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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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Enhancing Wireless Mesh Network Performance Through AI-Based Routing Algorithms 2

Enhancing Wireless Mesh Network Performance Through AI-Based...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Senthilvel, P. Gururama Thirupurasundari, D.R. Rajesh Kumar, K. Palani, Hari Kumar Sundravadivelu, K. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering TamilNadu Chennai India Madha Engineering College Kundrathur Department of Electronics and Communication Engineering TamilNadu Chennai India School of Engineering Ajeenkya D y Patil University India School of Information Technology Madurai Kamaraj University Department of Computer Science Tamil Nadu Madurai India

ISBN: (纸本)9798350369960

Wireless Mesh Networks (WMNs) are critical in ensuring ubiquitous internet coverage, particularly in environments and terrains with unique challenges. The increasing number of wireless devices and services has created a critical need for routing techniques that are more efficient and adaptable. Although conventional routing algorithms exhibit resilience, they frequently remain fixed and fail to adapt to swift fluctuations in network circumstances. By incorporating Artificial Intelligence (AI)-based routing algorithms, this article proposes a novel method for improving the efficacy of WMNs. We present an innovative AI-driven routing algorithm that dynamically adapts to fluctuating network conditions by leveraging reinforcement learning and deep learning techniques. Our methodology considers various elements, including interference, node dependability, bandwidth consumption, and node mobility. In contrast to conventional approaches, the algorithm powered by artificial intelligence perpetually acquires knowledge from its surroundings, forecasting possible breakdowns or congestion, and subsequently redirects data packets along a more efficient trajectory. Experiments performed on simulated and real-world WMN scenarios revealed significant enhancements in packet delivery ratio, latency, and throughput compared to traditional routing methods. In addition, our AI model's adaptability guarantees the network's resistance to external disturbances and its capacity to expand to accommodate an increasing number of nodes. The study's results highlight the capacity of artificial intelligence to fundamentally transform the domain of wireless networking. Incorporating intelligent algorithms into wide-area networks (WMNs) infrastructure can guarantee end-users a more streamlined, effective, and resilient internet experience. Further investigations are warranted to determine whether the proposed method can be implemented in alternative wireless architectures and to enhance the algorit

关键词： AI-based routing deep learning network performance reinforcement learning Wireless Mesh Network

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DB-DCAFN:dual-branch deformable cross-attention fusion network for bacterial segmentation

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 155-170页

作者： Jingkun Wang Xinyu Ma Long Cao Yilin Leng Zeyi Li Zihan Cheng Yuzhu Cao Xiaoping Huang Jian Zheng School of Biomedical Engineering(Suzhou) Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230026China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of SciencesSuzhou 215163China Department of Infectious Diseases the First Affiliated Hospital of Soochow UniversitySuzhou 215006China Institute of Biomedical Engineering School of Communication and Information EngineeringShanghai UniversityShanghai 200444China College of Computer and Information Hohai UniversityNanjing 210098China School of Electronic and Information Engineering Changchun University of Science and TechnologyChangchun 130022China Jinan Guoke Medical Technology Development Co. LtdJinan 250101China

Sputum smear tests are critical for the diagnosis of respiratory diseases. Automatic segmentation of bacteria from spu-tum smear images is important for improving diagnostic efficiency. However, this remains a challenging task owing to the high interclass similarity among different categories of bacteria and the low contrast of the bacterial edges. To explore more levels of global pattern features to promote the distinguishing ability of bacterial categories and main-tain sufficient local fine-grained features to ensure accurate localization of ambiguous bacteria simultaneously, we propose a novel dual-branch deformable cross-attention fusion network (DB-DCAFN) for accurate bacterial segmen-tation. Specifically, we first designed a dual-branch encoder consisting of multiple convolution and transformer blocks in parallel to simultaneously extract multilevel local and global features. We then designed a sparse and deformable cross-attention module to capture the semantic dependencies between local and global features, which can bridge the semantic gap and fuse features effectively. Furthermore, we designed a feature assignment fusion module to enhance meaningful features using an adaptive feature weighting strategy to obtain more accurate segmentation. We conducted extensive experiments to evaluate the effectiveness of DB-DCAFN on a clinical dataset comprising three bacterial categories: Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The experi-mental results demonstrate that the proposed DB-DCAFN outperforms other state-of-the-art methods and is effective at segmenting bacteria from sputum smear images.

关键词： Bacterial segmentation Dual-branch parallel encoder Deformable cross-attention module Feature assignment fusion module

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Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1877-1890页

作者： Mamta Brij B.Gupta Kuan-Ching Li Victor C.M.Leun Kostas E.Psannis Shingo Yamaguchi IEEE the Department of Computer Engineering National Institute of Technology the Asia University the Macquarie University the Department of Computer Science and Information Engineering Providence University the Computer Science and Software Engineering Shenzhen University the University of British Columbia the Department of Applied Informatics University of Macedonia the Graduate School of Sciences and Technology for Innovation Yamaguchi University

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret

关键词： Cloud-based cyber-physical systems(CCPS) data encryption healthcare information search and retrieval keyword search public-key cryptosystems searchable encryption

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A Fused Machine Learning Approach for Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Proceedings of the First International Workshop of Semantic Digital Humanities co-located with the Extended Semantic Web Conference 2024 1

Proceedings of the First International Workshop of Semantic ...

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1st International Workshop of Semantic Digital Humanities, SemDH 2024

作者： Bruns, Oleksandra Poltronieri, Andrea Stork, Lise Tietz, Tabea FIZ Karlsruhe Leibniz Institute for Information Infrastructure Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Kaiserstr. 89 Karlsruhe76133 Germany Department of Computer Science and Engineering University of Bologna Mura Anteo Zamboni 7 Bologna40126 Italy Informatics Institute University of Amsterdam Science Park 900 Amsterdam1098 XH Netherlands

Exploration, analysis, and preservation of the rich cultural and historical tapestry of the world are essential for our understanding of humanity’s past and shaping our future. In recent years, there has been increased interest in the creation and application of Ontologies, Knowledge Graphs, and other Semantic Web Technologies within cultural heritage (CH) and digital humanities (DH). However, to date, the distinct areas of expertise, methodologies and traditions across the fields have led to a noticeable gap between tech solutions and humanities’ needs. The aim of the International Workshop of Semantic Digital Humanities (SemDH) was to bridge this division and encourage closer collaboration and networking across diverse fields. © 2024 Copyright for this paper by its authors.

关键词： Ontology

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Evaluating Obesity and Metabolic Syndrome Risk: A Cloud-Driven Logistic Regression Framework 2

Evaluating Obesity and Metabolic Syndrome Risk: A Cloud-Driv...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Saravanan, K. Prabagar, S. Choudri, Subramani Roy Mishra, Suman Niranjansimha, B. Malathi, N. R.M.D. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai Chennai India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Karnataka Bangalore India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India Cmr Engineering College Department of Electronics & Communication Engineering Hyderabad India Mother Theresa Institute of Engineering and Technology Department of Mechanical Engineering Andhra Pradesh Palamaner India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Worldwide, people are struggling with obesity and metabolic syndrome. For successful preventative interventions, it is necessary to predict and evaluate the risk factors linked to these illnesses. For the purpose of determining the potential of metabolic syndrome and obesity, this research presents a new cloud-driven logistic regression paradigm. Using the power of the cloud, we mined a massive dataset that included a wide range of demographic and clinical details. The cloud platform was used to build logistic regression models that analyzed intricate relationships among variables using powerful machine learning methods. A thorough assessment of risk variables is made possible by the framework's incorporation of data from many sources. Predicting the likelihood of obesity and metabolic syndrome was a strong suit of our cloud-based logistic regression methodology. Important factors, such as genetic susceptibility, lifestyle variables, and clinical biomarkers, were discovered by the model. The ability to efficiently handle parallel data sets on the cloud improved the model's predictive powers and made it possible to analyze massive datasets. An efficient and scalable method for large-scale evaluation of obesity and metabolic syndrome risk is provided by the suggested framework. The model may be easily adjusted to new trends and changing datasets using cloud-driven analytics, allowing real-time updates and enhancements. This study provides a powerful instrument for risk assessment and focused preventative actions, which advances public health initiatives and personalized treatment. © 2024 IEEE.

关键词： Risk assessment

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Protecting Vulnerable Road users using IoT-CNN for Safety Measures 5

Protecting Vulnerable Road users using IoT-CNN for Safety Me...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Karpagalakshmi, R.C. Sampoornam, M. Maria Vinya, Viyyapu Lokeshwari Monikapreethi, S.K. Yuvaraj, S. Srinivasan, C. Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Ranga Reddy India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

The increasing number of road accidents involving Vulnerable Road Users (VRUs) highlights the urgent need for innovative safety solutions. This study proposes a novel approach that leverages IoT and Convolutional Neural Network (CNN) technologies to enhance VRU safety. By deploying IoT sensors on vehicles and infrastructure, the proposed model can accurately track VRU locations and environmental conditions in real-time. A CNN-based model is developed to robustly detect and classify VRUs under various traffic scenarios, with continuous improvement through learning. The integration of IoT and CNN enables real-time communication and timely warnings, facilitating proactive interventions to prevent accidents. Experimental evaluations demonstrate the effectiveness of the proposed system in improving VRU safety. This research offers a promising solution for reducing road accidents and creating safer urban environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning for Automated Classification of Kidney Lesions in Chronic Kidney Disease Patients 2

Deep Learning for Automated Classification of Kidney Lesions...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Lakshmi, B. Seetha Anand, Vijay Omotunde, Habeeb Birundadevi, M. Mahalakshmi, S. Brahmam, Madala Guru Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Namakkal India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University Department of Information Systems Riyadh Saudi Arabia Sri Sairam Siddha Medical College and Research Centre Department of Maruthuvam Chennai India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India

ISBN: (纸本)9798350317558

Deep Learning was utilized to autonomously classify renal abnormalities, which improved the diagnostic accuracy for individuals with chronic kidney disease (CKD), according to this study. During training on a meticulously selected dataset, the proposed model outperformed the most recent approaches. By utilizing medical imaging data - specifically quantitative and qualitative data - to differentiate between discrete kidney abnormalities, the model demonstrated exceptional levels of accuracy, precision, and recall. Furthermore, apart from effectively resolving ethical considerations pertaining to data privacy, this innovative methodology has the potential to fundamentally transform the existing landscape of chronic kidney disease (CKD) diagnosis and treatment. The results demonstrate that the model effectively manages intricate renal disorders, signifying a noteworthy progression in the field of medical image processing. This research paves the way for additional fruitful lines of inquiry, which ought to enhance nephrology diagnostics and patient care. © 2024 IEEE.

关键词： Automated Classification Chronic Kidney Disease Deep Learning Diagnostic Accuracy Kidney Lesions medical Image Analysis

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Enterprise Architecture Trends in the Digital Transformation Era

Enterprise Architecture Trends in the Digital Transformation...

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2023 International Seminar on Application for Technology of information and Communication, iSemantic 2023

作者： Mahendra, Irfan Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Computer Science Jakarta11480 Indonesia Nusa Mandiri University Faculty of Information Technology Jakarta11450 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40116 Indonesia University of Indonesia Faculty of Computer Science Jakarta16424 Indonesia

ISBN: (纸本)9798350339215

Enterprise Architecture (EA) is a solution to build alignment between business strategy and information technology in dealing with digital transformation that causes fundamental changes for companies and businesses. The purpose of this study is to examine the trends and progress of EA in responding to the challenges of the digital transformation era, which is carried out through SLRs using the PRISMA method. According to the research findings, implementing EA in businesses serves the following purposes: establishing clear objectives and strategies for the development of business and technology, achieving harmony between business and information technology, time and cost efficiency, and effectiveness in business development processes and information technology. Then, it was concluded that there are nine components of EA, namely, vision, mission, and strategic objectives;business architecture;data & information architecture;information system architecture;infrastructure and network architecture;security architecture;human resources architecture;governance, legal, and compliance;and standards & regulations. © 2023 IEEE.

关键词： Network architecture

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