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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Jang, Yurim Namgung, Jong Young Park, Yeongjun Park, Bo-Yong Inha University Artificial Intelligence Convergence Research Center Incheon Korea Republic of Inha University Department of Data Science Incheon Korea Republic of Inha University Department of Computer Engineering Incheon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of

ISBN: (纸本)9798350371499

Brain state is dynamic in nature, and identifying the transitions between different brain states is crucial for understanding ongoing cognitive functions. Here, we opted for a transformer-based blood-oxygen-level-dependent transformer model (BolT) to investigate dynamic transitions of brain states across time. Using the given time series data, the model predicted subsequent time series. At the same time, it provided information about the contribution of each time point for the prediction with the importance weights. We extracted the time series corresponding to the top 10% and bottom 10% contribution of the importance weights. For each time series data, we constructed the connectivity matrix and calculated the degree centrality values. We compared the degree values between the top 10% and bottom 10% contribution data and found significant differences in the regions involved in the default mode network. Specifically, decreases in the degree values were observed at the highly contributed time points. Our findings indicate that the reorganization of connectome organization occurs in the default mode network during transitions in brain states. © 2024 IEEE.

关键词： Brain models

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Exploring Advanced Analysis Technique for Shallow Water Flow Models with Diverse Applications

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第2期5卷 135-146页

作者： Ahmad, Hijaz Ismail, Gamal M. Alraddadi, Ibrahim El-Agamy, Rasha F. Aljanabi, Mohammad Farooq, Umar Near East University Operational Research Center in Healthcare TRNC Mersin 10 Nicosia99138 Turkey Department of Mathematics Faculty of Science Islamic University of Madinah Madinah Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Deputy Dean of Technical College Imam Ja’afar Al-Sadiq University Iraq Department of Mathematics Abdul Wali khan university Mardan Pakistan

The present article focuses on the analytical approach using fractional orders and its application in the dynamics of physical processes. Fractional order models align better with experimental data compared to non-fractional ones. This study primarily focuses on employing the new approximate analytical method to solve shallow water models with fractional orders. Numerical examples within the Caputo fractional derivative showcase the method’s application. Results for both integer and fractional orders are graphically depicted, demonstrating the fractional solutions’ closeness to actual data. Analysis of 3D and 2D fractional order graphs reveals convergence toward integer order graphs as fractional derivatives approach non-fractional ones. This method shows promise for direct application in solving targeted problems and can be easily adapted for other fractional nature problems. © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Numerical methods

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Quantum Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks

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IEEE Internet of Things Journal 2025年第12期12卷 21225-21239页

作者： Jeong, Seon-Geun Duc, Pham Dang Anh Do, Quang Vinh Noh, Dae-Il Tung, Nguyen Xuan Chien, Trinh Van Pham, Quoc-Viet Hasegawa, Mikio Sekiya, Hiroo Hwang, Won-Joo Department of Information Convergence Engineering Pusan National University Pusan National University Busan46241 Korea Republic of Wireless Communications Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Graduate School of Engineering Chiba University Chiba263-8522 Japan Hanoi100000 Viet Nam School of Computer Science and Statistics Trinity College Dublin DublinD02PN40 Ireland Department of Electrical Engineering Tokyo University of Science Tokyo162-8601 Japan School of Computer Science and Engineering Center for Artificial Intelligence Research Pusan National University Busan46241 Korea Republic of

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, sub-channel assignment, and power allocation in a multi-unmanned aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two sub-problems: 1) a QA-based clustering and 2) sub-channel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two sub-problems. For the first sub-problem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second sub-problem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time. © 2025 IEEE.

关键词： Combinatorial optimization

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FIN: A Deep Multi-task Model for Face Information Detection

FIN: A Deep Multi-task Model for Face Information Detection

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Lai, Yi-Shan Chen, Yi Fu, Li-Chen Hsiao, Pei-Yung Wang, Yu-Chieh National Taiwan University Department of Computer Science and Information Engineering Taiwan National University of Kaohsiung Department of Electrical Engineering Taiwan Automotive Research and Testing Center Taiwan Taiwan

ISBN: (纸本)9798350382204

In recent years, with the development of autonomous driving technology, designing a driver monitoring system that can run on vehicles has become increasingly important. The driving system analyzes various factors such as head pose, gaze direction, and eye state to determine the driver's attentiveness and aid the system in evaluating the driver's capability to control the vehicle. In this paper, we present a lightweight multi-task deep learning model that utilizes full-face images to detect head pose, gaze direction, and eye state simultaneously. We propose a task-based cross-dimensional attention module (TCAM) that selectively filters and enhances relevant features for each specific task. Additionally, to tackle the lack of diverse head pose angles in the eye state dataset, we introduce a data augmentation technique to generate eye state data under different head poses, enhancing the model's robustness in detecting eye states under varying head poses. We demonstrate the results of our work on the 300WLP, AFLW2000, BIWI, Gaze360, and MRL datasets. Extensive experiments show that our method can obtain competitive results with lower parameter costs. Furthermore, we establish a baseline for eye state detection using full-face images as the input on the CEW datasets. © 2023 IEEE.

关键词： Autonomous vehicles

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Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

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computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized Data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

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Low-Complexity Channel Estimation for RIS-Assisted ISAC System

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China Communications 2025年第4期22卷 296-308页

作者： Chen Zhen Li Jianqing Zhang Haijun Zhang Wei The Institute of Microelectronics University of MacaoMacao 999078China School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology BeijingBeijing 100083China Henan Academy of Science Applied Physics Institute Co. LtdZhengzhou 450008China

Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless ***,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability *** address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is *** approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift *** previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC *** effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.

关键词： channel estimation element grouping integrated sensing and communication(ISAC) RIS

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Convolutional Neural Network and Recursive Feature Elimination Based Model for the Diagnosis of Mild Cognitive Impairments 1

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Bhasin, Harsh Mehrotra, Abheer Ohri, Ansh Department of Computer Science and Engineering Center for Health Innovations Manav Rachna International Institute of Research and Studies and Computer Science Engineering MRIIRS Aravali Hills Sector 43 Haryana Faridabad India Manav Rachna University Aravali Hills Sector 43 Haryana Faridabad India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

Aging leads to reduced cognitive abilities. A person heading toward dementia will also show such signs but with more severity. This stage is referred to as Mild Cognitive Impairment. It has been observed that nearly one-fifth of the people suffering from MCI convert to dementia. The patients suffering from MCI who convert to dementia are called MCI-Converts and those who do not convert are called MCI Non-converts. This work proposes a model that extracts gray matter from the s-MRI brain volume and explores the applicability of combination of Convolutional Neural Network and Recursive Feature Elimination in the diagnosis of MCI. Features of the data obtained using the carefully crafted CNN followed by the selection of the appropriate features using Recursive Feature Elimination are then used to accomplish the task. Empirical analysis has been done to select the parameters of the CNN. The results are better than the state of the art and pave way for the exploitation of the Deep Learning models to classify the Converts and the Non-converts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Magnetic resonance imaging

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QUALYST: Data Quality Assessment System for Thailand Open Government Data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government Data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation 40

FedCross: Towards Accurate Federated Learning via Multi-Mode...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Hu, Ming Zhou, Peiheng Yue, Zhihao Ling, Zhiwei Huang, Yihao Li, Anran Liu, Yang Lian, Xiang Chen, Mingsong School of Computer Science and Engineering Nanyang Technological University Singapore East China Normal University MoE Engineering Research Center of SW/HW Co-Design Tech. and App. China Kent State University Department of Computer Science OH United States

ISBN: (纸本)9798350317152

As a promising distributed machine learning paradigm, Federated Learning (FL) has attracted increasing attention to deal with data silo problems without compromising user privacy. By adopting the classic one-to-multi training scheme (i.e., FedAvg), where the cloud server dispatches one single global model to multiple involved clients, conventional FL methods can achieve collaborative model training without data sharing. However, since only one global model cannot always accommodate all the incompatible convergence directions of local models, existing FL approaches greatly suffer from inferior classification accuracy. To address this issue, we present an efficient FL framework named FedCross, which uses a novel multi-to-multi FL training scheme based on our proposed multi-model cross-aggregation approach. Unlike traditional FL methods, in each round of FL training, FedCross uses multiple middleware models to conduct weighted fusion individually. Since the middleware models used by FedCross can quickly converge into the same flat valley in terms of loss landscapes, the generated global model can achieve a well-generalization. Experimental results on various well-known datasets show that, compared with state-of-the-art FL methods, Fed Cross can significantly improve FL accuracy within both IID and non-IID scenarios without causing additional communication overhead. © 2024 IEEE.

关键词： Middleware

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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