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IEEE Conference on Vehicular Technology (VTC)

作者： Nguyen Thi Thanh Van Nguyen Cong Luong Feng Shaohan Shimin Gong Dusit Niyato Dong In Kim Faculty of Computer Science PHENIKAA University Hanoi Vietnam School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China College of Computing and Data Sciene Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this paper, we propose a simultaneous wire-less information and power transfer (SWIPT)-enabled Ad hoc network underlay rate-splitting multiple access (RSMA)-based system with intelligent reflecting surface (IRS). Therein, a base station (BS) in a primary network uses RSMA to serve primary users (PUs), and secondary user (SU) pairs constitute an Ad hoc network sharing the spectrum with the primary network. The power splitting (PS)-based SWIPT protocol is used in the Ad hoc network that allow the SU receivers to decode the information and harvest energy simultaneously. An IRS is deployed to further enhance the system performance. We formulate optimization problems that optimize the common data rate allocation and beamformers associated with the common and private messages of RSMA, the beamformers and the PS factor in the Ad hoc network, and reflection coefficients of the IRS to maximize the minimum rate of the PUs while satisfying the requirements of harvested energy and data rate of the SU pairs. The optimization problems are non-convex and challenging to be solved. We propose a low complexity algorithm based on alternating descent techniques. Numerical results demonstrate the effectiveness and improvement of the proposed framework compared with the framework based on existing multiple access schemes, i.e., non-orthogonal multiple access (NOMA).

关键词： Cellular networks NOMA Vehicular and wireless technologies System performance Simulation Throughput Ad hoc networks

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Efficient Model Training in Decentralized systems with Federated Learning 2

Efficient Model Training in Decentralized Systems with Feder...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Rao, M.S. Srinivasa Saraswathy, S. Neela, V. Arun, M.R. Gupta, Sandeep Taqui, Syed Noeman Vignana Jyothi Institute of Engineering & Technology Vnr Department of Mechanical Engineering Telangana Hyderabad India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India School of Computing and Information Technology Reva University Karnataka Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Department of Electrical Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

In the fast-changing field of machine learning, data privacy and model training efficiency are crucial. Federated Learning (FL) is a breakthrough method for training models on several decentralized devices or servers without centralizing raw data. This study examines Federated Learning's challenges in optimizing model training in dispersed systems. The first stage is to examine the Federated Learning (FL) framework and its merits, particularly in protecting data privacy and reducing server dependence. The next section will discuss communication overhead, stragglers, and non-IID (independent and identically distributed) data issues in a decentralized training model. We developed novel optimization methodologies and adaptive algorithms to improve Federated Learning (FL) efficiency. This includes updating asynchronous models, adaptability-based communication frequency, and non-IID data management. We also present a resilient aggregation method that mitigates Byzantine failures in participating nodes. We demonstrate that our methods improve convergence rate, communication costs, and model performance trained with Federated Learning (FL) by conducting a series of comprehensive experiments and analyzing real-world case studies. We show that these enhancements do not compromise data privacy, preserving Federated Learning (FL) principles. In conclusion, this study advances Federated Learning in decentralized systems. In an age of data sensitivity, this approach could help implement efficient, scalable, and privacy-preserving machine-learning methods. © 2023 IEEE.

关键词： Byzantine Failures Communication Efficiency Data Privacy Decentralized systems Federated Learning Model Training Non-IID Data

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Analysis of Cyber Security Threats in Payment Gateway Technology

Analysis of Cyber Security Threats in Payment Gateway Techno...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Hari Prapan Sharma Somanchi Hari Krishna Vijay Prakash R Mohit Tiwari Tripti Tiwari Madarapu Naresh Kumar Institute of Business Management GLA University Mathura Uttar Pradesh Department of Business Management Vignana Bharathi Institute of Technology School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Department of Management Studies BVIMR Delhi Ministry of Electronics and Information Technology National Informatics Centre Delhi

There has been a meteoric rise in the use of online payment systems recently. Multiple e-payment systems exist, each designed to provide the highest degree of security while maintaining the greatest possible convenience for online shoppers. Cyber-attack techniques, on the other hand, are developing at the same rapid pace as security mechanisms. The authors of this paper examine the history of electronic payment systems, from the development of the corresponding language through the emergence of today's standard electronic payment systems. It also reveals a lack of security measures and approaches to fixing the problem. The current survey research makes a significant contribution by outlining the state of the electronic payment system framework and the possibilities it presents for the development of e-commerce in the future. The rates of suspicious purchases, which will serve as a yardstick in the creation of a trustworthy e-payment system, have been briefly discussed and analyzed.

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Online Seizure Prediction System: A Novel Probabilistic Approach for Efficient Prediction of Epileptic Seizure with iEEG Signal

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Open Biomedical Engineering Journal 2022年第1期16卷

作者： Abbaszadeh, Behrooz Teixeira, Cesar A. D. Yagoub, Mustapha C. E. School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada Unive Coimbra CISUC-Center for Informatics and Systems of the University of Coimbra Department of Informatics Engineering Coimbra Portugal

Background: 1% of people around the world are suffering from epilepsy. It is, therefore crucial to propose an efficient automated seizure prediction tool implemented in a portable device that uses the electroencephalogram (EEG) signal to enhance epileptic patients’ life quality. Methods: In this study, we focused on time-domain features to achieve discriminative information at a low CPU cost extracted from the intracranial electroencephalogram (iEEG) signals of six patients. The probabilistic framework based on XGBoost classifier requires the mean and maximum probability of the non-seizure and the seizure occurrence period segments. Once all these parameters are set for each patient, the medical decision maker can send alarm based on well-defined thresholds. Results: While finding a unique model for all patients is really challenging, and our modelling results demonstrated that the proposed algorithm can be an efficient tool for reliable and clinically relevant seizure forecasting. Using iEEG signals, the proposed algorithm can forecast seizures, informing a patient about 75 minutes before a seizure would occur, a period large enough for patients to take practical actions to minimize the potential impacts of the seizure. Conclusion: We posit that the ability to distinguish interictal intracranial EEG from pre-ictal signals at some low computational cost may be the first step towards an implanted portable semi-automatic seizure suppression system in the near future. It is believed that our seizure prediction technique can conceivably be coupled with treatment techniques aimed at interrupting the process even prior to a seizure initiates to develop. © 2022 Abbaszadeh et al.

关键词： Epileptic seizure Intracranial EEG Low CPU cost Matthews’s correlation coefficient Probabilistic framework Time domain features XGBoost classifier

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InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity

arXiv

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arXiv 2023年

作者： Chuang, Chun-Hsiang Fang, Shao-Xun Huang, Chih-Sheng Ding, Weiping Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Education and Learning Technology National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Department of Artificial Intelligence Research and Development Elan Microelectronics Corporation Hsinchu Taiwan College of Artificial Intelligence and Green Energy National Yang Ming Chiao Tung University Hsinchu Taiwan College of Electrical Engineering and Computer Science National Taipei University of Technology Hsinchu Taiwan School of Information Science and Technology Nantong University Nantong China Institute of Information Systems and Applications College of Electrical Engineering and Computer Science NTHU Treasure Taipei Chapter of IEEE Computational Intelligence Society Taiwan

Deciphering brain network topology can enhance the depth of neuroscientific knowledge and facilitate the development of neural engineering methods. Effective connectivity, a measure of brain network dynamics, is particularly useful for investigating the directional influences among different brain regions. In this study, we introduce a novel brain causal inference model named InfoFlowNet, which leverages the self-attention mechanism to capture associations among electroencephalogram (EEG) time series. The proposed method estimates the magnitude of directional information flow (dIF) among EEG processes by measuring the loss of model inference resulting from the shuffling of the time order of the original time series. To evaluate the feasibility of InfoFlowNet, we conducted experiments using a synthetic time series and two EEG datasets. The results demonstrate that InfoFlowNet can extract time-varying causal relationships among processes, reflected in the fluctuation of dIF values. Compared with the Granger causality model and temporal causal discovery framework, InfoFlowNet can identify more significant causal edges underlying EEG processes while maintaining an acceptable computation time. Our work demonstrates the potential of InfoFlowNet for analyzing effective connectivity in EEG data. The findings highlight the importance of effective connectivity in understanding the complex dynamics of the brain network. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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Sketch–to–3D: Transforming Hand-Sketched Floorplans into 3D Layouts

Sketch–to–3D: Transforming Hand-Sketched Floorplans into 3...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Muhammad Usman Abdullah Almulhim Mohammad Alaseri Mona Alzahran Hamzah Luqman Saeed Anwar Department of Information & Computer Science KFUPM Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran Saudi Arabia Center for Intelligent Secure Systems KFUPM Dhahran Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia The Australian National University Australia University of Canberra Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

The conventional approach for analyzing built environments' spatial relations and structural components utilizes 2D floorplans as the primary representation method. However, these 2D blueprints can be challenging and potentially misleading for end-users who lack expertise in architecture or design, as they rely on low-dimensional abstractions to convey complex, high-dimensional information. This paper introduces an automated pipeline that transforms hand-sketched 2D floorplans into immersive 3D layouts, addressing this issue through a supervised learning approach. Initially, a dataset of diverse hand-sketched floorplans is compiled, each labeled with semantics for the object and structural element. Next, supervised deep learning models were developed for layout and object detection using this dataset. The performance of the supervised models was evaluated on a test dataset. The 2D digital designs were then converted to 3D immersive layouts through automated mapping. The entire pipeline is an interactive application that allows novice users to capture a picture of their hand-sketched floorplan and view their design ideas come to life as immersive 3D layouts populated with virtual crowds.

关键词： Deep learning Solid modeling Vocabulary Three-dimensional displays Shape Digital images Layout Pipelines Transforms Virtual reality

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Applying Dynamic systems to Social Media by Using Controlling Stability

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Computational Intelligence and Neuroscience 2022年 2022卷 4569879页

作者： Abdullah Hamad, Abdulsattar Thivagar, M. Lellis Bader Alazzam, Malik Alassery, Fawaz Hajjej, Fahima Shihab, Ali A. School of Mathematics Madurai Kamaraj University Madurai India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Mathematics Department Education for Pure Sciences College Tikrit University Tikrit Iraq

This study focuses on hybrid synchronization, a new synchronization phenomenon in which one element of the system is synced with another part of the system that is not allowing full synchronization and nonsynchronization to coexist in the system. When limt→∞Y-αX=0, where Y and X are the state vectors of the drive and response systems, respectively, and Wan (α = 1)), the two systems' hybrid synchronization phenomena are realized mathematically. Nonlinear control is used to create four alternative error stabilization controllers that are based on two basic tools: Lyapunov stability theory and the linearization approach. © 2022 Abdulsattar Abdullah Hamad et al.

关键词： Synchronization

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A Review of Effectiveness and Efficiency Methodology of Decision Support System for Selecting Suppliers

A Review of Effectiveness and Efficiency Methodology of Deci...

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computer science and Emerging Technologies (CSET), International Conference on

作者： Sharaf Alzoubi Khalid Thaher Amayreh Mazen Mohammed Farea Yousef A. Baker El-Ebiary Syarilla Iryani Ahmad Saany Neeraj Bisht Information Technology department College of Computer Science and Informatics Amman Arab University (AAU) Jordan Business Administration Department Faculty of Finance Science and Business Irbid National University Jordan Faculty of Finance & Administrative Sciences Al-Madinah International University Malaysia Faculty of Informatics and Computing UniSZA Malaysia Birla Institute of Applied Sciences Bhimtal India

In today's highly competitive global business environment, effective supplier selection plays a crucial role in the success and sustainability of organizations. Decision Support systems (DSS) have emerged as powerful tools to aid in this complex process by providing valuable insights and data-driven recommendations for selecting suppliers. This research paper presents a comprehensive review of the methodologies used to assess the effectiveness and efficiency of Decision Support systems employed in supplier selection processes. The primary objective of this study is to evaluate and compare the various methodologies applied in Decision Support systems, focusing on their effectiveness and efficiency in assisting decision-makers during supplier selection. The research critically analyzes a range of published literature, academic papers, and case studies to highlight the strengths and limitations of different DSS methodologies in this context. The paper commences by elucidating the fundamental concepts of supplier selection and the significance of Decision Support systems in streamlining this multifaceted process. Subsequently, a systematic review of existing literature is conducted to identify the key methodologies employed in DSS frameworks. Furthermore, the research explores the novel integration of emerging technologies, such as Artificial Intelligence, Machine Learning, and Big Data analytics, within Decision Support systems for supplier selection. This investigation aims to discern how these advanced techniques contribute to the enhancement of decision-making precision and the overall efficiency of the selection process. By critically analyzing and comparing the effectiveness and efficiency of various Decision Support System methodologies, this research seeks to provide valuable insights for both academics and practitioners in supply chain management. The findings of this study will enable decision-makers to make informed choices regarding the adoption and customiz

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Mitigating Voltage Sag in Power systems with Proportional Integral Control using Tangent Search Algorithm and Multi-Fidelity Bayesian Neural Network 8

Mitigating Voltage Sag in Power Systems with Proportional In...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Meena, Radhey Shyam Singh, Harsimran Vasudevan, K. Ekambaram, Saranya Katiyar, Atul Kaliappan, S. Indian Institute of Technology Industry Coordinator Odl Uttar Pradesh Kanpur208016 India Guru Kashi University Department of Electrical Engineering Punjab Bathinda151302 India Parul University Department of Electrical Engineering Gujarat Vadodara391760 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Mjp Rohilkhand University Bareilly Faculty of Engineering and Technology Department of Electrical Engineering Uttar Pradesh 243006 India Lovely Professional University Division of Research and Development Delhi Jalandhar144411 India

ISBN: (纸本)9798350376425

Mitigation strategies for voltage sag in power systems using dynamic voltage restorers (DVRs) involve compensating for voltage dips by injecting the required voltage to maintain stability. However, DVRs present problems such as high initial costs in installation and subsequent costs in maintenance and replacement;it only corrects voltage sag within its capability to handle severe or long periods of voltage sags. Moreover, DVRs may need complex control algorithms and most accurate tuning for optimal performance under various grid conditions. To overcome these drawbacks, this manuscript proposes a hybrid approach for voltage sag mitigation in power systems with Proportional Integral (PI) controller. The proposed method is the combined execution of both Tangent Search Algorithm (TSA) and Multi-fidelity Bayesian Neural Network (MFBNN). Hence, it is named as hybrid TSA-MFBNN approach. The major aim is to minimize the impact of voltage sags on sensitive equipment and maintain consistent power quality across the network and reduce the integral squared error (ISE) and Total Harmonic Distortion (THD) of the system. The PI controller's gain parameters are optimized by the proposed TSA, and its optimum values are predicted by the MFBNN. By then, the proposed technique is executed in MATLAB and compared with different existing approaches. The proposed method outperforms all existing approaches such as Artificial Neural Network (ANN), Salp Swarm Algorithm (SSA), and Adaptive-Network Fuzzy Inference System (ANFIS). The proposed technique demonstrates a THD of 2.38% and an ISE of 0.00410, indicating superior performance in reducing harmonic distortions and minimizing error. © 2024 IEEE.

关键词： Harmonic distortion

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Learning to Generate and Predict New Conditional Small Organic Molecules

SSRN

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SSRN 2024年

作者： Alsowat, Amal Allahyani, Raghad Alsulami, Reham Almaqati, Shaima Alafif, Tarik Hawsawi, Mohammed Alsaidi, Hamed Alsubait, Tahani Department of Computer Science Jamoum University College Umm Al-Qura University Jamoum25375 Saudi Arabia Department of Chemistry Faculty of Applied Science Umm AL-Qura University Al Taif Road Makkah24382 Saudi Arabia Department of Chemistry Jamoum University College Umm Al-Qura University Jamoum25375 Saudi Arabia Department of Information Science College of Computers and Information Systems Umm Al-Qura University Makkah24382 Saudi Arabia

Organic molecules are important for many applications, such as drug discovery, pharmaceuticals, materials, and agrochemicals. However, synthesizing new organic molecules in chemical labs is expensive and time-consuming. This paper proposes a new approach to generating and predicting new conditional small organic molecules using machine learning. The approach employs a Generative Adversarial Network, more specifically a Wasserstein GAN (WGAN), to generate new molecules, and a Vision Transformer (ViT) to predict the categories of the generated molecules. To ensure that the generated organic compounds are possible and correct, certain conditions, such as specifying a type of bond and element, are added to the WGAN. The resulting dataset, called GenOrgo, consists of 400 newly generated conditional molecules, labeled into four categories. The ViT is evaluated using the GenOrgo dataset to evaluate its prediction performance. The ViT shows its effectiveness for predicting the categories of the generated organic molecules. © 2024, The Authors. All rights reserved.

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