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4th International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Alnajdawi, Ruwaida Elhoseny, Mohamed Clark, Andrew Gaber, Tarek School of Science Engineering & Environment The University of Salford United Kingdom The College of Computing and Informatics The University of Sharjah United Arab Emirates The University of Salford United Kingdom

ISBN: (纸本)9781839539954

Resource consumption is increasing globally. Global resource consumption has surged by over 65%, contributing to 70% of greenhouse gases (GHGs), raising concerns for future generations. Circular economy offers a strategic approach that perpetuates materials and regenerates nature, particularly with key materials, to potentially reduce GHGs by 40% by 2050. However, circular economy implementation faces challenges, notably in tracking material lifecycles. Due to the complexity of tracking material lifecycles, technology is needed, most notably, blockchain. This paper will review blockchain applications in the circular economy through existing literature. A total of 27 publications were reviewed where future research of the publications were also revealed. © The Institution of engineering & Technology 2023.

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LSTM-QGAN: SCALABLE NISQ GENERATIVE ADVERSARIAL NETWORK

arXiv

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arXiv 2024年

作者： Chu, Cheng Hastak, Aishwarya Chen, Fan Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can diminish the QGAN’s effectiveness. Second, methods that segment inputs into smaller patches processed by multiple generators face scalability issues. In this work, we propose LSTM-QGAN, a QGAN architecture that eliminates PCA preprocessing and integrates quantum long short-term memory (QLSTM) to ensure scalable performance. Our experiments show that LSTM-QGAN significantly enhances both performance and scalability over state-of-the-art QGAN models, with visual data improvements, reduced Fréchet Inception Distance scores, and reductions of 5× in qubit counts, 5× in single-qubit gates, and 12× in two-qubit gates. © 2024, CC BY.

关键词： Generative adversarial networks

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Prediction of Myocardial Infarction Using Convolutional Neural Network Method on Electrocardiogram Signal

Prediction of Myocardial Infarction Using Convolutional Neur...

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Data Science and Its Applications (ICoDSA), International Conference on

作者： Prinintya Nuri Alifa Satria Mandala Informatics School of Computing Telkom University Bandung Indonesia Human Centric Engineering School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798350365351

ISBN: (纸本)9798350365368

Myocardial infarction or MI is a medical condition that occurs when the supply of oxygen-rich blood to the heart muscle is interrupted or stops suddenly. As a result, the part of the heart muscle that does not get enough blood supply begins to suffer damage or death. Research related to MI using ECG signals has started since the 1900s. As technology and computing advances, analysis of ECG signals with more sophisticated methods, such as digital image processing and signal processing techniques, has been developed to improve accuracy and reliability in detecting myocardial infarction. But it does not provide detailed information about the interaction between embedding features. This research provides a solution to the above problem by developing a CNN-based MI model that will be optimized by Hyperparameter-tuning. This technique is proven to produce a solid and accurate MI model. However, it is important to run a comprehensive evaluation and validation of these models, including testing on existing data sets and peers. The methods used in the development of MI models with CNNs are as follows. First, this study examines the performance of ECG data using CNN algorithms, then the results will be compared with the performance of ECG data using Hyperparameter-tuning. The result of this research is the MI model with CNN Non-Tuning method has an accuracy of 77% and for CNN Fine-Tuning method has an accuracy of 80%.

关键词： Heart Accuracy Transfer learning Myocardium Predictive models Electrocardiography Data models

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GCAT: graph calibration attention transformer for robust object tracking

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Neural computing and Applications 2024年第23期36卷 14151-14172页

作者： Chen, Si Hu, Xinxin Wang, Da-Han Yan, Yan Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen361005 China

Recent Siamese trackers have taken advantage of transformers to achieve impressive advancements. However, existing transformer trackers ignore considering the positional and structural information between tokens, and traditional template update strategies easily introduce noises to the dynamic templates during tracking. In order to alleviate this issue, this paper develops a novel end-to-end graph calibration attention transformer network (GCAT) to enhance tracking robustness and accuracy. A graph calibration attention mechanism is first designed to calibrate and aggregate template information, for effectively updating dynamic templates during the tracking process. Specifically, each token is considered as a node in the graph, and then, we calculate the weight relationships between each node and their adjacent nodes. Thus, this mechanism can aggregate the global context information of the template and search nodes and activate feature channels based on weights and biases to obtain more discriminative feature information. Moreover, we leverage a multi-level dropout mechanism to perform the data dropout, the layer dropout, and the feature dropout on the data, network, and attention levels, respectively, to avoid overfitting of local-specific information and improve the generalization ability. Extensive experiments show the proposed method achieves superior performance on seven challenging benchmark datasets, i.e., OTB100, OTB2013, UAV123, LaSOT, GOT10K, VOT2020, and TrackingNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Aggregates

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IoT Task Offloading in Edge computing Using Non-Cooperative Game Theory for Healthcare Systems

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Computer Modeling in engineering & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed School of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Computer Engineering SRM University APGunturIndia College of Computer and Information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia Department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge computing(MEC)*** network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing *** central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy *** system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT *** study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial *** results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn ***,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy *** research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

关键词： Internet of Things edge computing offloading NOMA

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Slicing-Based Enhanced Method for Privacy-Preserving in Publishing Big Data

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Computers, Materials & Continua 2022年第8期72卷 3665-3686页

作者： Mohammed BinJubier Mohd Arfian Ismail Abdulghani Ali Ahmed Ali Safaa Sadiq Faculty of Computing Universiti Malaysia PahangKuantanPahangMalaysia School of Computer Science and Informatics De Montfort UniversityLeicesterLE19BHUnited Kingdom School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonWulfruna Street WolverhamptonWV11LYUnited Kingdom

Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan,conduct,and assess scientific ***,publishing and processing big data poses a privacy concern related to protecting individuals’sensitive information while maintaining the usability of the published *** anonymization methods,such as slicing and merging,have been designed as solutions to the privacy concerns for publishing big ***,the major drawback of merging and slicing is the random permutation procedure,which does not always guarantee complete protection against attribute or membership ***,merging procedures may generatemany fake tuples,leading to a loss of data utility and subsequent erroneous knowledge *** study therefore proposes a slicingbased enhanced method for privacy-preserving big data publishing while maintaining the data *** particular,the proposed method distributes the data into horizontal and vertical *** lower and upper protection levels are then used to identify the unique and identical attributes’*** unique and identical attributes are swapped to ensure the published big data is protected from disclosure *** outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.

关键词： Big data big data privacy preservation anonymization data publishing

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Adaptive iterated local search algorithm for dynamic patient admission scheduling problems

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Soft computing 2025年第7期29卷 1-11页

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Resource allocation

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Augmented Reality in Education: Transformative Innovations and Immersive Learning Experiences 4th

Augmented Reality in Education: Transformative Innovations a...

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4th International Conference on Advances in Computational Science and engineering, ICACSE 2023

作者： Abdul Rahim, Rini Hafzah Mohd Nizam, Dinna Nina Mohd Naim, Nur Faraha Baharum, Aslina User Experience Group Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Department of Computing and Information System School of Engineering and Technology Sunway University No 5 Jalan University Selangor Bandar Sunway57500 Malaysia

ISBN: (纸本)9789819729760

The use of technology, such as Augmented Reality Immersive Learning (ARIL), in education to improve student learning by fusing the actual world with the virtual world is thoroughly examined. This research aims to ascertain how Augmented Reality (AR) technology might improve immersive learning for practical skills. AR has exciting opportunities for immersive learning since it can blend digital content with the real world. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and three databases—Scopus, the Institute of Electrical and Electronics Engineers (IEEE), and the Education Resources Information Center (ERIC)—206 publications have been carefully selected for the study. Three main themes emerged from the 34 articles that met the qualifying requirements: Enhancing Learning Experiences with AR in Education, Innovative AR Applications in Education, and Immersive Technologies in Education: Challenges and Innovations. The three themes’ findings point to a common theme: the effective use of technology in the classroom to improve students’ immersive learning, confidence, memory retention abilities, positive concern, creative thinking, and self-awareness. Furthermore, the usage of AR may be coupled with other technologies such as chatbots, Artificial Intelligence (AI), Internet of Things (IoT), Mixed Reality (MR), Extended Reality (XR), and others. One significant finding from the study was that certain frameworks, such as the AR Immersive Learning Design (AILeaD), the Game-Based Assessment Framework (GBAF), and AR immersion (ARI), might serve as useful recommendations for implementing AR technology for educational purposes. Further studies should be conducted and considering that every student has a particular learning style and preference, more research should investigate using this framework and approach to help students learn theory or practical in various academic topics. © The Author(s), under exclusive license to Springer Nature Singa

关键词： Contrastive Learning

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Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecurity-Based Approach

Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecur...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Phan, Quoc Bao Nguyen, Linh Nguyen, Tuy Tan Nguyen, Dinh C. Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350324136

This paper presents a novel two-stage approach to enhance the quality and privacy of X-ray medical images. The first stage leverages generative adversarial networks (GANs) for effective denoising, eliminating noise and artifacts from X-ray images while improving the visibility of critical anatomical structures. Subsequently, number-Theoretic transform (NTT) polynomial multiplication is integrated with Kyber to accelerate the encryption and decryption of the denoised X-ray images. This encryption safeguards the privacy of sensitive patient data and provides resilience against potential quantum computing attacks, ensuring long-Term data security. Implementing Kyber-based encryption and decryption on a graphics processing unit (GPU) architecture significantly reduces latency, enabling real-Time and secure access to critical healthcare information. © 2024 IEEE.

关键词： GANs Images denoising Kyber number theoretic transform

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Sum Rate Maximization in Cooperative Non-Orthogonal Multiple Access Based 5G Wireless Networks 3

Sum Rate Maximization in Cooperative Non-Orthogonal Multiple...

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3rd IEEE International Conference on Recent Advances in Systems Science and engineering, RASSE 2023

作者： Aswathi, V.A. Rahul Manohar, O. Ranjan, Priyesh School of Electronics Engineering Vellore Institute of Technology Vellore India Christ College of Engineering Department of Electronics and Communication Engineering Kerala Irinjalakuda India Health Informatics Centre for Development of Advanced Computing Noida India

ISBN: (纸本)9798350341676

This paper addresses a coordinated direct and relay transmission (CDRT) network with a downlink non-orthogonal multiple access (NOMA) over Rayleigh fading channels. A Coordinated Direct and Relay Transmission network consists of a base station (BS), two downlink users (one near user and one far user or cell edge user), and a relay. The near user will receive its intended message directly from the BS, whereas the far user or cell edge user will access and be supported by a full-duplex (FD) decode and forward (DF) relay to receive its message. Since the paper considers a NOMA network, the near user will be allocated with a lower power and the far user will be allocated with a higher power to ensure user fairness. The first section of this study discusses the system model, signal models and associated signal-To-interference plus noise ratios (SINRs). The joint optimisation problem for maximising the minimum sum rate is defined in the second half. Mathematical analysis is used to generate the joint power allocation factor and the results of both optimal and non-optimal conditions are plotted using Monte-Carlo simulations. Through various analyses, this paper establishes that using optimal power allocation factors can significantly improve the system's performance. © 2023 IEEE.

关键词： Wireless networks

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