexity leads to quicker learning. Cache memory is also essential in accelerating reinforcement learning by minimizing the access time for frequently utilized data. It enhances the performance of RL algorithms by storing and retrieving crucial information, such as state-action pairs and Qvalues. Cache memory usage optimizes data access patterns, resulting in reduced latency and improved speed of learning rounds. The landscape of fast reinforcement learning will be shaped by overcoming challenges such as transfer learning and meta-learning, algorithmic complexity, scalability to handle data sets of complex environments, generalization, etc. will continue to shape the landscape of fast reinforcement learning. © 2025 The Author(s).

关键词： Reinforcement learning

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A Comprehensive Review of Soft computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead 5th

A Comprehensive Review of Soft Computing Enabled Techniques ...

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5th international conference on Data Science, Machine learning and Applications

作者： Parabrahmachari, Sriram Narayanasamy, Srinivasan Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India Rajalakshmi Engn Coll Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In the past ten years, the Internet of Things (IoT) has become a massive force in our daily lives, providing countless smart services that have improved human existence. Nevertheless, the increased availability and rapidly escalating demand for intelligent devices and networks mean that IoT is currently encountering unprecedented security challenges. While there are established security protocols in place for IoT, these conventional methods fall short in effectively addressing the escalating frequency and severity of attacks. The term 'security' is used to encompass various concepts, including integrity, confidentiality, and privacy. Therefore, a strong and up-to-date security system is necessary for the next generation of industrial IoT. One area of technological advancement that can address the ongoing and future challenges of IoT security is Soft computing. It has opened up many possible research avenues for detecting attacks and identifying abnormal behaviors in smart devices and networks. This paper explores the architecture of IoT and provides a comprehensive literature review of Soft computing based approaches to IoT security, including the different types of possible attacks. It also presents potential Soft computing-based solutions for IoT security and discusses open research issues along with future research scope.

关键词： Internet of Things (IoT) Security and privacy Soft computing Attacks computing methodologies

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