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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Elalaouy, Ouafae Aghoutane, Bilal El Ghzaoui, Mohammed Das, Sudipta Foshi, Jaouad El Faylali, Hanan University of Moulay Ismail Faculty of Sciences and Techniques Errachidia Errachidia Morocco Faculty of Sciences IbnTofail University Informatics Systems and Optimization Laboratory Department of Computer Science Kenitra Morocco Sidi Mohamed Ben Abdellah University Faculty of Sciences Dhar El Mahraz Fes Morocco Imps College of Engineering and Technology Department of Electronics and Communication Engineering W.B Malda India

ISBN: (纸本)9798350365306

In the contemporary era, harnessing millimeter-wave frequencies emerges as a compelling strategy to fulfill the swift data transfer requirements for 5G communications. Consequently, this study involves designing a metamaterial-based multiple-input multiple-output (MIMO) antenna with a focus on achieving high isolation and gain within the millimeter-wave spectrum. The suggested MIMO is printed on a Rogers RT5800 measuring 18 × 38 × 0.8 mm3. The single antenna element demonstrates a broad frequency coverage from 27.2 to 28.72GHz. To elevate the antenna's effectiveness, a two-port MIMO configuration is implemented by introducing a second antenna element parallel to benchmark antenna. However, the isolation between the two elements is deemed satisfactory. To address this, a 4 × 1 metamaterial unit cell is introduced, significantly enhancing isolation from 31 dB to 39 dB at 28 GHz. Furthermore, it improves the gain by 0.5 dBi at 28 GHz, elevating it from 8.5 to 9 dBi. The collective features of the suggested two ports MIMO antenna, encompassing its compactness and simplicity, position it as an excellent option for upcoming portable and shrink devices in Internet of Things uses. © 2024 IEEE.

关键词： Microwave antennas

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Deep CNN Model for Multimodal Medical Image Denoising

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computers, Materials & Continua 2022年第11期73卷 3795-3814页

作者： Walid El-Shafai Amira A.Mahmoud Anas M.Ali El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron Emission Tomography(PET).These techniques are organized into two main classes:the Multiple Image(MI)and the Single Image(SI)*** the MI techniques,images usually obtained for the same area scanned from different points of view are used.A single image is used in the entire procedure in the SI *** denoising techniques can be carried out both in a transform or spatial *** paper is concerned with single-image noise reduction techniques because we deal with single medical *** most well-known spatial domain noise reduction techniques,including Gaussian filter,Kuan filter,Frost filter,Lee filter,Gabor filter,Median filter,Homomorphic filter,Speckle reducing anisotropic diffusion(SRAD),Nonlocal-Means(NL-Means),and Total Variation(TV),are ***,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are ***,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image *** model utilizes the Batch Normalization(BN)and the ReLU as a basic *** a result,it attained a considerable improvement over the traditional *** previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),and system complexity to determine the optimum denoising algorithm to be applied *** on the quality metrics,it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditionaldenoising techniques.

关键词： Image enhancement medical imaging speckle noise Gaussian noise denoising filters CNN denoising

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Proposed Privacy Preservation Technique for Color Medical Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 719-732页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Noha A.El-Hag Ashraf A.M.Khalaf Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Nowadays,the security of images or information is very *** paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image ***,the secret medical image is encrypted using Advanced Encryption Standard(AES)***,the secret report of the patient is embedded into the encrypted secret medical image with the Least Signiﬁcant Bit(LSB)watermarking *** that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)***,the stego-image obtained is split into 16 ***,it is sent to the *** adopt this strategy to send the secret medical image and report over a network *** proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefﬁcient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to conﬁrm the matching between the secret medical image before and after *** results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

关键词： LSB steganography AES algorithm KMCG algorithm

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Sentiment Analysis of Local Tourism in Thailand from YouTube Comments Using BiLSTM 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Khruahong, Sanya Surinta, Olarik Lam, Sinh Cong Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok Thailand Faculty of Informatics Mahasarakham University Mahasarakham Thailand Faculty of Electronics and Telecommunication VNU - University of Engineering and Technology Hanoi Viet Nam

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

Currently, social networks, where people can express their opinion through content and comments, are fast developing and affect various areas of daily life;Particularly, some research on YouTube travel channels found that almost tourists and audiences leave comments about their attitudes to that place. Thus, mining the emotional recognition of comments through artificial intelligence can bring knowledge about the tourists’ general view. This article analyzes the relationship(s) between social media use and its effect on community-based tourism in Thailand using the Social Media Sensing framework (S-Sense) as sentiment analysis and the Bidirectional Long Short-Term Memory (BiLSTM) methods to analyze the text comments. This research collected 51,280 comments on 114 Youtube Videos, which are tourist attractions in various provinces in Thailand. The approach categorizes attractions based on sentiment analysis of 60% or more, including restaurants, markets, historical sites, temples, or natural attractions. The results show that 67.51% of the 19,391 clean-processed comments were satisfied with those attraction places. Therefore S-Sense and BiLSTM models can be sufficient to analyze the attitude of comments about attraction places with from 43 to remain 33 keywords of 1,603 comments. Furthermore, the offered sentiment analysis method has higher precision, recall, and F1 scores. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

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computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Facial Expression Classification for Online Interview Using Convolutional Neural Network

Facial Expression Classification for Online Interview Using ...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Trairat Sabaichai Datchakorn Tancharoen Sitapa Watcharapinchai Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Recently, online interview plays an important role for job recruitment. Online job interviews are convenient and time-saving for interviewees. Facial expressions in the job interview provide the impression or interviewers. In this paper, we aims to classify the facial expressions from the open dataset into seven basic emotions such as Angry, Disgust, Fear, Happy, Neutral, Sad and Surprise. Online interview concentrates on two facial expressions including Happy and Surprise. The CNN model is applied to train the FER2013 datasets and improve the accuracy for the specific expressions. The experimental results demonstrate the performance of our approach by offering a robust and reliable method for facial expression classification using Convolutional Neural Network (CNN). This approach can classify the specific facial expressions with precision 83 percent and recall 84 percent for Happy and satisfy Surprise with precision 75 percent and recall 81 percent. This approach can maintain the average accuracy at 64.1 percent.

关键词： Analytical models Accuracy Sociology Feature extraction Data models Convolutional neural networks Interviews

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Hybrid Convtranslstm for Spatio-Temporal Classification: Identifying Early Parkinson's Disease from Gait Patterns

SSRN

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SSRN 2025年

作者： Mahali, Muhammad Izzuddin Avian, Cries Putro, Nur Achmad Sulistyo Prakosa, Setya Widyawan Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Department Electrical Engineering Faculty of Engineering Universitas Brawijaya Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that affects motor function and impacts millions worldwide. Early diagnosis and precise staging of PD are critical for effective management and timely intervention. Traditional gait analysis focuses on lower limb movement to differentiate between moderate and advanced PD stages;however, integrating upper and lower limb dynamics offers a more comprehensive approach to distinguishing early-stage PD from *** study introduces a novel spatio-temporal model that integrates CNN, Transformer, and BiLSTM layers to classify PD based on the Hoehn and Yahr (H&Y) score. The architecture captures both spatial and temporal dependencies in gait patterns, with a CNN serving as the primary spatial feature extractor, utilizing a temporal-focused kernel, and augmented by a Transformer to enhance temporal feature learning. Additionally, a BiLSTM module is incorporated to effectively capture complex spatio-temporal gait dynamics across all modules. For data augmentation, sliding windows of 500, 800, and 1200 samples were employed, and the model's performance was rigorously assessed through 3-fold and 5-fold cross-subject *** included accuracy, F1-score, recall, precision, and a voting-based confidence score, while t-SNE visualization provided insights into spatio-temporal feature differentiation. The model demonstrated high performance, achieving optimal accuracy of 0.90 and an average confidence score of 0.97 with a 1200-sample window in 5-fold validation. These findings highlight the potential of a spatio-temporal approach incorporating upper and lower limb characteristics in gait analysis as a robust method for PD multi-class classification, supporting early and effective PD diagnosis from raw IMU signals. © 2025, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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Exploiting hybrid features extracted from color spaces of dermoscopic images 28

Exploiting hybrid features extracted from color spaces of de...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Moldovanu, Simona Miron, Mihaela Culea-Florescu, Anisia Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology 47 Domneasca Street 800008 Galaţi800008 Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications 47 Domneasca Street 800008 Galaţi800008 Romania

ISBN: (纸本)9798350339918

In the context of detecting dangerous illnesses of the patient affected by skin lesions, this study is focused on the problem of improving the classification of melanoma vs. normal nevus considering neural network and new proposed features based on high and low pixels intensity, and densities of the pattern detected with hit-or-miss (HoM) morphological transform. A large set of features from color spaces such as RGB (red, green, and blue), HSV (hue, saturation, and value) and L∗a∗b∗ (lightness variable L∗, chromaticity indices a∗ and b∗) were initially extracted and grouped in two parts: the first group includes first and second order features, and the second group contains hybrid features (a mix of first order, second order features and proposed features). The dimensionality of the features from these two groups is considerably reduced with principal component analysis (PCA) statistical method and fed three different types of neural network (NN): Radial Basis Function Neural Network (RBFNN), Cascade-Forward Neural Network (CFNN) and Pattern Recognition Neural Network (PRNN). From this corroboration of hybrid features, color space and NN, the L∗a∗b∗ color space with CFNN provided accuracy (ACC) of 0.897, F1-score of 0.91 and area under the ROC curve (AUC) of 0.896, respectively. © 2023 IEEE.

关键词： Color

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Influence of agglomerations on magnetic properties of polymer matrices filled with magnetic nanoparticles

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Materials Today: Proceedings 2022年 67卷 792-796页

作者： Blachowicz, Tomasz Grzybowski, Jacek Ehrmann, Andrea Institute of Physics – Centre for Science and Education Silesian University of Technology Gliwice 44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice 44-100 Poland Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences Bielefeld 33619 Germany

Magnetic nanoparticles can be embedded in electrospun nanofibers and other polymeric matrices to prepare magnetic composites with defined magnetic and mechanical properties. Metal-oxide nanoparticles, such as magnetite or nickel-ferrite, are of special interest since they do not need a coating to avoid oxidation. Like other nanoparticles, these metal-oxide nanoparticles tend to form agglomerations, in this way modifying the magnetic properties of the composites. After studying this effect for the magnetic elements Co, Fe, Ni as well as permalloy (Py) in a previous study, defining a new method to quantify the nanoparticle distribution in a polymer, here we concentrate on the influence of agglomerations on the magnetic properties of metal-oxide nanoparticles with different diameters in non-magnetic matrices. © 2022

关键词： Magnetite

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