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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Damaševičius, Robertas Taniar, David Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania Monash University Faculty of Information Technolog MelbourneVIC3800 Australia

ISBN: (纸本)9798350387490

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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Cyber Security Posterior Distribution 13

Cyber Security Posterior Distribution

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13th IEEE International Conference on Communication systems and Network Technologies, CSNT 2024

作者： Andrews, Leo John Baptist Venkatachary, Sampath Kumar Sarathkumar, D. Alagappan, Annamalai Fathima, M. Raj, Raymon Antony Rajeshkanna, R. Botho University Faculty of Engineering and Technology Department of Information Technology Gaborone Botswana Kongu Engineering College Department of Electrical and Electronics Engineering Erode district Tamilnadu 638 060 India Grant Thornton Gaborone Botswana Botho University Faculty of Engineering and Technology Department of Network and Infrastructure Management Gaborone Botswana Thamirabarani Engineering College Department of Eee Tamil Nadu Tirunelveli627 358 India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798350305463

Currently, the vast majority of international interactions and activities related to trade, government, culture, social interaction, and economics take place online. These interactions involve people, non-governmental organizations, governments, and government agencies at all levels. Recently, a sizeable number of commercial firms and government organizations all over the world have been presented with the challenge offered by cyberattacks as well as the danger represented by wireless communication technologies. A very little amount of time has passed since this had place. We were able to develop a model for the risk of cyber security by making use of Bayesian Networks. This model has the capability of locating the point of cyber security that is weak and merging two different cyber analysis models. Among these is the activityquality model, which shows how individuals and/or groups adhere to the legal requirements for cyber security. Evidence describing the critical component of cyber security for a research reactor's RPS may be produced via the cyber security risk model. An further model that shows the probability of a cyberattack against RPS architecture is the cyber security risk model. © 2024 IEEE.

关键词： Bayesian networks

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Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

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computers, Materials & Continua 2023年第3期74卷 5447-5465页

作者： Badriyya B.Al-onazi Saud S.Alotaib Saeed Masoud Alshahrani Najm Alotaibi Mrim M.Alnfiai Ahmed S.Salama Manar Ahmed Hamza Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The text classification process has been extensively investigated in various languages,especially *** classification models are vital in several Natural Language Processing(NLP)*** Arabic language has a lot of *** instance,it is the fourth mostly-used language on the internet and the sixth official language of ***,there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic *** general,researchers face two challenges in the Arabic text classification process:low accuracy and high dimensionality of the *** this study,an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning(AATC-HTHDL)model is *** major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic *** first step in the proposed model is to pre-process the input data to transform it into a useful *** Term Frequency-Inverse Document Frequency(TF-IDF)model is applied to extract the feature ***,the Convolutional Neural Network with Recurrent Neural Network(CRNN)model is utilized to classify the Arabic *** the final stage,the Crow Search Algorithm(CSA)is applied to fine-tune the CRNN model’s hyperparameters,showing the work’s *** proposed AATCHTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.

关键词： Hybrid deep learning natural language processing arabic language text classification parameter tuning

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Efficiency Effect of Obstacle Margin on Line-of-Sight in Wireless Networks

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computers, Materials & Continua 2022年第7期72卷 227-242页

作者： Murad A.A.Almekhlafi Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a University61101Yemen Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning,allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be *** the increasing literature on the line of sight with different scenarios,no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their ***,this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances the accuracy of these values’*** have designed MATLAB simulation and programming programs by Visual *** for a semi-realistic communication *** includes all the basic parameters of this system,taking into account the environment’s diversity and the characteristics of the obstacle between the transmitting station and the receiving *** we verified the correctness of the system’s ***,we begin by analyzing and studying multiple and branching cases to achieve the *** get several values from the results,which are finite values,which are a useful reference for engineers and designers of wireless networks.

关键词： Line of sight obstacle margin wireless networks communication link radio wave propagation

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Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification 4

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

ISBN: (纸本)9798350387490

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital. © 2024 IEEE.

关键词： Ophthalmology

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Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

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computers, Materials & Continua 2023年第3期74卷 5467-5482页

作者： Mohammed Maray Badriyya B.Al-onazi Jaber S.Alzahrani Saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character ***, many features usually create difficulties due to high dimensionality *** this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this *** Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

关键词： Arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning

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A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

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computers, Materials & Continua 2022年第7期72卷 365-380页

作者： Mahmoud Ragab Ali Altalbe Abdullah Saad Al-Malaise ALGhamdi SAbdel-khalek Rashid A.Saeed Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Mathematics Faculty of ScienceTaif UniversityTaifSaudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

The smart city comprises various interlinked elements which communicate data and offers urban life to *** Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and *** improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with *** addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV ***,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)*** design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

关键词： Drones smart city swarm intelligence route selection internet of drones networking

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Development of a visual programming tool called SmrAI for function testing to mitigate the shortage of programming personnel in the manufacturing

Computer Software

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computer Software 2022年第4期39卷 61-74页

作者： Sugiyama, Ko-Ichiro Okuda, Satsuki Tanabe, Tsubasa Takao, Kouji Higashi, Hirohito Tanaka, Kazuaki Dept. of Information Engineering National Institute of Technology Matsue College Japan NIPPO Co. Ltd. Japan Ruby Programming Shounendan Japan Shimane IT Open-innovation Center Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

We have developed a visual programming tool called SmrAI for function testing, and investigated whether wrapping existing source code with Scratch Blocks is an effective means of mitigating the shortage of programming personnel in the manufacturing. SmrAI is based on Smalruby, which can convert Scratch Blocks into Ruby code. In addition, SmrAI introduces two new sets of Blocks that wrap existing Ruby libraries for function testing. Since existing tools and libraries were utilized, the cost of developing SmrAI was low, and there were no changes in the function testing procedure. We conducted a verification test using staff members who were familiar with the function testing process but had little or no programming experience, and confirmed that they were able to program using SmrAI and that they had confidence in its business applications. These results are expected to contribute to the diversification and increase of programming personnel responsible for function test programming. © 2022 Japan Society for Software Science and technology. All rights reserved.

关键词： Application programs

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Enhancing MANETs Security Against Black Hole Attacks through Time-Based Node Analysis in AOMDV Protocol

Enhancing MANETs Security Against Black Hole Attacks through...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah A. Naji Mogeeb A. Saeed Ammar H. Almagashi Alaa M. Ahmed Hashem S. Al-Atefi Mohammed N. Khaled Olayan M. Rashed Computer Network & Distributed Systems Department Al-Saeed Faculty Of Engineering &IT Taiz University Computer Networks & Distributed Systems Al-Saeed Faculty Of Engineering &IT Taiz University Computer Center & Information Technology Taiz University

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Mobile ad-hoc networks (MANETs) rely solely on direct node communication and function without a fixed infrastructure. Due to their decentralized design, they are susceptible to numerous security risks, with black hole attacks posing significant threats to routing integrity and confidentiality. This paper proposes a technique to enhance the Ad hoc On-Demand Multi-path Distance Vector (AOMDV) routing protocol's security against black hole attacks. The method involves a time-based reply message analysis to identify and mitigate malicious nodes. This algorithm improves the protocol's ability to detect and prevent black hole attacks by closely analyzing response time differences between malicious and normal nodes. The results of the simulation show that the suggested solution greatly enhances network performance by lowering the Routing Overhead Ratio from 1388.04% to 0.73% and the Packet Loss Rate from 74% to 11% while keeping a high Packet Delivery Ratio—up to 93% in different scenarios. These enhancements sustain strong data transmission in dynamic MANET environments, supporting network integrity and reliability.

关键词： Protocols Packet loss Routing Ad hoc networks Vectors Security Time factors

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