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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

computer Modeling in engineering & Sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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Enhancing students' learning achievements, self-efficacy, and motivation using mobile augmented reality

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Journal of computer Assisted Learning 2024年第4期40卷 1823-1837页

作者： Aldeeb, Fatimah H. Sallabi, Omar M. Elaish, Monther M. Hwang, Gwo-Jen Computer Network College of Computer Technology Benghazi Libya Computer Science The Libyan Academy Benghazi Libya Software Engineering Information Technology Faculty University of Benghazi Benghazi Libya Computer Science Information Technology Faculty University of Benghazi Benghazi Libya Graduate Institute of Educational Information and Measurement National Taichung University of Education Taichung City Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei City Taiwan Yuan Ze University Taoyuan City Taiwan

Background: This paper examines the use of augmented reality (AR) as a concept-association tool in schools, with the aim of enhancing primary school students' learning outcomes and engagement. Conflicting findings exist in previous studies regarding the cognitive load of AR-enriched learning, with some reporting reduced load and others indicating increased demand and poorer performance. Understanding these implications is essential for effectively leveraging AR in education. The study offers a fresh perspective on the potential of AR technology in improving educational experiences. Objectives: The primary goal of this research was to evaluate the effectiveness of an AR-assisted concept-association strategy for improving essential knowledge acquisition and skills outcomes, increasing cognitive load, and increasing self-efficacy and learning motivation among primary school students. Methods: A quasi-experimental design with a control group was employed to investigate the effectiveness of the intervention. Results and Conclusions: The results demonstrate that the implementation of the AR-assisted concept-association strategy effectively enhanced essential knowledge acquisition and skills outcomes, increased cognitive load, and increased self-efficacy and learning motivation among primary school students. These findings highlight the potential of AR technology to improve the learning experience and engagement of primary school students. The study contributes to the existing literature on the effectiveness of AR technology in education, suggesting future research directions. Ultimately, it offers a practical solution for improving the learning experience by presenting a new approach for teaching using AR technology. © 2024 John Wiley & Sons Ltd.

关键词： augmented reality biology education cognitive load learning motivation mobile learning self-efficacy

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A radius-incorporated localized multiple kernel learning algorithm for detecting depression in speech

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International Journal of Speech technology 2023年第2期26卷 371-378页

作者： Jiang, Haihua Hu, Bin Liu, Zhenyu Wang, Gang Zhang, Lan School of Computer and Information Engineering Zhejiang Gongshang University Hangzhou China Faculty of Information Technology Beijing University of Technology Beijing China School of Information Science and Engineering Lanzhou University Lanzhou China Beijing Anding Hospital of Capital Medical University Beijing China Lanzhou University Second Hospital Lanzhou China

Early intervention for depression could provide a means to reducing the disease burden, but there is a lack of objective diagnostic methods. This study investigated automatic depression classification on a speech dataset of 85 healthy controls (51 females and 34 males) and 85 depressed patients (53 females and 32 males). Considering that there are obvious differences in the performance of different types of speech features, we propose a radius-incorporated localized multiple kernel learning (trLMKL) algorithm for detecting depression in speech to make the best use of speech features. To improve the classification accuracy, we combine the information of both the margin and the radius of the MEB to learn the gating model parameters in our algorithm. Furthermore, we do not directly incorporate the radius of the MEB, but incorporate the trace of the total scattering matrix of training data. This method can avoid the time cost of calculating the radius at each iteration and decrease the computational complexity. Comprehensive experiments were carried out on our depressed speech dataset and 10 UCI datasets. Our algorithm achieved better classification performance overall than SimpleMKL and LMKL, and it was efficient at detecting depression, indicating its potential for use as a diagnostic method for depression. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Iterative methods

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Optical Ciphering Scheme for Cancellable Speaker Identification System

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computer Systems Science & engineering 2023年第4期45卷 563-578页

作者： Walid El-Shafai Marwa A.Elsayed Mohsen A.Rashwan Moawad I.Dessouky Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi EAbd El-Samie Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Communications Faculty of EngineeringCairo UniversityCairoEgypt 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

Most current security and authentication systems are based on personal *** security problem is a major issue in the field of biometric *** is due to the use in databases of the original *** biometrics will forever be lost if these databases are *** privacy is the most important goal of cancelable *** order to protect privacy,therefore,cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification *** methodology to achieve non-invertibility is the employment of non-invertible *** work suggests an encryption process for cancellable speaker identification using a hybrid encryption *** system includes the 3D Jigsaw transforms and Fractional Fourier Transform(FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding(DRPE)encryption *** evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure,authoritative,and *** encryption and cancelability effects are good and reveal good ***,it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

关键词： Cancellable biometrics jigsaw transform FrFT DRPE speaker identification

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Idecomp: imbalance-aware decomposition for class-decomposed classification using conditional GANs

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Discover Artificial Intelligence 2023年第1期3卷 31页

作者： Buczek, Patryk Zidan, Usama Gaber, Mohamed Medhat Abdelsamea, Mohammed M. School of Computing and Digital Technology Birmingham City University Birmingham100190 United Kingdom Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Department of Computer Science Faculty of Computers and Information Assiut University Assiut71515 Egypt

Medical image classification tasks frequently encounter challenges associated with class imbalance, resulting in biased model training and suboptimal classification performance. To address this issue, the combination of class decomposition and transfer learning has proven to be effective in classifying imbalanced medical imaging datasets. Nevertheless, in order to further augment the performance gains achieved through the utilisation of class decomposition within deep learning frameworks, we propose a novel model coined imbalance-Aware Decomposition for Class-Decomposed Classification (iDeComp) model. By incorporating a conditional Generative Adversarial Network (GAN) model, iDeComp is capable of generating additional samples specifically tailored to underrepresented decomposed subclasses. This paper investigates the application of iDeComp using two different medical imaging datasets. iDeComp selects underrepresented samples from the training set of the sublevel classes within each dataset, which are then employed to train separate conditional Deep Convolutional GAN (DCGAN) models and verification models. The conditional DCGAN model is responsible for generating additional samples, while the verification model critically evaluates the appropriateness of the synthesised images. Subsequently, the resulting augmented samples are utilized to train the classification model. To assess the effectiveness of iDeComp, we employ various evaluation metrics including accuracy, precision, recall, and F1 score. The results obtained from our experiments clearly indicate that iDeComp outperforms existing approaches in terms of classifying both imbalanced datasets. © The Author(s) 2023.

关键词： Generative adversarial networks

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An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

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computer Systems Science & engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman El-Sayed Walid El-Shafai Ezz El-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed work accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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Shortest Cover After Edit 35

Shortest Cover After Edit

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35th Annual Symposium on Combinatorial Pattern Matching, CPM 2024

作者： Mitani, Kazuki Mieno, Takuya Seto, Kazuhisa Horiyama, Takashi Graduate School of Information Science and Technology Hokkaido University Japan Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan Faculty of Information Science and Technology Hokkaido University Japan

ISBN: (纸本)9783959773263

This paper investigates the (quasi-)periodicity of a string when the string is edited. A string C is called a cover (as known as a quasi-period) of a string T if each character of T lies within some occurrence of C. By definition, a cover of T must be a border of T;that is, it occurs both as a prefix and as a suffix of T. In this paper, we focus on the changes in the longest border and the shortest cover of a string when the string is edited only once. We propose a data structure of size O(n) that computes the longest border and the shortest cover of the string in O(log n) time after an edit operation (either insertion, deletion, or substitution of some string) is applied to the input string T of length n, where is the length of the string being inserted or substituted. The data structure can be constructed in O(n) time given string T. © Kazuki Mitani, Takuya Mieno, Kazuhisa Seto, and Takashi Horiyama.

关键词： Data structures

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A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

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Materials Today: Proceedings 2023年 80卷 2701-2705页

作者： Qahatan Alsudani, Mustafa Abbdal Reflish, Salah H. Moorthy, Kohbalan Mundher Adnan, Myasar Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals;firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called TLBO-ELM based on the Firefly algorithm and Fast Learning Network. © 2021

关键词： Optimization

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computer Network Design for Office Building Environment 32nd

Computer Network Design for Office Building Environment

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Blažević, Damir Keser, Tomislav Mance, Matko Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Kneza Trpimira 2B Osijek Croatia

ISBN: (纸本)9783031514937

This paper presents an example of designing a computer network for a business building. When creating the project documentation, care was taken to ensure that it complies with current standards and the Rulebook on technical conditions for the electronic communication network of commercial and residential buildings. The technical description explains what structured cabling is, describes the importance of following its rules and standards, and defines the ways of marking all the equipment used in the creation of this computer network. Furthermore, an overview of all equipment with its characteristics is presented in detail. The logical scheme defines the so-called vertical network, i.e., the backbone, that is, it shows how each individual part of the active equipment inside the building is connected, how the communication cabinets are connected between the floors, and how the network switches and routers inside the communication cabinets are connected to each other. For each socket and cable, a unique label is defined, this creates a horizontal office network. This is the main part of structured cabling, where the precise definition of each individual part of the computer network provides the basis for safe and stable network operation, the possibility of easy upgrades in the future, and the elimination of possible difficulties that may arise. Application of the quality of service (QoS) on the network will be considered is the and recommendations given to avoid undesirable congestion during the flow of data by properly planning the QoS rules, such as packet marking and traffic shaping. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quality of service

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Design and Performance Analysis of a 38 GHz Microstrip Patch Antenna with Slits Loading for 5G Millimeter-Wave Communications 3

Design and Performance Analysis of a 38 GHz Microstrip Patch...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ali, Mohammed A. M. Gaid, AbdulGuddoos S. A. Saeed, Mamoon M. Saeed, Rashid A. Taiz University Faculty of Engineering & Information Technology Dept. of Communications & Computer Taiz Yemen Faculty of Engineering Dept. of Communications & Electronics Engineering Sana'a Yemen College of Computers and Information Technology Taif University Dept. of Computer Engineering Taif21944 Saudi Arabia

ISBN: (纸本)9798350305333

In this paper, we present a compact, rectangular microstrip patch antenna with small rectangular slits designed for 5G wireless applications in the 38GHz band. The antenna is built on a Roger RT duroid 5880 substrate with a relative permittivity of 2.2, a height of 0.381 mm, and a loss tangent of 0.0009. The microstrip line technique is utilized for power feeding to the radiating patch. To enhance the antenna's performance, three slits are incorporated into the front side of the radiating patch. The design, simulations, and optimizations are conducted using the High-Frequency Structure Simulator (HFSS), while computer Simulation technology (CST) is used for validating the HFSS results. The antenna has a compact overall size of 6.12 × 6 × 0.381 mm3. Moreover, the proposed antenna exhibits impressive characteristics, including a resonance frequency of 38.67 GHz, a reflection coefficient (S11) of - 47.82 dB, and an impedance bandwidth of 2.35 GHz (37.45 GHz - 39.8 GHz). In addition, the antenna's gain varies from 7.84 dBi at the lower frequency of the operational band to 7.9 dBi at the higher frequency, with a maximum gain of 8.037 dBi at the resonance frequency. Likewise, the radiation efficiency is above 99% across the operational band. The results obtained from both simulators are in a good agreement and surpass those of many previously published antennas, indicating that the proposed antenna is a promising solution for 5G wireless communication applications. © 2023 IEEE.

关键词： Millimeter waves

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