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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-ID2R: An Intelligent Device Disaster Recovery Mechanism in Multipath Scenarios

Multi-ID2R: An Intelligent Device Disaster Recovery Mechanis...

引用

2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Wang, Wenxiao Zhang, Yuyang Dong, Ping Du, Xiaojiang Yu, Chengxiao Zhang, Hongke Aitsaadi, Nadjib Beijing Jiaotong University School of Electronic and Information Engineering China Pengcheng Laboratory Dept. of New Networks Shenzhen China Stevens Institute of Technology Dept. of Electrical and Computer Engineering HobokenNJ United States Université Paris-Saclay Uvsq David Versailles France

ISBN: (纸本)9798350351255

At present, multipath transmission realized by multi-interface devices and bandwidth aggregation technology meets user demand for high-bandwidth communication in 6G wireless networks. However, multipath transmission systems face the threat of single-point failure by multi-interface servers themselves. Existing solutions for such failure are not suitable for multipath transmission scenarios, and this seriously limits the ability to bandwidth aggregation and reduces the reliability and invulnerability of the multipath transmission system in 6G wireless networks. In this paper, we propose a novel Intelligent Device Disaster Recovery (Multi-ID2R) mechanism to solve the single-point failure in multipath and aggregated environments for the first time. In particular, we establish the Multi-Dimensional Parameter Joint Analysis model (MDPJA) and propose an algorithm for judging the running state of multi-interface devices. The algorithm takes into account the different network parameters of the paths, including delay, packet loss rate, and throughput. Moreover, an intelligent switching mechanism based on service quality is designed. Multi-ID2R comprehensively considers the characteristics of the business and the current parameters of multipath networks to determine the moment of switching to flexibly adjust switching strategies. Finally, we deploy the mechanism on multi-interface servers in actual networks. Experiments demonstrate that, compared with Virtual Router Redundancy Protocol, Gateway Load Balancing Protocol, and Hot Standby Router Protocol, Multi-ID2R effectively improves the reliability and invulnerability of multi-interface server in 6G wireless networks. © 2024 IEEE.

关键词： Bandwidth

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Network-based Intrusion Detection Datasets: A Survey 23

Network-based Intrusion Detection Datasets: A Survey

引用

23rd International Arab Conference on Information technology, ACIT 2022

作者： Ahmed, Lubna Ali Hassan Hamad, Yahia Abdalla Mohamed Abdalla, Ahmed Abdallah Mohamed Ali Sudan University of Science and Technology Dept. of Electronics Engineering Khartoum Sudan Sudan University of Science and Technology Dept. of Computer Networks and Systems Khartoum Sudan

ISBN: (纸本)9798350320244

The increasing reliance on computers and the internet has increased the need for protection against attacks and security threats. A network-based intrusion detection system (NIDS) is one of the promising research fields that could highly contribute to cyber-Attacks detection and mitigation. Datasets are necessary for NIDS training and evaluation. This paper provides a comprehensive survey of publicly available NIDS datasets. The study covers the most used NIDS datasets, each dataset is briefly described along with its main properties and shortcomings. Moreover, the paper highlights the most important dataset characteristics and provides a clear datasets comparison accordingly. Finally, the paper discusses observations and provides some recommendations to help researchers identify future work. © 2022 IEEE.

关键词： Network security

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Multi-Access Edge Computing Using Intelligent Mobile User Resource Allocation In 6G 4

Multi-Access Edge Computing Using Intelligent Mobile User Re...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Gaid, AbdulGuddoos S. A. Ahmed, Zeinab E. Elbasheir, Mohammed S. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University Faculty of Engineering Department of Electrical and Electronic Engineering Altaif Saudi Arabia College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Malaysia School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan

ISBN: (纸本)9798350372632

Edge computing plays a crucial role in the advancement of Beyond 5G (B5G) and 6G networks. The substantial growth in network traffic and computational requirements necessitates the implementation of multi-access edge computing (MEC) as a viable solution. MEC aims to bring the capabilities of cloud computing closer to end-users by integrating them into the radio access network (RAN). This research introduces an optimal approach to connect mobile users with MEC hosts and access points, effectively distributing radio and computing resources to individual mobile users. The objective is achieved by lowering the total user transmit power while abiding by latency constraints in both the communication and computation phases, using the Grey Wolf Optimization technique (GWO). A comparison to alternative smart techniques demonstrates the effectiveness of the suggested approach © 2024 IEEE.

关键词： 5G mobile communication systems

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User-Resource Associations for Delay Minimization in THz-Enabled Mobile Edge Computing Systems 15

User-Resource Associations for Delay Minimization in THz-Ena...

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15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Song, Heekang Choi, Wan School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Institute of New Media and Communications Seoul National University Seoul Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

Mobile edge computing (MEC) and terahertz (THz)-enabled communication systems are gaining significant attention for their potential to reduce user service delays in future mobile networks. This paper leverages user offloading and resource management in MEC systems operating at THz frequencies to minimize expected user service delays. Numerical results demon-strate that our approach outperforms the baseline algorithm. © 2024 IEEE.

关键词： Mobile edge computing

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Task Reverse Offloading with Deep Reinforcement Learning in Multi-Access Edge Computing 9

Task Reverse Offloading with Deep Reinforcement Learning in ...

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Khalifa, Othman O. Ahmed, Zeinab E. Barakat, Mohammed Elnaim, Areeg Ali Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia International Islamic University Malaysia Department of Electrical and Computer Engineering Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya International Islamic University Malaysia Electrical and Computer Engineering Dept. Malaysia University of Gezira Computer Engineering Dept. Sudan Alneelain University College of Engineering Khartoum Sudan

ISBN: (纸本)9798350325218

The Multi-access Edge Computing (MEC) technology's quick development greatly benefits the Collaborative Mobile Infrastructure System (CMIS). To combine the data and produce tasks, crowd-sensing data will be transferred to the MEC server in CMIS. Nevertheless, if there are too many devices, it becomes extremely difficult for MEC to decide appropriately based on the data from the devices and infrastructure. This study builds a framework for reverse offloading that carefully balances the relationship between task completion time and user mobile energy consumption. Moreover, to decrease system use generally, an adaptive optimal reverse offloading method based on Deep Q-Network is created (DQN). The results of the simulations demonstrate that the suggested approach may successfully minimize energy consumption and work latency when compared to full local and fixed offloading techniques. © 2023 IEEE.

关键词： Reinforcement learning

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Energy Consumption for Cognitive Radio Network Enabled Multi-Access Edge Computing 3

Energy Consumption for Cognitive Radio Network Enabled Multi...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Elnaim, Areeg Ali Babeker, Amin Barakat, Mohammed Gaid, Abdulguddoos S.A. Saeed, Mamoon M. Ahmed, Zainab. E. Saeed, Rashid A. College of Engineering and Communication Alneelain University Khartoum Sudan School of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Taiz University Dept. of Communication & Computer Engineering Taiz Yemen Faculty of Engineering Department of Communications and Electronics Engineering Yemen Gezira University Department of Electrical and Computer Engineering Khartoum Sudan College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia

ISBN: (纸本)9798350305333

This article discusses optimizing energy Consumption in CRN-enabled Multi-access Edge Computing (MEC) systems, presenting a novel approach to optimize the energy Consumption of a cognitive radio network-enabled MEC system using iterative optimization techniques. The proposed model aims to minimize the system's energy consumption while maintaining its performance and quality of service requirements. The iterative optimization algorithm is used to find the optimal allocation of resources, such as computing, communication, and power, among different nodes in the MEC system. As a result, the cognitive radio network enables efficient spectrum utilization by dynamically allocating available spectrum to different users based on their requirements. The performance evaluation approach is evaluated through simulations, and the results show that it can significantly improve the energy consumption of the MEC system while maintaining its performance and QoS requirements. This article contributes to developing sustainable and energy-efficient communication systems for future wireless networks. © 2023 IEEE.

关键词： Cognitive radio

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Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-Free Massive MIMO

Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-F...

引用

2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Tran, Thong-Nhat Interdonato, Giovanni Da Costa, Daniel Benevides An, Beongku Hongik University Dept. of Electronics and Computer Engineering Graduate School Korea Republic of Italy Digital Science Research Center Technology Innovation Institute Abu Dhabi Masdar City United Arab Emirates Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350370218

This paper evaluates the uplink spectral efficiency (SE) achieved by a reconfigurable intelligent surface (RIS)-aided cell-free massive MIMO system with max-min fairness (MMF) power allocation. We provide an exact closed-form expression for an achievable SE expression which assumes maximum ratio receive combining at the access points (APs) and centralized large-scale fading decoding at the central processing unit. The derived SE expression also accounts for imperfect channel estimation and spatially correlated Ricean fading channels for both direct (APs-users) and cascaded (APs-RISs-users) links. Alongside this, we provide a generalized SE expression and evaluate the performance achieved by local partial minimum mean-squared error combining. Finally, we optimally solve the RIS phase-shift selection problem in order to maximize the aggregate channel gains, and derive a closed-form solution to the MMF power allocation problem. Simulation results validate the effectiveness of the proposed framework in enhancing the unlink SE with resnect to various benchmarks. © 2023 IEEE.

关键词： Cell-free massive MIMO max-min fairness power control reconfigurable intelligent surface spectral efficiency

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Exploring Students' Attitudes Towards Robotics technology in STEM Education: A Machine Learning-Based Prediction Analysis 4

Exploring Students' Attitudes Towards Robotics Technology in...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： El Aziz, Mahmoud I. Abd Elshewey, Ahmed M. Tarek, Zahraa El-Kenawy, El-Sayed M. Ghazy, Ahmed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Education Department of Curriculum Instruction and Educational Technology Kafrelsheikh33511 Egypt Suez University Faculty of Computers and Information Computer Science Department Suez Egypt Mansoura University Faculty of Computers and Information Computer Science Department Mansoura35561 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India Kafrelsheiku University Faculty of Artificial Intelligence Dept. of Machine Learning 33516 Egypt

ISBN: (纸本)9798350387063

The aim of this paper is to predict the extent to which students will accept robotics technology in future teaching plans. The study involves students engaging in a two-week project-based learning program on robotics technology, after which they will decide whether it is suitable for them. To achieve this, the paper uses several prediction algorithms based on Machine Learning (ML), including Linear Regression (LR), Ridge Regression (RR), Elastic Net (EN) regression, and K-Nearest Neighbor (KNN) models. The sample group consists of 35 students from STEM high schools in Egypt. The research findings indicate that the students have gained confidence and satisfaction in robotics technology and believe it will have significant value in the future. The prediction results obtained from the ML regression models support these findings. The dataset used in the study includes seven input features and one target output representing the total prediction values of accepting and rejecting robotics technology in STEM schools. The regression models LR, RR, EN, and KNN achieve determination coefficients (R2) of 99.99%, 98.80%,99.20%, and 85.60%, respectively. © 2024 IEEE.

关键词： Prediction models

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Optimizing mmWave UAV networks: Mobility-Aware Deployment and Resource Allocation

Optimizing mmWave UAV Networks: Mobility-Aware Deployment an...

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IEEE Conference on Vehicular technology (VTC)

作者： Sicong Li Kazutoshi Yoshii Shigeru Shimamoto Tu Dac Ho Dept. of Communications and Computer Engineering Waseda University Tokyo Japan Dept. of Information Security and Communication Technology Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Application of Unmanned Aerial Vehicles (UAVs) as small airborne base stations is gradually becoming a research hotspot in the field of wireless communications. With the flexible deployment, wide coverage and low cost, UAV communications show great potential in the fields of emergency rescue, environmental monitoring and military operations. In particular, the easier availability of line-of-sight (LOS) links for UAVs makes its combination with millimetre wave (mmWave) technology significantly enhance the communication bandwidth and data transfer rate. However, the very limited coverage of mmWave communications greatly affects the actual data rates provided by small aerial base stations of mmWave UAVs, especially considering user movement on the ground. In this paper, we study the joint optimisation problem of UAV deployment, user association, spectrum resource allocation in millimetre-wave heterogeneous networks, and explore the impact of different user movement models on system performance. Given that this problem is NP-hard, our paper proposes an MCR-SAC method to maximize the network’s total throughput. Simulation results demonstrate an average 18% improvement in total transmission rate compared to the conventional.

关键词： Wireless communication Base stations Vehicular and wireless technologies Mobility models System performance Autonomous aerial vehicles Throughput Trajectory Resource management Millimeter wave communication

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