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IEEE Transactions on Artificial Intelligence 2025年

作者： Ye, Wei Tang, Shuhao Tian, Hao Chen, Qijun Shanghai Institute of Intelligent Science and Technology Tongji University College of Electronic and Information Engineering Shanghai201804 China Shanghai Innovation Institute Shanghai200231 China City University of Macau Faculty of Data Science Taipa China Tongji University College of Electronic and Information Engineering Shanghai201804 China

R-convolution graph kernels are conventional methods for graph classification. They decompose graphs into substructures and aggregate all the substructure similarity as graph similarity. However, the substructure similarity is based on graph isomorphism, which not only leads to binary similarity values but also cannot be aware of the probability distribution of substructures in each graph. Moreover, the simple sum aggregation is not aware of the probability distribution differences of substructures across graphs. These drawbacks cause inaccurate graph similarity. To resolve these problems, we propose a new method called the Distribution-Aware Simple-Path (DASP) graph kernel. The neural language models are employed to capture the probability distribution of substructures (specifically, simple paths) in each graph. A new metric called Probabilistic Minkowski Distance is developed to capture the probability distribution differences of simple paths across graphs. To further improve the performance, the label alphabet is expanded to enlarge the corpus of simple paths for the neural language models and DASP. Experiments demonstrate that DASP achieves the best classification accuracy on all the selected graph benchmark datasets. © 2020 IEEE.

关键词： Knowledge graph

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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What drives consumers' intention to purchase self-driving cars

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Managerial and Decision Economics 2024年第4期45卷 1773-1792页

作者： Yu-Min Wang Wan-Ching Chiu Chung-Lun Wei Hsing-Hsien Wang Jih-Hua Yang Yi-Shun Wang Department of Information Management National Chi Nan University Nantou County Taiwan Division of Research and Development National Taichung University of Science and Technology Taichung Taiwan Faculty of Data Science City University of Macau Macau China Department of Information Management National Changhua University of Education Changhua City Taiwan

This study endeavors to examine the factors influencing consumers' intentions to purchase self-driving cars. The data collected from 174 respondents underwent analysis using the partial least squares technique. The findings reveal that relative advantage, compatibility, trialability, and observability significantly impact consumer attitudes. Additionally, consumer attitude, subjective norm, and perceived behavioral control were identified as significant influencers of the intention to purchase a self-driving car. Furthermore, achievement vanity was observed to moderate the effect of observability on consumer attitude. These results offer critical theoretical insights and practical implications for the adoption of self-driving cars.

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A Hybrid Cloud ERP Framework For Processing Purchasing data

arXiv

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arXiv 2022年

作者： Zhang, Xinyu Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Australia

Cloud-based enterprise resource planning (cloud ERP) systems have existed in the business market for around ten years. Cloud ERP supports enterprises' daily activities by integrating organizational back-end systems in the cloud environment. One of the critical functions that cloud ERP offers is the purchasing application. The purchasing function of cloud ERP enables enterprises to streamline all the online purchasing transactions in real-time automatically. Even cloud ERP is deployed quite often these days, organizations somehow still lack the knowledge of it;to be specific, there are many issues attached to cloud ERP implementation yet to be solved. Hence, this paper compares four leading cloud ERP platforms in Australia and proposes a hybrid cloud ERP framework to process online purchasing transactions. By adopting a case study approach, a purchasing web-based application is designed and presented in this paper. In general, the proposed hybrid cloud ERP framework and the integrated web-based purchasing application allow user companies to process online purchasing transactions with short operation time and increased business efficiency;in the meantime, the proposed framework also reduces security risks attached to the public cloud. Copyright © 2022, The Authors. All rights reserved.

关键词： Enterprise resource planning

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection

Bridging Machine Learning and Formal Concept Analysis for Ef...

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information technology (ACIT)

作者： Anas M. Al-Oraiqat Oleksandr Drieiev Ghassan Samara Sattam Almatarneh Karim A. Al-Oraiqat Hazim Alrawashdeh Hanna Drieieva Ali Elrashidi Yassin M. Y. Hasan Department of Computer Science Faculty of Information Technology Zarqa University Jordan Department of Cybersecurity and Software Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Data Science and Artificial Intelligence Faculty of Information Technology Zarqa University Jordan Department of Software Engineering Faculty of Information Technology Zarqa University Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Electrical Engineering Department Assiut University Egypt

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude" low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering" of crowded zones into hierarchical formal concepts and these latter’s "bottom-up parsing" to finally identify the dense zones.

关键词： Formal concept analysis Scalability Prevention and mitigation Decision making Lattices Machine learning Feature extraction Prediction algorithms Real-time systems data mining

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Analyzing and Developing the Security of DNS-Based Authentication in Wireless Sensor Network 3

Analyzing and Developing the Security of DNS-Based Authentic...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sharma, Umesh Chand Kumar, Jambi Ratna Raja Kumari, Meenakshi Anandhasilambarasan, D. Latha, B. Jadhav, Prranjali Mangalayatan University Department of Computer Science Aligarh India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Analyzing and developing the safety of DNS-based total Authentication in Wireless Sensor Networks (WSNs) is a critical research vicinity that is being actively pursued. While preceding work has blanketed authentication strategies to relax the communications between a node and its base station, our studies focus on analyzing the authentication schemes used to set up a comfy verbal exchange in a wireless Sensor network among nodes. We look at the assets of Symmetric and asymmetric Authentication Protocols for WSNs and examine their effectiveness in terms of their authentication effectiveness and overhead. We also discuss how they can be incorporated with current authentication schemes used for WSNs. We then gift a singular protocol that uses DNS-based Authentication to safely set up a verbal exchange in a Wi-Fi Sensor network. Ultimately, we put in force and tested our protocol and the degree of its overall performance in simulated surroundings. Our experiments' results show that our protocol gives an effective authentication answer for WSNs. © 2023 IEEE.

关键词： authentication effectiveness exchange experiments schemes

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

arXiv

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arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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LOSS FUNCTIONS IN THE ERA OF SEMANTIC SEGMENTATION: A SURVEY AND OUTLOOK

arXiv

引用

arXiv 2023年

作者： Azad, Reza Heidari, Moein Yilmaz, Kadir Hüttemann, Michael Karimijafarbigloo, Sanaz Wu, Yuli Schmeink, Anke Merhof, Dorit Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany

Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. As the predominant criterion for evaluating the performance of statistical models, loss functions are crucial for shaping the development of deep learning-based segmentation algorithms and improving their overall performance. To aid researchers in identifying the optimal loss function for their particular application, this survey provides a comprehensive and unified review of 25 loss functions utilized in image segmentation. We provide a novel taxonomy and thorough review of how these loss functions are customized and leveraged in image segmentation, with a systematic categorization emphasizing their significant features and applications. Furthermore, to evaluate the efficacy of these methods in real-world scenarios, we propose unbiased evaluations of some distinct and renowned loss functions on established medical and natural image datasets. We conclude this review by identifying current challenges and unveiling future research opportunities. Finally, we have compiled the reviewed studies that have open-source implementations at our GitHub1 © 2023, CC BY.

关键词： Medical imaging

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Extending RIS Life Span for Reliable Communication Under Hardware Ageing Effects

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3395-3409页

作者： Mondal, Atiquzzaman Ghosh, Shyamal Singh, Keshav Biswas, Sudip Duong, Trung Q. Indian Institute of Information Technology Guwahati Department of Electrical Communication Engineering Guwahati781015 India Indian Institute of Science Education and Research Thiruvananthapuram Department of Data Science Thiruvananthapuram695551 India National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

In this paper, we address the critical aspect of hardware ageing effects in reconfigurable intelligent surfaces (RISs), whereby the problem of extending the RIS's life cycle under the impact of non-residual stochastic hardware impairment is considered. Through the replication of the RIS hardware and diverse wireless environments within a statistical simulation environment, we formulate an electronic maintenance framework (EMF) for RIS, where we incorporate non-residual impairments through stochastic modeling and determine the electronic reliability of the system. Accordingly, we derive optimal analytical solutions within the EMF to determine whether systematic maintenance of the RIS hardware should be done immediately or postponed in order to extend the expected life cycle of the RIS system. Furthermore, for the scenario with imperfect maintenance of the RIS-aided system, a reliable communication framework (RCF) is also introduced with residual impairments to assess the error probability of the RIS-aided communication. The RCF is established by deriving the distribution of the received signal-to-interference-plus-noise ratio in the presence of residual hardware impairment arising due to imperfect maintenance of the RIS system. Extensive numerical examples are provided to elucidate the derived solutions and illustrate the reliability performance of the proposed framework under hardware impairments. © 2020 IEEE.

关键词： Wireless sensor networks

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