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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier:A Case Study of Seasonal Influenza in Hong Kong

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Current Medical Science 2022年第1期42卷 226-236页

作者： Zi-xiao WANG James NTAMBARA Yan LU Wei DAI Rui-jun MENG Dan-min QIAN Department of Medical Informatics School of MedicineNantong UniversityNantong 226001China Department of Computer Science College of Engineering and Computing SciencesNew York Institute of TechnologyNew York 10023USA Department of Computer Science College of Overseas EducationNanjing University of Posts and TelecommunicationsNanjing 210023China Department of Epidemiology School of Public HealthNantong UniversityNantong 226019China Artificial Intelligence Laboratory Center De Montfort University of LeicesterLeicester LE1 9BHUnited Kingdom

Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

关键词： influenza prediction data-driven Support Vector Machine Discriminant Analysis Ensemble Classifier

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Leaf Disease Segmentation Using Uunet++ Architecture 3rd

Leaf Disease Segmentation Using Uunet++ Architecture

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3rd International Conference on Trends in Electronics and Health informatics, TEHI 2023

作者： Farooqui, Nafees Akhter Mishra, Amit Kumar Ray, Kanad Mallik, Saurav Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Karnataka Bengaluru India Department of Physics Amity School of Applied Sciences Amity University Rajasthan Rajasthan Jaipur India Amity Cognitive Computing and Brain Informatics Centre Amity University Rajasthan Rajasthan303002 India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA02115 United States Pharmacology & Toxicology University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9789819739363

Segmentation of leaves is an initial step in image-based plant phenotyping, which has received more attention recently. Leaf segmentation shows features of the leaf and growth stages at the leaf level. Due to the significant overlapping between leaves and fluctuating environmental circumstances, such as intensity change and blur due to wind, segmentation of plant tissues like leaves is a most important issue in plant phenotyping. The leaf segmentation work has more complicated problems, such as leaf surface, genotypes, length, structure, and thickness variability. This study proposes an ultra-modern deep gaining knowledge of architectures: UUNet++, a convolutional neural network for advance segmentation. A vital assessment is done using computer Vision for Plant Phenotyping dataset (CVPPP). The plant phenotyping is performed on tobacco and Arabidopsis leaf of RGB images from plant village dataset. The proposed model needs less space as compared to the main UNet architecture. In contrast to typical deep learning image sets, this dataset contains a limited number of samples. Despite that, we achieve exceptional results in leaf segmentation, specifically in distinguishing the interior of leaves from the binary segmentation of the entire leaf. Furthermore, studies are necessary to accurately quantify the number of leaves. Without reducing performance measures, the disease classification inferences time is extremely efficient and accurate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Research on optimization strategy of medical data information security and privacy 9

Research on optimization strategy of medical data informatio...

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9th International Conference on Electrical engineering, computer Science and informatics, EECSI 2022

作者： Abdulshaheed, Haider Rasheed Mohammed Al-Juboori, Sura Abdulmunem Al Sayed, Intisar A.M. Barazanchi, Israa Al Gheni, Hassan Muwafaq Jaaz, Zahraa A. Baghdad College of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq Ministry of Higher Education and Scientific Research Baghdad Iraq Ashur University College-Medical Instrumentation Techniques Engineering Baghdad Iraq College of Computing and Informatics UNITEN Malaysia Al-Mustaqbal University College Computer Techniques Engineering Department Hillah 51001 Iraq College of Science-AINahrain University Baghdad College of Computing and Informatics UNITEN Computer Department Malaysia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

Due to the rapid development of information technology and network technology, personal medical data information is increasingly challenged by privacy breaches and security issues at all life cycle stages. In this paper, we analyze the risk patterns of privacy information leakage of personal medical data in the comprehensive lifecycle of medical information in the context of the importance of hospital information system construction and discuss the risk factors of medical data privacy security in hospital information system based on the installation of the vulnerability scanning system, establishment of perfect access control mechanism, installation of the intrusion detection system (IDS) and data backup. The protection strategy of medical data privacy security under the hospital information system is discussed. The change strategy can physically separate the use and storage of patients personal information and use different algorithms to encrypt privacy information hierarchically. The hospital departments protect the security of patients personal privacy information from the source according to the mechanism of hierarchical grading and on-demand access without affecting the hospital business. © 2022 Institute of Advanced engineering and Science (IAES).

关键词： Data security Hospital information system Medical privacy leak Privacy protection Privacy security

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Maylia Fatmawati Mega Novita Nugroho Dwi Saputro Alok Singh Chauhan Noora Qotrun Nada Amit Sinha Bambang Agus Herlambang Shruti Gupta Department of Informatics Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Indonesia Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering ABES Engineering College Ghaziabad Uttar Pradesh India

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks.

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Early Warning Urgent Logistic Disaster in Rural Area based on Classification Algorithm: Indonesian Case 5

Early Warning Urgent Logistic Disaster in Rural Area based o...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2021

作者： Yahya, Muhammad Mabe Parenreng, Jumadi Suhartono Syahid Nur Wahid, M. Saputra, Andika Wahid, Abdul Automotive Engineering Education Department Universitas Negeri Makassar Makassar Indonesia ICT Centre Department Universitas Negeri Makassar Makassar Indonesia STKIP YPUP Makassar Mathematics Education Department Makassar Indonesia Informatics And Computer Engineering Department Universitas Negeri Makassar Makassar Indonesia

ISBN: (纸本)9781665401968

According to the ESCAP report, nearly half of the world's natural disasters occur in the Asia Pacific region, with Indonesia ranking first in terms of the number of victims. In terms of disasters, Indonesia lags in terms of early warning, response, and recovery. In terms of post-disaster management, the issue is the distribution of aid to disaster victims, including uneven logistics distribution, slow distribution, and poor coordination among related agencies. This paper proposes a solution in the form of a disaster logistics management system with data management features for disaster victims' needs, a donation collection system, and distribution management and monitoring of volunteers who will go to disaster sites. This system's decision-making model is based on a smart system that uses sorting data to determine the priority level of the type of assistance needed at the disaster site and the K-Nearest Neighbor (KNN) algorithm to determine the priority of the distribution of aid locations. As a result, command center officers will be able to better coordinate with field teams in making decisions about priority actions that must be taken. Furthermore, this system serves as a public accountability medium for distributing disaster logistics to the government and related officials. © 2021 IEEE.

关键词： Disasters

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Smart Guard Towns: Multi-Drone Systems with Deep Learning for Situational Awareness in Urban Management and Security 10

Smart Guard Towns: Multi-Drone Systems with Deep Learning fo...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Hermanus, Davy Ronald Supangkat, Suhono Harso Hidayat, Fadhil Sasmoko Indrianti, Yasinta Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Bandung Indonesia School of Electric and Informatic Institut Teknologi Bandung Bandung Indonesia School of Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Bina Nusantara University Faculty of Humanities RIG Group in Educational Technology Primary Teacher Education Department Jakarta Indonesia Universitas Agung Podomoro Entrepreneurship Department Jakarta Indonesia

ISBN: (纸本)9798350339543

The design of multi-drone systems with deep learning capabilities in Smart Guard Towns is examined in this research study to improve situational awareness for urban management and security. The study's main objective is to increase real-time understanding and monitoring of the urban environment through the deployment of cutting-edge technologies. The system intends to give comprehensive situational awareness, enabling improved choice-making and resource allocation, by utilizing multiple drones and the power of deep learning algorithms. The research explores the advantages of this method for managing urban environments, boosting security, and addressing the unique difficulties that Smart Guard Towns face. The report also explores possible future advances and ethical issues related to the use of multi-drone systems with deep learning in these situations. With the contribution of this research, lawmakers, urban planners, and technology developers may better apply such systems and progress the development of Smart Guard Towns to create safe and sustainable urban environments. © 2023 IEEE.

关键词： Drones

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Word-Based Turkish Lip Reading System with Two Stream 3dcnn and Bilstm

SSRN

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SSRN 2023年

作者： Erbey, Ali Barışçı, Necaattin Department of Computer Programming Distance Education Vocational School Usak University Usak64200 Turkey Department of Information Systems Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara06500 Turkey

Lip reading is a technique that aims to understand spoken words by analyzing people's lip movements. Deep learning algorithms are used as a powerful tool for detecting and recognizing lip movements more accurately. In this study, we present a lip-reading architecture for the Turkish language. The architecture is trained to recognize Turkish words using 3DCNN (3D Convolutional Neural Network) architecture and two-stream deep learning methods. The model was trained on video data representing lip movements and learned to classify words accurately. The Turkish lip reading system has been trained and tested on a large Turkish lip reading dataset. In the experiments, an accuracy of 87.12% was achieved with the proposed model, indicating successful results in the Turkish language. The proposed model demonstrates high accuracy rates in recognizing Turkish words, showcasing its effectiveness. This paper introduces a novel architecture, a lip-reading system that employs deep learning techniques, providing a robust tool for successful Turkish lipreading applications. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Empowering Smart education through computer Vision and the Internet of Everything (IoE) in Intelligent Smart School Transportation 10

Empowering Smart Education through Computer Vision and the I...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Hermanus, Davy Ronald Supangkat, Suhono Harso Hidayat, Fadhil Sasmoko Indrianti, Yasinta Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Bandung Indonesia School of Electric and Informatic Institut Teknologi Bandung Bandung Indonesia School of Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Research Interest Group in Educational Technology Bina Nusantara University Faculty of Humanities Primary Teacher Education Department Jakarta Indonesia Universitas Agung Podomoro Entrepreneurship Department Jakarta Indonesia

ISBN: (纸本)9798350339543

This paper presents an innovative intelligent school transportation system designed for a smart town within a larger smart city framework. By harnessing the power of computer vision, the Internet of Everything (IoE), and advanced surveillance technologies, the system offers enhanced safety and convenience for students, parents, schools, public transportation, drivers, law enforcement, and the government. The research methodology incorporates qualitative research methods to capture valuable insights from stakeholders and inform the design architecture of the system. The case study focuses on Kota Baru Parahyangan City, where Cahaya Bangsa Classical School serves as an exemplary institution. Through the integration of computer vision techniques, such as facial recognition and object detection, the system enables real-time monitoring and tracking of students during their transportation journey. The IoE integration ensures seamless connectivity, enabling efficient communication and data exchange among surveillance cameras, central monitoring systems, and IoT-enabled vehicles. The proposed intelligent school transportation system offers not only improved safety but also optimized traffic management and resource allocation. The findings of this study contribute to the broader field of smart city development, specifically in the context of intelligent transportation systems. © 2023 IEEE.

关键词： Object detection

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Retraction Note: Resnet-based deep learning multilayer fault detection model-based fault diagnosis

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Multimedia Tools and Applications 2024年第38期83卷 86323-86324页

作者： Jaber, Mustafa Musa Ali, Mohammed Hasan Abd, Sura Khalil Jassim, Mustafa Mohammed Alkhayyat, Ahmed Majid, Mohammed Sh. Alkhuwaylidee, Ahmed Rashid Alyousif, Shahad Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Najaf Iraq Directorate of Research and development Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq College of Technical Engineering The Islamic University Najaf Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Technical Engineering Mazaya University College Thi Qar Iraq Department of Electrical and Electronic Engineering College of Engineering Gulf University Sanad Kingdom of Bahrain Research Centre University of Mashreq Baghdad Iraq

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