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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： Moussa, Aya Mohammed A. El-Gamal, Fatma El-Zahraa A. El Fetouh, Ahmed Saleh A. Business Information Technology Program Faculty of Computer and Information Mansoura University Mansoura Egypt Information Technology Department Faculty of Computer and Information Mansoura University Mansoura Egypt Delta Higher Institute for Management and Accounting Information Systems Mansoura Egypt

ISBN: (纸本)9789819947638

Medicine is one of the products that must be delivered to the patient safely, undamaged, and during its validity period. However, the risks of the drug supply chain have become increasingly apparent. To address these risks and due to the rapid advances in the technology field, various digital transformation trials around the globe start to be considered in order to replace the traditional drug supply chain until its delivery to the patient. Therefore, the aim of this paper is to contribute in the digital transformation goal of the drug supply chain in Egypt. Thus, this paper proposes a trusted automatic system, based on the blockchain and Internet of Things (IoT) technologies to enhance the performance of the drug supply chain and accordingly make the drug manufacturing and distribution system in Egypt more efficient and reliable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Internet of things

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Leveraging Attention to Achieve Generalization for Image Forgery Detection 1

Leveraging Attention to Achieve Generalization for Image For...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Atta, Mohamed Nile University Artificial Intelligence Program School of Information Technology and Computer Science Giza Egypt

ISBN: (纸本)9798350335569

Recently, image forgery has become an alarming trend with the growth of available easy-to-use editing and generation tools. Modern DeepFake methods have achieved extraordinary progress in realistic face manipulation, thus raising concerns among the public about the misuse of such technologies. Unfortunately, with the obnoxiously wide range of possible manipulation and artifact-covering methods, most existing state-of-the-art detection methods lack the generalization capability to handle the output variations. To address this issue, a noticeable shift towards using attention mechanisms has emerged using balanced portions of the latest challenging datasets to detect intra-and inter-spatial relations. Our paper provides a comprehensive analysis of modern deep learning-based methods, showing the benefits of the shift. In addition, we make propositions for future research directions and dataset-building methodology. © 2023 IEEE.

关键词： Generative adversarial networks

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Enabling Smart Home Energy Management through Gesture-Based Control and IoT technology

Enabling Smart Home Energy Management through Gesture-Based ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Rambe, Khairul Hawani Ou, Chia-Ho International Master Program in Information Technology and Applications Sweden National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

The Internet of Things (IoTs) have become ubiquitous in all aspects of public needs today. The application of IoT technology is crucial for promoting energy-saving behavior. This paper presents a control system used for smart home devices based on hand gesture control and Android applications. The hardware design uses the ESP32 microcontroller, which has an in-chip Wi-Fi module, making it very suitable for creating IoT application systems. The software was designed using the Arduino IDE, and the application display design was developed using Kodular Creator, which is used to control the lamps and switches. The experiment results show that the electrical equipment can be activated using the Android application with gesture sensors by waving down, up, left, and right. © 2023 IEEE.

关键词： Internet of things

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Robot-based therapy for improving academic skills of children with autism

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International Journal of information technology (Singapore) 2024年第6期16卷 3371-3380页

作者： Abu-Amara, Fadi Mohammad, Heba Bensefia, Ameur Cybersecurity Program Shenandoah University Winchester VA United States Computer Information and Sciences Department Higher Colleges of Technology Abu Dhabi United Arab Emirates

Robots are being employed more as mediators between therapists and children with autism. This paper proposes a robot-based therapeutic method that uses the NAO robot, a developed mobile application called AutisPlay, a tablet, and a control laptop. The proposed method is implemented in therapeutic sessions at a local autism center. In this research, a study involving nine children diagnosed with autism spectrum disorder is conducted. The researchers and autism therapists agreed on the targeted academic skills. The main goal of this work is to assess the use of robots and mobile applications in improving the academic skills of children with autism. The obtained experimental results are analyzed using statistical measures such as the percentage of correct responses, informal observations of students’ attention to the robot, reported academic progress, and enjoyment of the therapy sessions. Further, the therapeutic sessions are customized to each child's response to the robot and mobile application. The children showed different engagement levels and a variety of progress and achievements. The experimental results show three children acquired the indicating skills, while the remaining children show slow progress in their skill acquisition. Furthermore, seven students enjoyed the interaction with the NAO robot and AutisPlay mobile app during therapy sessions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Academic skills Autism spectrum disorder Human–robot interaction Mobile application Robot-assisted therapy

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Advancements in Deep Learning Optimization for Post-Earthquake Building Damage Detection A Systematic Review

Advancements in Deep Learning Optimization for Post-Earthqua...

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2024 International Conference on Advanced information Scientific Development, ICAISD 2024

作者： Sonang, Sahat Yuhandri, Y. Tajuddin, Muhammad Universitas Putra Indonesia Yptk Information Technology Study Program Padang Indonesia Universitas Bumigora Department of Computer Science Mataram Indonesia

ISBN: (纸本)9798350392036

This study conducts a systematic literature review (SLR) that focuses on optimizing deep-learning techniques for post-earthquake building damage detection. By employing the PRISMA protocol, this review aimed to collect, analyze, and summarize the findings of various studies published between 2014 and 2024. This research identifies and evaluates optimization methods such as hyperparameter tuning, transfer learning, and data augmentation, highlighting their effectiveness and implementation. Challenges such as data quality, computational costs, and model interpretability were also discussed. The findings indicate that while deep learning techniques significantly enhance the accuracy and efficiency of damage detection, further research is required to address existing challenges and improve model robustness in diverse earthquake scenarios. © 2024 IEEE.

关键词： Contrastive Learning

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Investigating the potential adverse effects of virtual reality-based learning system usage: from UTAUT2 and PPVITU perspectives

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Interactive Learning Environments 2024年第9期32卷 5106-5125页

作者： Wang, Yu-Yin Chuang, Yu-Wei Department of Computer Science and Information Management Providence University Taichung Taiwan Bachelor Program in Business and Management National Yunlin University of Science and Technology Yunlin Taiwan

A review of the literature shows that much academic effort has been expended studying information system usage and information technology adoption. However, these theories/models based on psychological research are not specific to the virtual reality context and may not fully capture the nature of virtual reality-based learning system (VR-BLS) usage behavior in the virtual environment. Given the limited utility of existing theories/models, this study aims to build a theoretical model and to validate the psychological and physiological effects of using VR-BLSs. To explore students’ usage intentions, this study integrates the extended unified theory of acceptance and use of technology (UTAUT2), representing the psychological effects, and the perceived physiological vulnerability to IT usage (PPVITU), representing the physiological effects. We empirically examine the research model using a cross-sectional survey, collecting data from a sample of 352 students, and utilizing partial least squares (PLS) for the analysis. Our findings show that the behavioral intention to use VR-BLSs is determined by performance expectancy, social influence, and facilitating conditions. Moreover, PPVITU negatively moderates the association between social influence and the behavioral intention to use VR-BLSs. The results of this study enhance our understanding of the relationship between students’ VR-BLS usage behavior and VR-based education. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： extended unified theory of acceptance and use of technology (UTAUT2) perceived physiological vulnerability to IT usage (PPVITU) system usage virtual reality-based education Virtual reality-based learning

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Spreading cybersecurity awareness via gamification: zero-day game

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International Journal of information technology (Singapore) 2024年第5期16卷 2945-2953页

作者： Abu-Amara, Fadi Hosani, Reem Al Tamimi, Hamda Al Hamdi, Baraka Al Cybersecurity Program Shenandoah University Winchester VA United States Computer and Information Sciences Department Higher Colleges of Technology Abu Dhabi United Arab Emirates

This research project aims to increase the employees' cybersecurity awareness by proposing a training program that consists of pre-game and post-game phishing attacks, pre-game and post-game surveys, and an interactive zero-day game with embedded threat scenarios. Furthermore, we propose five flowcharts that simulate common cyberattacks. The zero-day game consists of five levels. The first level trains employees in using complex passwords. Two levels cover the social engineering attacks: one level trains employees about suspicious links and online survey tricks, while the other level trains players about the organization's security policies. The fourth level educates employees about malware spread via repackaged fake applications. The last level educates employees about phishing email attacks. Furthermore, we developed a chatbot inside the game to aid users in playing the different game levels. Additionally, two surveys and two phishing attacks are carried out to assess the employees' knowledge level of cybersecurity threats. The zero-day cybersecurity awareness program is applied to twenty-three employees. Experimental results indicate that the zero-day cybersecurity awareness program is more engaging and effective than traditional awareness programs. Results also indicate that the proposed program accurately measures and improves the employees' cybersecurity awareness level. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Awareness program Chatbot Cybersecurity Interactive video game Social engineering

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Optimizing Random Forest Classification Using Chi-Square and SMOTE-ENN on Student Drop-Out Data 8

Optimizing Random Forest Classification Using Chi-Square and...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Andri Yunis, Roni Tanti Universitas Mikroskil Undergraduate Program of Computer Science Department Medan Indonesia Universitas Mikroskil Undergraduate Program of Information Systems Department Medan Indonesia Universitas Mikroskil Undergraduate Program of Information Technology Department Medan Indonesia

ISBN: (纸本)9798350342604

Dropout is a particular concern for countries striving to increase human capital. Various attempts have been made by universities to minimize the number of dropouts. Machine learning has also developed various predictive models to determine the likelihood of students dropping out. However, there is a challenge in dropout data, specifically the problem of class imbalance, where the number of students who drop out (minority class) is significantly less than those who do not drop out (majority class). This imbalance can reduce the model's ability to classify students at risk of dropping out. This study proposes classification optimization using the Random Forest algorithm to handle class imbalances in student dropout data. To overcome class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) techniques are used. Additionally, the attribute selection method is also applied to enhance the predictive results. The test results demonstrate that the combination of implementing feature selection with Chi-Square, followed by class imbalance handling with SMOTE-ENN, provided the most optimal predictive performance for identifying the status of both dropouts and graduates. © 2023 IEEE.

关键词： Students

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Comments on a Key Agreement Scheme for the V2G System 14th

Comments on a Key Agreement Scheme for the V2G System

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14th International Conference on Frontier Computing, FC 2024

作者： Chang, Ya-Fen Chang, Wen-Ting Tai, Wei-Liang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Vehicle-to-grid (V2G) technology supporting bidirectional power transfer allows electric vehicles (EVs) to contribute and consume energy bidirectionally. Because the specific properties and requirements of V2G, Khan et al. proposed a key agreement scheme for the V2G system to ensure the security of communication between a vehicle user and the grid server. However, after analyzing Khan et al.’s scheme, we find that it suffers from some flaws such that it cannot ensure anonymity, resist impersonation attack, man-in-the-middle attack and denial-of-service attack, and work as expected. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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DMSS:An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

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Big Data Mining and Analytics 2024年第3期7卷 577-589页

作者： Yihui Ren Yu Wang Wenkai Han Yikang Huang Xiaoyang Hou Chunming Zhang Dongbo Bu Xin Gao Shiwei Sun Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190Chinaand with University of Chinese Academy of SciencesBeijing 100049China Syneron Technology Guangzhou 510000China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Insitute of Computing Technology Chinese Academy of SciencesBeijing 100190China with Western Institute of Computing Technology Chongqing 400000China

Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis,as well as for gaining insight into various biological *** this study,we introduce Deep MS Simulator(DMSS),a novel attention-based model tailored for forecasting theoretical spectra in mass *** has undergone rigorous validation through a series of experiments,consistently demonstrating superior performance compared to current methods in forecasting theoretical *** superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein *** findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.

关键词： mass spectrometry proteomics machine learning deep learning

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