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A systematic literature review to implement large language model in higher education: issues and solutions

Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

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Text Classification Accuracy Enhancement Using Deep Neural Networks

Text Classification Accuracy Enhancement Using Deep Neural N...

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2023 Al-Sadiq International Conference on Communication and Information technology, AICCIT 2023

作者： Najm, Mobajjal Kadhim Aljumaily, Mustafa S. Al-Khaleefa, Ahmed Ayal, A.M. Mohammed, Dheyaa Abdulameer Challoob, Aqeel Luaibi Shahid Chamran University of Ahvaz Faculty of Engineering Computer Engineering Department Ahvaz Iran University of Tennessee Electrical Engineering and Computer Science Department Tennessee United States University of Misan Faculty of Education Department of Physics Maysan Iraq University of Misan Maysan Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq Department of Computer Techniques Engineering1 Baghdad Iraq

ISBN: (纸本)9798350341881

Machine-learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), using object and learning approaches, performs well in statistical tasks, such as text classification or sentiment mining. Currently, in the field of natural language processing (NLP), various models, methods, and architectures have been proposed and a wide range of NLP tasks have been developed. Most natural language processing (NLP) problems can be formulated as classification problems (decide on the class of this object depending on the object and its context). In this field, machine learning methods and especially using neural network models are the most used. In this research, we propose a deep learning architecture for text classification that can influence model performance. The basic steps of this proposed method include text pre-processing (including sentence segmentation, punctuation, rooting, speech tagging, and vector conversion), text feature extraction, and LSTM algorithm-based classification model construction. The dataset used in this study is 20NewsGroup and IMDB. The evaluation results are provided in the form of MSE, Precision, Recall, Accuracy, and F-Measure evaluation criteria. © 2023 IEEE.

关键词： Neural network models

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Digitally-defined ultrathin transparent wireless sensor network for room-scale imperceptible ambient intelligence

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npj Flexible Electronics 2024年第1期8卷 869-878页

作者： Yunxia Jin Mengxia Yu Dat T.Nguyen Xin Yang Zhipeng Li Ze Xiong Chenhui Li Yuxin Liu Yong Lin Kong John S.Ho Institute for Health Innovation and Technology National University of SingaporeSingapore 117599Singapore Department of Biomedical Engineering National University of SingaporeSingapore 119276Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Integrative Sciences and Engineering Programme National University of Singapore Graduate SchoolSingapore 119077Singapore The N.1 Institute for Health National University of SingaporeSingapore 117456Singapore Institute of Materials Research and Engineering Agency for Science Technology and ResearchSingapore 138634Singapore Department of Mechanical Engineering University of UtahSalt Lake CityUT 84112USA

Wireless and battery-free radio-frequency(RF)sensors can be used to create physical spaces that ambiently sense and respond to human *** such sensors ultra-flexible and transparent is important to preserve the aesthetics of living environments,accommodate daily activities,and functionally integrate with ***,existing RF sensors are unable to simultaneously achieve high transparency,flexibility,and the electrical conductivity required for remote room-scale ***,we report 4.5μm RF tag sensors achieving transparency exceeding 90%that provide capabilities in room-scale ambient wireless *** develop a laser-assisted water-based adhesion-reversion process to digitally realize computer-aided RF design at *** individually tagging multiple objects and regions of the human body,we demonstrate multiplexed wireless tracking of humanenvironment interactions and physiological signals at a range of up to *** radio-frequency identification sensors open opportunities for non-intrusive wireless sensing of daily living spaces for applications in health monitoring and elderly care.

关键词： network ambient transparent

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3D magnetic field vector measurement by magneto-optical imaging

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Journal of the Magnetics Society of Japan 2022年第2期46卷 37-41页

作者： Sakaguchi, H. Wada, R. Oya. S. Matsumura, T. Saito, H. Ishibashi, T. Department of Materials Science and Technology Nagaoka University of Technology 1603-1 Kamitomioka Niigata Nagaoka940-2188 Japan Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita University 1-1 Tegata Gakuen-machi Akita010-8502 Japan

Magneto-optical (MO) imaging using MO imaging plates is a magnetic imaging technique that enables real-time measurements and is expected to be used for non-destructive testing and for observing the magnetic domains of magnetic materials. In this study, we propose a quantitative measurement method for three dimensional (3D) magnetic field vector measurements. The x-and y-components of the magnetic fields within the measured plane are calculated from the measured z-component by using a signal transformation method based on magnetic field transfer functions. Furthermore, the magnetic field distributions at different heights are also obtained from a one-shot image. In this paper, 3D magnetic field vector measurements are demonstrated for ferrite magnets and an electrical steel sheet. © 2022, Magnetics Society of Japan. All rights reserved.

关键词： Magnetic fields

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Prediction of Power Consumption and Indoor Ambient Conditions by Artificial Neural Network and Long Short-Term Memory 6th

Prediction of Power Consumption and Indoor Ambient Condition...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Intaraumnauy, Tosapon Pimavilai, Nattavit Sirisumrannukul, Somporn Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s Univerity of Technology North Bangkok 1518 Pracharat 1 Rd. Wongsawang Bangsue Bangkok Thailand

ISBN: (纸本)9783031501500

A data analysis model is proposed to predict the indoor ambient conditions and the power consumption of an air conditioner from the information obtained from air conditioner adjustment and ambient conditions. The ambient and usage data were collected from sensors and internet of things (IoT) devices installed in an occupied home. An artificial neural network (ANN) has been developed to predict the power consumption of the air conditioner after adjusting the air conditioner’s target temperature, fan mode setting, and ambient conditions. In addition, a long short-term memory (LSTM) has been developed to predict the indoor temperature and the humidity of the air conditioner when it is controlled. With the information on the upcoming indoor ambient conditions that are expected to occur, the data analysis model can also indicate the maximum energy savings while satisfying the constraint of the resident’s comfort. The experimental results demonstrate that the proposed data analysis model can reliably predict the ambient conditions in the air-conditioned room and the power consumption of the air conditioner from the air-conditioning adjustment and ambient, giving the best-operating settings in any different environment scenarios and therefore shows potential for home energy savings for smart home applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Prototype Development of Electric Vehicle Database Platform Using Serverless and Microservice Architecture with Intelligent Data Analytics 6th

Prototype Development of Electric Vehicle Database Platform ...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Sirisumrannukul, Somporn Piamvilai, Nattavit Limprapassorn, Sirawich Wattanakitkarn, Tanachot Loedchayakan, Touchakorn Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Pracharat 1 Rd. Wongsawang Bangsue Bangkok1518 Thailand

ISBN: (纸本)9783031503290

To fully realize the benefits of EVs, this paper proposes a prototype platform that can perform essential functions as an EV data center for storing and centralizing the data acquired from EVs and chargers directly. Using serverless and microservice architecture, this platform is purposely designed and built for scalability and includes intelligent data analytics capabilities with two different tasks: calculation of CO2 emission, and intraday spatial charging demand analysis. The results confirm that the communication between EVs, chargers, and the platform using the REST API protocol satisfies the response time of the Open Charge Point Protocol (OCPP) 2.0.1 standard, and the backend performance is flexible in terms of scalability and database optimization to accommodate the number of EVs. Given real-time data, the data analytics functions can display CO2 emissions, and predict an intraday power demand. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data Analytics

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Robust Hybrid Transceiver Designs for Linear Decentralized Estimation in mmWave MIMO IoT Networks in the Face of Imperfect CSI

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IEEE Internet of Things Journal 2023年第20期10卷 18125-18139页

作者： Maity, Priyanka Rajput, Kunwar Pritiraj Srivastava, Suraj Venkategowda, Naveen K. D. Jagannatham, Aditya K. Hanzo, Lajos Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Kirchberg1855 Luxembourg Linköping University Department of Science and Technology Norrköping60174 Sweden University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Hybrid transceivers are designed for linear decentralized estimation (LDE) in a mmWave multiple-input-multiple-output (MIMO) IoT network (IoTNe). For a noiseless fusion center (FC), it is demonstrated that the mean squared error (MSE) performance is determined by the number of RF chains used at each IoT node (IoTNo). Next, the minimum-MSE RF transmit precoders (TPCs) and receiver combiner (RC) matrices are designed for this setup using the dominant array response vectors, and subsequently, a closed-form expression is obtained for the baseband (BB) TPC at each IoTNo using Cauchy's interlacing theorem. For a realistic noisy FC, it is shown that the resultant MSE minimization problem is nonconvex. To address this challenge, a block-coordinate descent-based iterative scheme is proposed to obtain the fully digital TPC and RC matrices followed by the simultaneous orthogonal matching pursuit (SOMP) technique for decomposing the fully digital transceiver into its corresponding RF and BB components. A theoretical proof of the convergence is also presented for the proposed iterative design procedure. Furthermore, robust hybrid transceiver designs are also derived for a practical scenario in the face of channel state information (CSI) uncertainty. The centralized MMSE lower bound has also been derived that benchmarks the performance of the proposed LDE schemes. Finally, our numerical results characterize the performance of the proposed transceivers as well as corroborate our various analytical propositions. © 2014 IEEE.

关键词： Wireless sensor networks

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Identification and Visualization of Spatial and Temporal Trends in Textile Industry

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computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed Noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text *** to these enormous text corpora,we are drowning in data and starving for ***,recent research employed different text mining approaches to extract information from this text *** proposed approaches extract meaningful and precise phrases that effectively describe the text’s *** extracted phrases are commonly termed ***,these key phrases are employed to determine the different fields of study ***,these key phrases can also be used to determine the spatiotemporal trends in the various research *** this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend ***,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific *** study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research *** data was collected from various open access ***,this research determined the spatiotemporal trends using quality *** research also focused on finding the research collaboration of different countries in a particular research *** research collaborations of other countries’researchers show the impact on import and export of those *** visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

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Monolithically Integrated 940 nm Half VCSELs on Bulk Ge Substrates

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IEEE Photonics technology Letters 2024年第3期36卷 203-206页

作者： Zhao, Yunlong Wan, Zeyu Guo, Jia Yang, Yun-Cheng Cheng, Hao-Tien Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui Department of Materials Engineering VancouverV6T 1Z4 Canada Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada Freiburg im Breisgau79110 Germany Graduate Institute of Photonics and Optoelectronics Graduate School of Advanced Technology National Taiwan University Taipei10617 Taiwan

High-quality n-type AlGaAs distributed Bragg reflectors (DBRs) and lnGaAs multiple quantum wells (MQWs) were successfully monolithically grown on 4-inch off-cut Ge (100) wafers. Even without any design and process optimization for the Ge substrates, the Ge-based half VCSELs have photoluminescence and reflectance spectra comparable to those grown on conventional GaAs wafers. Flat and sub-nm RMS surface roughness and uniform DBR and MQW growth across the wafer were achieved. These results strongly support full VCSEL growth and fabrication on larger-area Ge wafers for the mass production of AlGaAs-based VCSELs. © 1989-2012 IEEE.

关键词： Surface emitting lasers

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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