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Efficient Deep CNN Model for COVID-19 Classification

computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic *** recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable ***,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN ***,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five ***,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)***,the utilized dataset is clustered into normal and four major attack categories via the clustering ***,a deep learning method is projected for building an efficient SDN-based intrusion detection *** results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD ***,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing *** example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification KDD dataset

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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Advancing Hardware Implementation of Hyperdimensional Computing for Edge Intelligence

Advancing Hardware Implementation of Hyperdimensional Comput...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Eman Hassan Meriem Bettayeb Baker Mohammad Electrical Engineering and Computer Science Department System on Chip Lab Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

Hyperdimensional Computing (HDC) is a promising paradigm known for its energy efficiency and simplicity. Human brain functions inspire HDC, which operates in high-dimensional spaces with low-precision data, making it suitable for efficient machine-learning applications. This paper analyzes the State-of-the-Art (SOTA) significance and challenges of implementing HDC on diverse hardware platforms, including Application Specific Integrated Circuits (ASIC), Central Processing Units (CPU), and Field-Programmable Gate Arrays (FPGA), and explores its potential in In-Memory Computing (IMC). Also, the significance of hardware-accelerated Associative Memory (AM) in HDC systems is discussed, emphasizing its role in optimizing overall performance and efficiency.

关键词： Application specific integrated circuits Associative memory Circuits and systems Machine learning In-memory computing Hardware Energy efficiency

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Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and Vision Transformer ViT 17

Smart Soft Sensor for Froth flotation quality monitoring usi...

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17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023

作者： Hasidi, Oussama Bendaouia, Ahmed Abdelwahed, El Hassan Abdellah, El Alaoui-Chrifi Moulay Boussetta, Abdelmalek Benzakour, Intissar Bourzeix, Francois Oubouhss, Kaotar Mohammed Vi Polytechnic University Lisi Laboratory Computer Science Departement Uca Analytics Lab Marrakech Morocco Cadi Ayyad University Lisi Laboratory Computer Science Departement Marrakech Morocco Reminex Managem Group R&d and Engineering Center Marrakech Morocco Mohammed Vi Polytechnic University Analytics Lab Rabat Morocco National School of Applied Science Uae Department of Electrical and Industrial Engineering Tangier Morocco

ISBN: (纸本)9798350370911

Smart sensor technologies have gained prominence in the field of machine vision-based applications. Within this context, the utilization of machine vision systems for monitoring and controlling flotation plants has become increasingly prevalent. This paper addresses the critical need for accurate froth image classification in the development of an online machine vision-based control system. This paper explores the recent advancements in machine learning, with a focus on deep learning techniques. Specifically, it introduces the application of a Vision Transformer (VIT) model as an alternative to the traditional Convolutional Neural Network (CNN) for classifying froth images collected from an industrial lead flotation circuit. The results of this research demonstrate the remarkable performance of the ViT model, achieving an impressive overall accuracy rate of 96% when combined with pre-trained feature extractor models. This achievement surpasses the capabilities of conventional artificial neural network (CNN) classifiers of the state of art in terms of both accuracy and computational efficiency. These findings underscore the promising potential of deep learning neural networks in froth image analysis, highlighting their significance in the advancement of smart sensor-based machine vision systems within the field of flotation plant control. © 2023 IEEE.

关键词： computer vision

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Abstract: A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT

Abstract: A Spatiotemporal Model for Precise and Efficient F...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Ploner, Stefan B. Chen, Siyu Won, Jungeun Husvogt, Lennart Breininger, Katharina Schottenhamml, Julia Fujimoto, James G. Maier, Andreas K. Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Electrical Engineering and Computer Science Research Laboratory for Electronics Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9783658416560

Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning a focused light spot across the retina, acquiring sequential cross-sectional images to generate volumetric data. Patient eye motion during the acquisition poses unique challenges: Non-rigid, discontinuous distortions can occur, leading to gaps in data and distorted topographic measurements. We present a new distortion model and a corresponding fully-automatic, reference-free optimization strategy for computational motion correction in orthogonally raster-scanned, retinal OCT volumes. Using a novel, domain-specific spatiotemporal parametrization of forward-warping displacements, eye motion can be corrected continuously for the first time. Parameter estimation with temporal regularization improves robustness and accuracy over previous spatial approaches. We correct each A-scan individually in 3D in a single mapping, including repeated acquisitions used in OCT angiography protocols. Specialized 3D forward image warping reduces median runtime to © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： Optical tomography

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What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding 41

What Improves the Generalization of Graph Transformers? A Th...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony BrookNY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks. Copyright 2024 by the author(s)

关键词： Stochastic systems

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Meta: Toward a Unified, Multimodal Dataset for Network Intrusion Detection Systems

IEEE Data Descriptions

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IEEE Data Descriptions 2024年 1卷 50-57页

作者： Syed Wali Yasir Ali Farrukh Irfan Khan Nathaniel D. Bastian Clean and Resilient Energy System Lab (CARES) Department of Electrical & Computer Engineering Texas A&M University College Station TX USA Army Cyber Institute Department of Electrical Engineering & Computer Science United States Military Academy West Point NY USA

The lack of standardization across publicly available network intrusion detection datasets presents a significant challenge in developing generalizable machine learning-based models. These existing datasets often exhibit inconsistencies in feature sets and typically focus only on flow-level data, overlooking critical elements such as payload information and time-window-based contextual features. Such limitations make it difficult to detect sophisticated, time-sensitive attacks that rely heavily on both payload analysis and temporal patterns in network behavior. To address these challenges, we propose a unified multimodal dataset that integrates flow, payload, and contextual features from several renowned datasets. Our methodology implements a three-stage pipeline that processes raw packet capture (PCAP) files, extracts detailed metadata, and synchronizes the flow and payload data with time-based contextual features to ensure a comprehensive and enriched dataset. This approach allows for extensive cross-dataset validation, enabling the development of more robust and adaptable machine learning models for network intrusion detection. By providing a unified feature space and incorporating payload and contextual data, the dataset enhances the ability to detect a wider range of network threats with greater precision. IEEE SOCIETY/COUNCIL Communications Society (COMSOC) DATA TYPE/LOCATION CSV, Network traffic (flow and payload content); n/a DATA DOI/PID 10.21227/d8at-gb29

关键词： Payloads Feature extraction Metadata labeling Pipelines Machine learning Data mining Network intrusion detection Telecommunication traffic Analytical models

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Performance Evaluation of Secure and Privacy-preserving DNS at the 5G Edge 20

Performance Evaluation of Secure and Privacy-preserving DNS ...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Hanna, Yacoub Pineda, Diana Akkaya, Kemal Aydeger, Abdullah Harrilal-Parchment, Ricardo Albalawi, Hamdah Florida International University Advanced Wireless and Security Lab MiamiFL33174 United States Florida Institute of Technology Dept. of Electrical Engineering and Computer Science MelbourneFL32901 United States

ISBN: (纸本)9798350324334

With the improved network performance and efficiency, 5G has been a very appealing alternative for various applications and devices, including but not limited to Industrial IoT (IIoT) applications. However, since IIoT applications require real-time transmission guarantees for time-critical data, optimizing the 5G network performance has attracted much research recently by pushing critical services to the 5G edge. One of such services is Domain Name System (DNS) which is typically offered by ISPs to serve 5G networks. However, due to its heavy role on Internet traffic, DNS has seen many attacks in the past in terms of its authenticity of records and exposure of user requests. Therefore, there have been many variants of DNS protocol such as DNSSEC and DNS over TLS (DoT) to address these security and privacy issues. As such these new protocols need to be integrated into 5G edge and their overhead should be investigated for their potential use in time-critical applications. In this paper, we consider deploying secure and privacy-preserving DNS to the 5G base stations and investigate the feasibility and performance of such an edge computing approach. To this end, we utilize SDN capabilities and forward packets to a local SDN controller for extracting and processing DNS queries. Thus, the DNS packets are served at the 5G edge with optional security and privacy features without being forwarded to remote ISP servers. Our approach is implemented in a virtualized 5G testbed network, and we present various DNS performance results via different metrics and scenarios. © 2023 IEEE.

关键词： 5G mobile communication systems

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