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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

computers, Materials & Continua 2022年第1期70卷 1683-1697页

作者： Mohamed Esmail Karar Marwa Ahmed Shouman Claire Chalopin Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Computer Engineering and Networks College of Computing and Information TechnologyShaqra UniversityShaqra11961Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Innovation Center for Computer Assisted Surgery(ICCAS) Universit鋞LeipzigLeipzig04103Germany

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide *** X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians *** this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary *** one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested *** tests have been successfully conducted on public dataset of US images acquired with a convex US *** study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 *** results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent ***,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

关键词： COVID-19 medical imaging machine learning applications adversarial neural networks ultrasound

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Correction to: Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network (Multimedia Tools and Applications, (2024), 10.1007/s11042-024-19254-0)

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Multimedia Tools and Applications 2024年 1-2页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

The original publication of this article contains an error in the affiliation of authors Fadwa Alrowais and Hanen Karamti. Incorrect: Department of information Systems, College of computer and information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia Correct: Department of computer Sciences, College of computer and information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia The original article has been corrected. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

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Intrusion Detection in Networks using Honey Badger Algorithm with improved Adaptive Neuro-Fuzzy Inference System

Intrusion Detection in Networks using Honey Badger Algorithm...

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2023 Al-Sadiq International Conference on Communication and information technology, AICCIT 2023

作者： Alrikabi, Ibrahim Riyadh Alazzawi, Murtadha A. Al-Khaleefa, Ahmed Al-Mosawi, Buraq T. Sh. Kareem, Hassanain Raheem Alsalamy, Ali Shahid Chamran University of Ahvaz Faculty of Engineering Department of Computer Engineering Ahvaz Iran Department of Computer Techniques Engineering Baghdad Iraq University of Misan Faculty of Education Department of Physics Maysan Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq

ISBN: (纸本)9798350341881

An Intrusion detection system IDS is a key component of the security management infrastructure. The goal of IDS is to monitor the processes prevailing in a network and to analyze them for signs of any possible deviations. Because of the large volume of data, the network gets expansion with a false alarm rate of intrusion, and detection accuracy decreased. It is one of the main problems in unknown attacks on network experiences. The basic aim was to develop accuracy also reduce the false alarm rate (FAR). We will design an Adaptive Neuro-Fuzzy based system to identify the intrusion activities within a network effectively. The Honey Badger Algorithm with an improved Adaptive Neuro-Fuzzy Inference System (HBA-IANFIS) is used to address the problems presented. The IANFIS combines an artificial neural network and a fuzzy interference system. To enhance the performance of the IANFIS model, the Honey Badger Algorithm is used to optimize the IANFIS. The intrusion detection results based on the NSL-KDD dataset were better and more efficient than those models because the detection rate was 96.68%, and the FAR result was 0.438%. © 2023 IEEE.

关键词： Fuzzy inference

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Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases 2

Machine Learning Models for Brain Signal Classification: A F...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Almomani, Ammar Sakhnini, Mahmoud Al Aldweesh, Amjad AlAhliyya Amman University Communications And Computer Engineering Department Faculty Of Engineering Amman19328 Jordan Cybersecurity Department Faculty Of Information Technology Amman11831 Jordan Zarqa University Computer Science Department Faculty Of Information Technology Zarqa13110 Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan Shaqra University College Of Computing And It Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Numerical analysis of a ITO based circularly polarized optically transparent THz antenna employing characteristic mode analysis

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Progress In Electromagnetics Research C 2021年 117卷 1-16页

作者： Rahman, Muhammad Asad Chowdhury, Md. Sarwar Uddin Hossain, Md. Azad Mobashsher, Ahmed Toaha Faculty of Electrical and Computer Engineering Chittagong University of Engineering and Technology Bangladesh School of Information Technology and Electrical Engineering The University of Queensland Australia

An optically transparent circularly polarized indium tin oxide (ITO) based antenna having operability in THz region is proposed in this paper. An E-shaped slot and an I-shaped slot are embedded into an E-shaped radiating patch modeled by ITO and conductive carbon nanotube (CNT) on a polyimide substrate to obtain circular polarization. The unequal parallel slits of the E-shaped patch with an E-shaped slot lead to introduce two orthogonal modes, and hence circular polarization is achieved. Besides, integration of an I-shaped slot also helps to create the difference in magnitude of current distribution between the two working modes to get better axial ratio. The patch of the antenna is covered with highly CNT film which improves the overall performance of the antenna. To overcome the limitations of the traditional design process, characteristic mode analysis is carried out which helps to realize and analyze circular polarization generation mechanism effectively. The proposed antenna shows a wide 3-dB axial ratio bandwidth of 9.66%. A reasonable gain of 2.61 dBic is obtained at 1.11 THz with excellent radiation performance. Wide 3-dB axial ratio bandwidth with reasonable gain makes this light weight transparent small-antenna competent for wireless and satellites applications. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Circular polarization

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Arabic Sentiment Analysis of Mobile Banking Services Reviews 10

Arabic Sentiment Analysis of Mobile Banking Services Reviews

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10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023

作者： Khabour, Safaa M. Mustafa, Dheya Althebyan, Qutaibah Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Al Ain University Dept. of Cybersecurity Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350318906

Online customer reviews have developed into a significant source of information about a business's performance. Due to shifting consumer expectations and growing internet penetration, the Middle East, especially Jordan, is seeing an increase in the usage of online banking. This propensity toward digital transition has been accelerated by the COVID-19 pandemic. Mobile banking service companies aim to effectively use customers' feedback to identify areas for improvement in customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on mobile banking reviews in Arabic, exploiting machine learning, natural language processing, and resampling methods to obtain subjective feedback, determine polarity, and identify customers' feelings in the banking domain. This work walks the reader through detailed steps of cleaning and preprocessing a manually annotated dataset of online banking reviews in Arabic, followed by features extraction and modeling, dataset splitting, and balancing training data. Finally, we examined classification algorithms including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Ensemble hard and soft voting ML methods were also evaluated. We achieved a maximum accuracy of about 94% using the LR classifier and a maximum precision of 94% using the LR and hard voting methods. Our methodology will enable banks to acquire insights on how to develop their online presence and meet the demands of customers and stakeholders. © 2023 IEEE.

关键词： Sentiment analysis

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A Deep Learning Driven Feature Based Steganalysis Approach

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Intelligent Automation & Soft Computing 2023年第8期37卷 2213-2225页

作者： Yuchen Li Baohong Ling Donghui Hu Shuli Zheng Guoan Zhang College of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China College of Information Engineering Anhui Broadcasting Movie and Television CollegeHefei230011China Department of Informatics Faculty of Natural&Mathematical SciencesKing’s College LondonLondonWC2R2LSUK

The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic *** traditional ste-ganalysis detector is trained on the stego images created by a certain type of ste-ganographic algorithm,whose detection performance drops rapidly when it is applied to detect another type of steganographic *** phenomenon is called as steganographic algorithm mismatch in *** resolve this pro-blem,we propose a deep learning driven feature-based *** advanced steganalysis neural network is used to extract steganographic features,different pairs of training images embedded with steganographic algorithms can obtain diverse features of each *** a multi-classiﬁer implemented as lightgbm is used to predict the matching *** results on four types of JPEG steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.

关键词： Image steganalysis algorithm mismatch convolutional neural network JPEG images

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Complex Pythagorean Fuzzy Decision-Based Approach for Developing English Translation of the Scripture-Based Multiclustering Algorithms

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International Journal of information technology and Decision Making 2025年第2期24卷 473-533页

作者： Ahmed, Mohammed A. Zaidan, A.A. Qahtan, Sarah Alsattar, Hassan A. Zaidan, B.B. Mourad, Nahia Baharin, Hanif Kou, Gang Yatim, Khaironi Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi43600 Malaysia Network Engineering Department College of Engineering Al-Iraqia University Al Adhmia-Haiba Khaton Baghdad10053 Iraq SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia Information Technology Unit College of Health and Medical Technology-Baghdad Middle Technical University Baghdad Iraq Research Center Mazaya University College Nasiriyah Iraq MEU Research Unit Middle East University Amman Jordan Faculty of Engineering and IT British University in Dubai Dubai United Arab Emirates School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Computer and Information Sciences Faculty of Sciences and Information Technology Universiti Teknologi PETRONAS Seri Iskandar32610 Malaysia

The English Translation of the Quran Tafsir (ETQT) is essential to understanding and interpreting Allah's words. Clustering is a common text mining technique for eliciting meaningful knowledge from a text collection. It is commonly used when the selected datasets lack typical ground truths. To the best of our knowledge, no study has evaluated and benchmarked ETQTs to select the most comprehensive and appropriate one. The process of evaluating and benchmarking ETQTs falls under the multicriteria decision-making (MCDM) problem because of different issues, namely, multiple internal clustering validation criteria, data variation, and trade-offs between different criteria. The fuzzy decision by opinion score method (FDOSM) is one of the most recommended MCDM ranking methods in the literature to address the said issues. FDOSM has been extended under different fuzzy set (FS) environments to address issues of uncertainty and vagueness caused by expert feedback subjectivity. Although prior versions of FDOSM improved the uncertainty and vagueness issues, they remain open issues. Therefore, this paper extended FDOSM into the complex Pythagorean fuzzy decision by opinion score method (CPFDOSM) to evaluate and benchmark ETQTs. The proposed method consists of two main phases. The first phase formulates decision-matrix-based cluster algorithms and internal cluster validation criteria. The second phase (CPFDOSM development) prioritizes ETQTs and selects the optimum one. Data generation is performed on five different cluster algorithms and six internal cluster validation criteria using 16 ETQTs based on three decision-makers (DMs). Results show the following: (1) 6.25% of the individual decision-making results are identical among the three DMs, whereas 93.75% (n = 15/16) are different when I = 0.5 and I = 2. When I = 0.5. In addition, T7 has consistent ranks (Rank=16) across all DMs, whereas T14 has consistent ranks (Rank=1) across all DMs when I = 1. (2) The results of the group de

关键词： Clustering algorithms

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A secured GA-WPC framework for scheduling the independent tasks in cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 13003-13015页

作者： Dubey, Kalka Sharma, S.C. Kumar, Mohit Kumar, Prashant Nasr, Aida A. Cloud Computing and Wireless Sensor Lab Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais Uttar Pradesh Amethi229304 India Department of Information Technology Dr BR Ambedkar National Institute of Technology Punjab Jalandhar144011 India Information Technology Department Faculty of Computers and Informatics Tanta University Tanta Egypt

Cloud computing proved itself a powerful and popular platform for providing computing services. It offers on-demand services to users with highly efficient resources over the internet. With the exponential increase in the number of internet users and due to the high acceptance rate to use the cloud services in everything, the demand for cloud platforms has also increased significantly. This leads to heavy workloads on the servers, which is a difficult task to manage due to the heterogeneity and uncertainty of the resources. However, several traditional, heuristic, and evolutionary-based approaches have been proposed to solve the problem of scheduling. Existing state-of-art techniques and frameworks have solved the problem up to a limited level. Still, these solutions have not given any effective and concrete solution for the overall resource scheduling problem. Therefore, this paper tries to overcome the bottleneck of existing solutions by presenting a new framework named Genetic Algorithm Water Pressure Change (GAWPC) to solve task scheduling problems efficiently. The GAWPC framework exploited the feature of the Genetic Algorithm (GA) and Water Pressure Change optimization (WPCO) techniques to form a new hybrid mechanism. In the proposed framework, GA has been deployed to find the optimal solution for a particular population. WPCO has been used to optimize fitness function for a fast convergence rate. The computer-based simulation has demonstrated promising benefits like improved computation cost, make-span time, and load balancing when compared with the most recent existing baseline techniques using CloudSim. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Genetic algorithms

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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