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Distributed intelligence for IoT-based smart cities: a survey

Neural computing and Applications 2024年第27期36卷 16621-16656页

作者： Hashem, Ibrahim Abaker Siddiqa, Aisha Alaba, Fadele Ayotunde Bilal, Muhammad Alhashmi, Saadat Mehmood Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science Federal College of Education Zaria Kaduna Tudun Wada810282 Nigeria Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32610 United States Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smart city

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Palm Tree Diseases Detection Using Deep Learning: A Short Review 8

Palm Tree Diseases Detection Using Deep Learning: A Short Re...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Bouthaina, Rechachi Rezeg, Khaled Okba, Kazar Merizig, Abdelhak Mohamed Khider University LINFI Laboratory Biskra Algeria Sharjah University Computing and Informatics College Arts Sciences & Information Technology College Kalba United Arab Emirates

ISBN: (纸本)9798350309249

Agriculture is a sector that represents a real indicator of the country's development, and it is a sector that affects national security. The palm tree is a significant, affordable, and nutrient-rich food source that gives millions of people access to wholesome food globally. It can get a lot of illnesses, much like other plants. DL has been effectively implemented in agriculture and has been employed in many other industries. This paper examines the latest advancements in the use of deep learning (DL) for the diagnosis of palm tree diseases. Particularly Convolutional Neural Networks (CNN) are often utilised in agricultural research due to its strong image processing ability. © 2024 IEEE.

关键词： Precision agriculture

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Enabling Knowledge Management Practices in Museums: The Benefits of and Barriers to Achieving Public Value 18th

Enabling Knowledge Management Practices in Museums: The Bene...

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18th International Conference on Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, iConference 2023

作者： Vakharia, Neville Poole, Alex H. Department of Arts and Entertainment Enterprise College of Media Arts and Design Drexel University PhiladelphiaPA19104 United States Department of Information Science College of Computing and Informatics Drexel University PhiladelphiaPA19104 United States

ISBN: (纸本)9783031280313

This qualitative case study explores a new approach to understanding knowledge management in a specific type of information organization: museums. We adapt the broader constructs of knowledge management enablers (KMEs) and a knowledge management orientation (KMO) to study how museums augment their public value through inclusive and equitable community engagement. Semistructured interviews with 45 senior leaders of art and science museums teased out 26 beneficial practices and barriers related to knowledge management enablers and a knowledge management orientation. We found that museums who exhibited these beneficial practices were more successful in creating public value than those who faced barriers in doing so. This critical link between enabling knowledge management practices and public value both opens new research opportunities and suggests practical implications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge management

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A Review on Deep Learning Techniques for EEG-Based Driver Drowsiness Detection Systems

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Informatica (Slovenia) 2024年第3期48卷 359-378页

作者： Latreche, Imene Slatnia, Sihem Kazar, Okba Barka, Ezedin Harous, Saad Department of Computer Science University of Mohamed Khider Biskra Algeria College of Arts Sciences & Information Technology University of Kalba Sharjah United Arab Emirates Department of Information Systems and Security UAE University Al Ain United Arab Emirates Computer Science Department College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Driver Drowsiness is considered one of the significant causes of road accidents and fatal injuries. Due to this, creating systems that can monitor drivers and detect early drowsiness has become an important field of research and a challenging task in recent years. Several research attempts were proposed to solve this problem based on several approaches and techniques. The Electroencephalogram (EEG) is one of the most efficient and reliable method, among the physiological signals-based monitoring approaches. In this area, many Machine Learning (ML) techniques have been used to detect EEG-based driver drowsiness. However, due to the limitations of ML techniques, many researchers have shifted their focus to the use of deep learning (DL) techniques, which have demonstrated superior performance in many fields including the physiological signals classification tasks. This paper reviews and discusses numerous new research papers that have proposed and implemented driver drowsiness detection systems based on EEG and deep learning techniques. In addition, we have outlined the limitations and difficulties of the existing works and highlighted and proposed some propositions that will help future field researchers enhance and generalize the results. Based on our thorough analysis, we have determined that the latest advancements in detecting driver drowsiness have employed the convolutional neural network (CNN) technique, which has demonstrated effective performance in classifying signals. Furthermore, the primary issue encountered in all works is developing a more precise and accurate method. Nevertheless, we seek a precise system capable of swiftly identifying a state of drowsiness while using minimal spatial memory and processing resources. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Electroencephalography

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AI Technology in IT Service Desk (ITSD) 7

AI Technology in IT Service Desk (ITSD)

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Karahamo, Mohamed Alden, Feras Zen Nour Hindia, M.H.D. Daud, Ali Yusny Deprtment of IT Schlumberger Oilfield UK Limited London United Kingdom College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia School of Computing College of Arts and Sciences Universiti Utara Malaysia Sintok Kedah Malaysia

ISBN: (纸本)9798331519513

This research assesses the impact of applying AI technologies to address IT Service Desk (ITSD) problems. The necessity of this study arises with the increasing dependency on technology in recent years. IT Service Desk plays a crucial role in providing remote IT support to the businesses’ end-users regardless of their location. The high demand for the IT Service Desk resulted in the need for additional IT resources to manage the increasing number of IT requests, which could increase the IT support activities. In many cases, increasing the number of IT agents is not adequate, which leads to quality degradation caused by delayed or inaccurate resolutions provided by the IT agents. The main objectives of this study are to show how the adoption of AI technologies may improve the effectiveness of IT services. Also, this study aims to evaluate the challenges on the implementation of AI solutions within the IT Service Desk by highlighting the future potential of AI solutions in the IT support world. © 2024 IEEE.

关键词： Natural language processing systems

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Blockchain-Based Ensemble Deep Learning Model Technique for Breast Cancer Classification 2

Blockchain-Based Ensemble Deep Learning Model Technique for ...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Rahal, Hakima Rym Slatnia, Sihem Kazar, Okba Harous, Saad University of Mohammed Khider Linfi Laboratory Computer Science Department Biskra Algeria College of Arts Sciences & Information Technology University of Kalba Sharjah United Arab Emirates College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331542573

Breast cancer is a condition brought on by the uncontrollable growth and multiplication of aberrant cells in the breast tissue. The misdiagnosis of breast cancer can result in needless or delayed therapies, mental and physical health, increased anxiety and stress, and decreased survival rates for patients. Also, the exchange of healthcare data is difficult to secure due to strict privacy laws, online threats, and the possibility of human error. Blockchain technology can enhance the security and privacy of patient data, give patients greater control over their health data, and facilitate safe data exchange between healthcare providers. In this study, we propose a Blockchain-based Ensemble Deep Learning Model method that can classify Breast Cancer either Malignant or Benign. This method is built on combining different breast cancer classification ANN models and sharing them through blockchain to use their weights to train and optimize the performance of a final model. This model achieved 98% accuracy, and showed a promising in protecting patients from fatal diagnosis mistakes. © 2024 IEEE.

关键词： Differential privacy

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Understanding Safety Risks and Safety Design in Social VR Environments

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Proceedings of the ACM on Human-Computer Interaction 2023年第CSCW1期7卷 1-37页

作者： Zheng, Qingxiao Xu, Shengyang Wang, Lingqing Tang, Yiliu Salvi, Rohan C. Freeman, Guo Huang, Yun School of Information Sciences University of Illinois Urbana-Champaign United States College of Fine and Applied Arts University of Illinois Urbana-Champaign United States Department of Industrial Engineering Tsinghua University China School of Informatics University of Illinois Urbana-Champaign United States School of Computing Clemson University Clemson United States

Understanding emerging safety risks in nuanced social VR spaces and how existing safety features are used is crucial for the future development of safe and inclusive 3D social worlds. Prior research on safety risks in social VR is mainly based on interview or survey data about social VR users' experiences and opinions, which lacks "in-situ observations"of how individuals react to these risks. Using two empirical studies, this paper seeks to understand safety risks and safety design in social VR. In Study 1, we investigated 212 YouTube videos and their transcripts that document social VR users' immediate experiences of safety risks as victims, attackers, or bystanders. We also analyzed spectators' reactions to these risks shown in comments to the videos. In Study 2, we summarized 13 safety features across various social VR platforms and mapped how each existing safety feature in social VR can mitigate the risks identified in Study 1. Based on the uniqueness of social VR interaction dynamics and users' multi-modal simulated reactions, we call for further re-thinking and re-approaching safety designs for future social VR environments and propose potential design implications for future safety protection mechanisms in social VR. © 2023 ACM.

关键词： Virtual reality

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Palm Tree Diseases Detection Using Deep Learning: A Short Review

Palm Tree Diseases Detection Using Deep Learning: A Short Re...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Rechachi Bouthaina Rezeg Khaled Kazar Okba Merizig Abdelhak LINFI Laboratory Mohamed Khider University Biskra Algeria Computing and Informatics College Arts Sciences & Information Technology College Sharjah University Kalba UAE

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Agriculture is a sector that represents a real indicator of the country’s development, and it is a sector that affects national security. The palm tree is a significant, affordable, and nutrient-rich food source that gives millions of people access to wholesome food globally. It can get a lot of illnesses, much like other plants. DL has been effectively implemented in agriculture and has been employed in many other industries. This paper examines the latest advancements in the use of deep learning (DL) for the diagnosis of palm tree diseases. Particularly Convolutional Neural Networks (CNN) are often utilised in agricultural research due to its strong image processing ability.

关键词： Deep learning Smart agriculture Reviews Signal processing algorithms Vegetation Production Signal processing

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Design and Development of Maiji Mountain Grottoes Immersive Virtual Reality Learning Environment 9th

Design and Development of Maiji Mountain Grottoes Immersive ...

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9th International Conference on Computational Science and Technology, ICCST 2022

作者： Cao, Ying Ng, Giap Weng Wang, Hui College of Arts and Science Beijing Union University 197 Beitucheng West Rd. Haidian District Beijing China Faculty of Computing and Informatics University Malaysia Sabah. Jalan UMS Sabah Kota Kinabalu88400 Malaysia

ISBN: (纸本)9789811984051

This study attempted to take Maiji Mountain Grottoes as an example to design an immersive virtual reality learning environment serving for the field of cultural heritage or archaeology. Cultural heritage needs to be protected and not easy to arrive, which brings difficulties and challenges to learners. Virtual reality learning environment can provide immersive experience for users without worrying about time and space and allow flexibly switch between macro and micro world to learn more intuitively and efficiently. Based on the constructivist learning principles, this study designed a learning-guide model for immersive virtual reality learning environment, which not only dynamically display the 3D knowledge map and guide users to explore interactively in the immersive virtual reality environment, but also intelligently recommend the learning path according to the real-time feedback of learning performance and learning behavior. This paper will focus on the key points of the design of the immersive virtual reality learning environment and put forward the plan of future work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer aided instruction

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Estimating Construction Material Indices with ARIMA and Optimized NARNETs

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Computers, Materials & Continua 2023年第1期74卷 113-129页

作者： Ümit Isıkdag Aycan Hepsag Sureyya Imre Bıyıklı DeryaÖz Gebrail Bekdas Zong Woo Geem Department of Informatics Mimar Sinan Fine Arts UniversityIstanbulTurkey Econometrics Department Istanbul UniversityIstanbulTurkey MIS Department Istanbul Gelisim UniversityIstanbulTurkey Computing HND Prog. Nisantasi UniversityIstanbulTurkey Department of Civil Engineering Istanbul University-CerrahpasaIstanbulTurkey College of IT Convergence Gachon UniversitySeongnam13120Korea

Construction Industry operates relying on various key economic *** of these indicators is material *** the other hand,cost is a key concern in all operations of the construction *** the uncertain conditions,reliable cost forecasts become an important source of *** cost is one of the key components of the overall cost of *** addition,cost overrun is a common problem in the construction industry,where nine out of ten construction projects face cost *** order to carry out a successful cost management strategy and prevent cost overruns,it is very important to find reliable methods for the estimation of construction material *** prices have a time dependent *** order to increase the foreseeability of the costs of construction materials,this study focuses on estimation of construction material indices through time series *** different types of analysis are implemented for estimation of the future values of construction material *** first method implemented was Autoregressive Integrated Moving Average(ARIMA),which is known to be successful in estimation of time series having a linear *** second method implementedwas Non-LinearAutoregressive Neural Network(NARNET)which is known to be successful in modeling and estimating of series with non-linear *** results have shown that depending on the nature of the series,both these methods can successfully and accurately estimate the future values of the *** addition,we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.

关键词： Construction material indices ARIMA non-linear autoregressive neural network NARNETs

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