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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Li, Monica M.Q. Polytehcnique Montreal Department of Computer and Software Engineering Montreal Canada

ISBN: (纸本)9798350389692

In human machine interaction tasks, the quality of motion capture plays a critical role. Rokoko Motion Capture System (Rokoko) is a relatively economic motion capture device and has been utilized in various areas of motion-related research. In this study, we test the representative ability of the products captured by Rokoko and Microsoft Kinect v2 (Kinect). Three non-professional actors wore the Rokoko to do three kinds of activities: walking with normal velocity, jumping with vertical acceleration and rotation, by displaying different body functions. Motion data were recorded using these two devices simultaneously. We compared the pros and cons of the results of the captured motions by these two devices and provide suggestions and precautions using Rokoko Smartsuit Pro for motion capture activities for researchers. © 2024 IEEE.

关键词： Human robot interaction

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Consumer grade VR gloves based on hall effect sensors in an angular flexion arrangement 1

Consumer grade VR gloves based on hall effect sensors in an ...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Yessentayev, Timur Mukanova, Zhanna Department of Computer and Software Engineering Turan University Almaty Kazakhstan

ISBN: (纸本)9798350364378

VR gloves can greatly enhance the realism of the VR experience by allowing users to not only see and hear the virtual environment, but also touch it without having to press buttons. This could make VR more appealing to a wide range of users, as well as increase the effectiveness of VR applications in various fields such as education, entertainment and others. VR gloves are currently aimed at the enterprise market and are out of the price range of the regular consumer. They offer benefits for social interaction in VR experiences and improve immersion. This article covers findings during the design process of an affordable VR glove prototype, based on hall effect sensors in an angular flexion arrangement, such as: user input surfaces (buttons, joysticks etc.), power supply and management of the glove, sensor accuracy, noise suppression, form factor, wiring, weight distribution, tracking solutions and material selection. ©2024 IEEE.

关键词： Virtual reality

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Exploration of the Possibility for a Wider Range of the Discount Factor for Reinforcement Learning 6

Exploration of the Possibility for a Wider Range of the Disc...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Li, Monica M.Q. Polytechnique Montreal Department of Computer and Software Engineering Montreal Canada

ISBN: (纸本)9798350353174

The discount factor for reinforcement learning has been constrained to the range [0, 1] to avoid overestimation of state values during training. Although theoretical studies have indicated that discount factors greater than 1 could be valid for certain tasks, experimental work has generally maintained the [0, 1] limit in flexible discount factor optimization. Our study explores the impact of allowing discount factors to exceed 1 across a variety of reinforcement learning algorithms and classic training environments. Our findings demonstrate that in some grid-based environments, reinforcement learning algorithms with discount factors above 1 can achieve comparable performance to those with discount factors within the [0, 1] range. However, performance tends to be more variable when the discount factor exceeds 1. © 2024 IEEE.

关键词： discount factor reinforcement learning

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A remote and cost-optimized voting system using blockchain and smart contract

IET Blockchain

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IET Blockchain 2023年第1期3卷 1-17页

作者： Neloy, Mohammad Nabiluzzaman Wahab, Md. Abdul Wasif, Sheikh All Noman, Abdulla Rahaman, Mustafizur Pranto, Tahmid Hasan Haque, A. K. M. Bahalul Rahman, Rashedur M. Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Software Engineering LUT University Lappeenranta Finland

Traditional voting procedures are non-remote, time-consuming, and less secure. While the voter believes their vote was submitted successfully, the authority does not provide evidence that the vote was counted and tallied. In most cases, the anonymity of a voter is also not sure, as the voter's details are included in the ballot papers. Many voters consider this voting system untrustworthy and manipulative, discouraging them from voting, and consequently, an election loses a significant number of participants. Although the inclusion of electronic voting systems (EVS) has increased efficiency;however, it has raised concerns over security, legitimacy, and transparency. To mitigate these problems, blockchain technology has been leveraged and smart contract facilities with a combination of artificial intelligence (AI) to propose a remote voting system that makes the overall voting procedure transparent, semi-decentralized, and secure. In addition, a system that aids in boosting the number of turnouts in an election through an incentivization policy for the voters have also developed. Through the proposed virtual campaigning feature, the authority can generate a decent amount of revenue, which downsizes the overall cost of an election. To reduce the associated cost of transactions using smart contracts, this system implements a hybrid storage system where only a few cardinal data are stored in the blockchain network. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Smart contract

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An Improved Farmland Fertility Algorithm with Hyper-Heuristic Approach for Solving Travelling Salesman Problem

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computer Modeling in engineering & Sciences 2023年第6期135卷 1981-2006页

作者： Farhad Soleimanian Gharehchopogh Benyamin Abdollahzadeh Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

Travelling Salesman Problem(TSP)is a discrete hybrid optimization problem considered *** aims to discover the shortest Hamilton route that visits each city precisely once and then returns to the starting point,making it the shortest route *** paper employed a Farmland Fertility Algorithm(FFA)inspired by agricultural land fertility and a hyper-heuristic technique based on the Modified Choice Function(MCF).The neighborhood search operator can use this strategy to automatically select the best heuristic method formaking the best ***-Kernighan(LK)local search has been incorporated to increase the efficiency and performance of this suggested approach.71 TSPLIB datasets have been compared with different algorithms to prove the proposed algorithm’s performance and *** results indicated that the proposed algorithm outperforms comparable methods of average mean computation time,average percentage deviation(PDav),and tour length.

关键词： Travelling salesman problem optimization farmland fertility optimization algorithm Lin-Kernighan

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Classification of Fetal Cardiac Arrhythmia Using Heart Rate Variability and Machine Learning 5

Classification of Fetal Cardiac Arrhythmia Using Heart Rate ...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Shahid, Muhammad Arslan Shaukat, Arslan Akram, Muhammad Usman Butt, Wasi Haider College of Electrical and Mechanical Engineering Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350394764

Fetal arrhythmias can lead to cardiac failure or death;thus, early detection is crucial but challenged by noise and artifacts. This paper investigates fetal arrhythmia detection using time, frequency, and non-linear Heart Rate Variability (HRV) features extracted from fetal ECG signals in the NIFEEG dataset. Machine learning classifiers including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, XGBoost, Naive Bayes, and Convolutional Neural Network (CNN) are evaluated. The HRV features provide insights into fetal heart rate patterns. While SVM, KNN, Decision Tree, and XGBoost show promise, CNN demonstrates the highest performance with 98.16% accuracy, 100% sensitivity, 96.15% specificity, and 98.11% F1-score. This highlights CNN's potential for early and reliable fetal arrhythmia detection. Statistical analysis supports the significance of HRV features, paving the way for improved prenatal monitoring. To further improve model generalizability, future work should focus on expanding the dataset size, exploring advanced deep neural network architectures such as LSTMs, and integrating supplementary physiological inputs like uterine contractions. Overall, this study demonstrates the capabilities of HRV-based machine learning approaches, especially CNNs, for robust fetal arrhythmia detection, offering valuable clinical implications for early diagnosis and management. © 2024 IEEE.

关键词： Feature extraction

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Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

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Multimedia Tools and Applications 2025年第13期84卷 11451-11474页

作者： Moradi, MohammadMahdi Hassanzadeh, Alireza Haghanifar, Arman Ko, Seok Bum Faculty of Computer Science and Software Engineering Concordia University MontrealQC Canada Faculty of Electrical Engineering Shahid Beheshti University Tehran Tehran Iran Department of Electrical & Computer Engineering University of Saskatchewan SaskatoonSK Canada

This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen

关键词： COVID-19

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Priority-Aware Resource Allocation for VNF Deployment in Service Function Chains Based on Graph Reinforcement Learning

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computers, Materials & Continua 2025年第5期83卷 1649-1665页

作者： Seyha Ros Seungwoo Kang Taikuong Iv Inseok Song Prohim Tam Seokhoon Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Republic of Korea School of Digital Technologies American University of Phnom PenhPhnom Penh12106Cambodia Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Republic of Korea

Recently,Network Functions Virtualization(NFV)has become a critical resource for optimizing capability utilization in the 5G/B5G *** decomposes the network resource paradigm,demonstrating the efficient utilization of Network Functions(NFs)to enable configurable service priorities and resource *** Service Providers(TSPs)face challenges in network utilization,as the vast amounts of data generated by the Internet of Things(IoT)overwhelm existing *** applications,which generate massive volumes of diverse data and require real-time communication,contribute to bottlenecks and *** this context,Multiaccess Edge Computing(MEC)is employed to support resource and priority-aware IoT applications by implementing Virtual Network Function(VNF)sequences within Service Function Chaining(SFC).This paper proposes the use of Deep Reinforcement Learning(DRL)combined with Graph Neural Networks(GNN)to enhance network processing,performance,and resource pooling *** facilitates feature extraction through Message-Passing Neural Network(MPNN)*** with DRL,Deep Q-Networks(DQN)are utilized to dynamically allocate resources based on IoT network priorities and *** focus is on minimizing delay times for VNF instance execution,ensuring effective resource placement,and allocation in SFC deployments,offering flexibility to adapt to real-time changes in priority and *** results demonstrate that our proposed scheme outperforms reference models in terms of reward,delay,delivery,service drop ratios,and average completion ratios,proving its potential for IoT applications.

关键词： Deep reinforcement learning graph neural network multi-access edge computing network functions virtualization software-defined networking

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AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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