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MEC-IoT-Healthcare: Analysis and Prospects

computers, Materials & Continua 2023年第6期75卷 6219-6250页

作者： Hongyuan Wang Mohammed Dauwed Imran Khan Nor Samsiah Sani Hasmila Amirah Omar Hirofumi Amano Samih M.Mostafa College of Computer Science Guangdong University of Science and TechnologyDongGuan523083China Department of Medical Instrumentation Techniques Engineering Dijlah University CollegeBaghdadIraq Department of Electrical Engineering University of Engineering&TechnologyPeshawar814Pakistan Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600BangiSelangorMalaysia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their *** health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare *** of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right ***,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent *** the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big *** article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other ***,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health *** comparative analysis provides a reference for the the mobile edge computing in healthcare systems.

关键词： IoT mobile-edge computing cloud computing e-health

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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2024 International Conference on computer and Applications, ICCA 2024

作者： Nabil, Mohamed Hossam Hassan Hammam, Laila Bastawrous, Hany Ayad Ebrahim, Gamal A. The British University in Egypt Electrical Engineering Department Cairo Egypt Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Ain Shams University Faculty of Engineering Computer and Systems Engineerig Department Cairo Egypt

ISBN: (纸本)9798350367560

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Analyzing the Impact of Demographic and Operational Variables on 1-to-Many Face ID Search

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2024年第2期5卷 217-230页

作者： Pangelinan, Gabriella Bhatta, Aman Wu, Haiyu King, Michael C. Bowyer, Kevin W. Florida Institute of Technology Department of Electrical Engineering and Computer Science MelbourneFL32901 United States University of Notre Dame Department of Computer Science and Engineering Notre DameIN46556 United States

Concerns related to the proliferation of automated face recognition technology with the intent of solving or preventing crimes continue to mount. The technology being implicated in wrongful arrest following 1-to-many face identification has heightened scrutiny in the media and spawned lawsuits. We analyze the accuracy of 1-to-many face identification across African-American and Caucasian demographic groups and in the presence of blur and reduced resolution in the probe image, as might occur in "surveillance camera quality"images. Because of the high accuracy of modern face recognition algorithms with "government ID quality"images and the size of available datasets, we use the following indirect metrics to assess the relative propensity for false positive identifications: (1) the traditional d-prime statistic between mated and non-mated score distributions, (2) absolute score difference between thresholds in the high-similarity tail of the non-mated distribution and the low-similarity tail of the mated distribution, and (3) distribution of (mated - non-mated rank-one scores) across the set of probe images. We find that demographic variation patterns in 1-to-many accuracy largely, but not perfectly, follow that observed in 1-to-1 accuracy. We show that, different from the case with 1-to-1 matching accuracy, demographic comparison of 1-to-many accuracy can be affected by different numbers of identities and images across demographics. We also show that increased blur or reduced resolution of the face in the probe image can significantly increase the false positive identification rate. This point is important because the reputation of modern face recognition algorithms for high accuracy in 1-to-many face identification comes from statistics based on analyzing "government ID quality"rather than "surveillance camera quality"images. © 2024 IEEE.

关键词： Face recognition

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Fractional Processing Based Adaptive Beamforming Algorithm

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computers, Materials & Continua 2023年第7期76卷 1065-1084页

作者： Syed Asghar Ali Shah Tariqullah Jan Syed Muslim Shah Ruhul Amin Khalil Ahmad Sawalmeh Muhammad Anan Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science Northern Border UniversityArar91431Saudi Arabia Department of Software Engineering Alfaisal UniversityRiyadh11533Saudi Arabia

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity *** effectiveness of the algorithm will be studied and evaluated in this *** this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of *** algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational *** FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight *** derivation of the algorithm is provided and supported by mathematical convergence *** is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various *** results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter *** outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

关键词： Adaptive beamforming adaptive array fractional processing least mean square fractional least mean square

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FSIW Cavity-Based Self-Packaging Filtering Slot Antennas with Wide-Stopband and High Frequency Selectivity

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3503-3516页

作者： Zhou, Xin Zhang, Gang Tam, Kam-Weng Pang, Ziyu Zhang, Zhuowei Lin, Huawei Hong, Jiasheng University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China Nanjing Normal University School of Electrical and Automation Engineering Nanjing210046 China Nanjing Normal University Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing210046 China Heriot-Watt University School of Engineering and Physical Sciences Department of Electrical Electronic and Computer Engineering EdinburghEH14 4AS United Kingdom

This article proposes a novel design approach for miniaturized, highly selective, self-packaged, and wide-stopband filtering slot antennas based on C- and T-type folded substrate integrated waveguide (C-/T-FSIW) cavities. Firstly, two forms of FSIW cavity are explored and analyzed. A new coupled C-type half-mode FSIW (C-HMFSIW) cavities are developed. A filtering antenna is proposed, employing a C-HMFSIW cavity for feeding and a slot-loaded C-FSIW cavity for radiation. By introducing cross-coupling between different resonators, a radiation null (RN) is achieved. To further enhance bandwidth and frequency selectivity, a new antenna configuration is developed using a C-HMFSIW cavity for feeding and a slot-loaded T-FSIW cavity for radiation. After loading the radiation slot, the T-FSIW cavity serves as two parts: one as the main radiator and the other as a parasitic radiator opposite to it. This configuration increases antenna bandwidth and generates an additional RN. Interestingly, adjusting the position of the radiation slot allows the RN to switch between the upper and lower stopbands. The antennas proposed in this work exhibit good stopband performance and compact size. Three filtering antenna prototypes are designed, fabricated, and measured, showing consistent results between measurements and simulations. © 1963-2012 IEEE.

关键词： Radiators

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Software design and FPGA implementation of optimized medical image fusion techniques

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Multimedia Tools and Applications 2025年 1-31页

作者： Elshazly, E.A. El-Shafai, Walid El-Hoseny, Heba El-Rabaie, El-Sayed M Zahran, O. Abdelwahab, Safey A. S. El-Halawany, M.M. Fikry, R.M. Elaraby, S.M. Faragallah, Osama S. Mohamed, Wael A. Mahmoud, Korany R. Abd El-Samie, Fathi E. Cairo Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Faculty of Computer Studies Arab Open University 11681 Riyadh Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Benha University Benha Egypt Electronics and Communications Engineering Department Faculty of Engineering Helwan University Helwan Egypt Giza Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Nowadays, medical image fusion plays a crucial role in enhancing the diagnosis accuracy and the clinical decision-making process in various healthcare applications. This research work presents a comprehensive study of the design and implementation of optimized medical image fusion techniques using a combination of software and Field-Programmable Gate Array (FPGA) technologies. The proposed medical image fusion strategy is based on the utilization of Discrete Wavelet Transform (DWT) and Modified Central Force Optimization (MCFO). The implementation of the proposed technique as well as the traditional medical image fusion techniques is considered using an appropriate software design and FPGA. The presented techniques aim to overcome the limitations of traditional fusion techniques by integrating advanced image processing algorithms, optimization algorithms, and parallel computing capabilities offered by FPGA platforms. The first step in the proposed framework is to match the histogram of one of the images with that of the other, so that both images will have the same dynamic range. After that, the DWT is used to decompose the images that should be fused together. Based on some constraints, the MCFO optimization algorithm is used to evaluate the optimum level of decomposition and the optimum parameters for the best fusion quality. Finally, to improve the obtained visual quality and reinforce the information in the fusion result, an additional contrast enhancement step using adaptive histogram equalization is applied to the fusion result. Comparative study between the proposed optimized DWT-based fusion framework, the traditional Principal Component Analysis (PCA), Additive Wavelet Transform (AWT), and DWT-based fusion techniques is presented. Various metrics of fusion quality are considered, including average gradient, standard deviation, local contrast, entropy, edge strength, Peak Signal-to-Noise Ratio (PSNR), Qab/f, and processing time. The proposed optimized DWT-ba

关键词： MATLAB

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Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

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computers, Materials & Continua 2024年第9期80卷 3655-3683页

作者： Abdullah Alabdulatif Navod Neranjan Thilakarathne Mohamed Aashiq Department of Cybersecurity College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of ICT Faculty of TechnologyUniversity of ColomboColombo00700Sri Lanka Department of Computer Science and Engineering Faculty of EngineeringSouth Eastern University of Sri LankaOluvil32360Sri Lanka

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber *** the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of *** IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT *** this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS *** CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)*** proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in *** inv

关键词： Machine learning Internet of Things(IoT),DoS,DDoS cybersecurity intrusion prevention network security,feature optimization,sustainability

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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SKGDC: Effective Segmentation Based Deep Learning Methodology for Banana Leaf, Fruit, and Stem Disease Prediction

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SN computer science 2024年第6期5卷 698页

作者： Shetty, Sahana Mahesh, T.R. Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India

In agriculture, detecting plant diseases is crucial for optimal plant growth. Initially, input images are collected from three datasets: banana leaf spot diseases (BananaLSD) dataset, banana leaf dataset, and PSFD-Musa dataset. These collected images undergo filtering using the upgraded self-guided filtering (Up-SGF) model, chosen for its ability to preserve sharp edges effectively with strong smoothing, enhancing edge perception. Additionally, the improved Wiener convolution filter (IWCF) is applied to enhance image quality by reducing noise while preserving features. Following pre-processing, the images are segmented using spatial kernelized gravity-based density clustering (SKGDC) to identify disease-affected regions. Kaze, Blob, and histogram of oriented gradient (HOG) methods are utilized to extract relevant features: Kaze for scale-invariant key points, Blob for identifying and characterizing areas with shared color or intensity characteristics, and HOG for detecting edges and contours. These extracted features are then fused together using convolutional pyramid fusion (CPF). Finally, the hybrid channel attention-based YOLOv8 (HCA-YOLOv8) model is employed to predict diseases in banana fruit, stems, and leaves. Channel attention is incorporated into the backbone network architecture, and an improved deformable convolution replaces the normal convolution. Hyper parameters of the network model can be optimized using the improved chimp optimization (ICO) algorithm. Simulation results are obtained using the Python programming language. The proposed model attained 98.12% of accuracy in BananaLSD dataset, 99% of accuracy in banana leaf dataset, and 98.5% of accuracy in PSFD-Musa dataset. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Banana leaf disease Chimp optimization Clustering Pyramid fusion Self-guided filtering Wiener filter YOLOv8

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Design of a Magneto-Electric Monopole Antenna

Design of a Magneto-Electric Monopole Antenna

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Nakamura, Shunta Sano, Makoto Kuse, Ryuji Fukusako, Takeshi Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (纸本)9798350364774

This paper presents a compact magneto-electric (ME) monopole antenna. Based on the image theory, the lateral size of the conventional ME dipole antenna is reduced by half, by placing a vertical ground plane. The ME monopole antenna was designed at 2.5 GHz. High front-to-back ratio of 20 dB and low cross polarization are observed. © 2024 IEEE.

关键词： Monopole antennas

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