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Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

Computers, Materials & Continua 2022年第9期72卷 6091-6106页

作者： Medhat A.Tawfeek Saad alanazi A.A.Abd El-Aziz Department of Computer Science College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA Department of Computer Science Faculty of Computers and InformationMenoufia UniversityEgypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA) Department of Information Systems and Technology Faculty of Graduates Studies and ResearchCairo UniversityEgypt

The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very *** is a trade-off in the objectives in the existing techniques of MultipleSequence Alignment (MSA). The techniques that concern with speed ignoreaccuracy, whereas techniques that concern with accuracy ignore speed. Theterm alignment means to get the similarity in different sequences with highaccuracy. The more growing number of sequences leads to a very complexand complicated problem. Because of the emergence;rapid development;anddependence on gene sequencing, sequence alignment has become importantin every biological relationship analysis process. Calculating the numberof similar amino acids is the primary method for proving that there is arelationship between two sequences. The time is a main issue in any alignmenttechnique. In this paper, a more effective MSA method for handling themassive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method dependson Artificial Fish Swarm (AFS) algorithm that can break down the mostchallenges of MSA problems. The AFS is exploited to obtain high accuracyin adequate time. ASF has been increasing popularly in various applicationssuch as artificial intelligence, computer vision, machine learning, and dataintensive application. It basically mimics the behavior of fish trying to getthe food in nature. The proposed mechanisms of AFS that is like preying,swarming, following, moving, and leaping help in increasing the accuracy andconcerning the speed by decreasing execution time. The sense organs that aidthe artificial fishes to collect information and vision from the environmenthelp in concerning the accuracy. These features of the proposed AFS make thealignment operation more efficient and are suitable especially for large-scaledata. The implementation and experimental results put the proposed AFS as afirst choice in th

关键词： Multiple sequence alignment swarm intelligence artificial fish swarm protein sequences

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Applying and Comparison of Chaotic-Based Permutation Algorithms for Audio Encryption

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Computers, Materials & Continua 2021年第6期67卷 3161-3176页

作者： Osama M.Abu Zaid Medhat A.Tawfeek Saad alanazi Department of Computer Science Faculty of Computers and InformationKafrelsheikh UniversityEgypt Department of Computer Science College of Computer and Information SciencesJouf UniversityKSA Department of Computer Science Faculty of Computers and InformationMenoua UniversityEgypt

This research presents,and claries the application of two permutation algorithms,based on chaotic map systems,and applied to a le of speech *** are the Arnold cat map-based permutation algorithm,and the Baker’s chaotic map-based permutation *** algorithms are implemented on the same speech signal ***,both the premier and the encrypted le histograms are documented and *** speech signal amplitude values with time signals of the original le are recorded and plotted against the encrypted and decrypted ***,the original le is plotted against the encrypted le,using the spectrogram frequencies of speech signals with the signal *** permutation algorithms are used to shufe the positions of the speech les signals’values without any changes,to produce an encrypted speech le.A comparative analysis is introduced by using some of sundry statistical and experimental analyses for the procedures of encryption and decryption,e.g.,the time of both procedures,the encrypted audio signals histogram,the correlation coefcient between specimens in the premier and encrypted signals,a test of the Spectral Distortion(SD),and the Log-Likelihood Ratio(LLR)*** outcomes of the different experimental and comparative studies demonstrate that the two permutation algorithms(Baker and Arnold)are sufcient for providing an efcient and reliable voice signal encryption ***,the Arnold’s algorithm gives better results in most cases as compared to the results of Baker’s algorithm.

关键词： Arnold’s cat map chaotic maps permutation algorithms speech encryption Baker’s chaotic map

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Ergodic Capacity Evaluation of Multi-Hop Decode-and-Forward MIMO-OFDM Relaying Network

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Computers, Materials & Continua 2021年第9期68卷 3133-3145页

作者： Latif Jan Mohammad Haseeb Zafar Abdul Waheed Mahdi Zareei Faisal alanazi Department of Electrical Engineering University of Engineering&TechnologyPeshawar25120Pakistan Department of Information Technology Hazara UniversityMansehra21120Pakistan Tecnologico de Monterrey School of Engineering and SciencesZapopan45201Mexico Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia

Spatial diversity plays a significant role in wireless communication systems,including the Fourth Generation(4G)and Fifth Generation(5G)systems,and it is expected to be a fundamental part of the future wireless communication systems as *** Multiple-Input Multiple-Output(MIMO)technology,which is included in the IEEE 802.16j standard,still holds the most crucial position in the 4G spectrum as it promises to improve the throughput,capacity,spectral,and energy efficiency of wireless communication systems in the *** makes MIMO a viable technology for delay constrained medical and health care *** paper presents an approximate closed-form expression of the ergodic capacity for the Decode-and-Forward(DF)protocol MIMO-Orthogonal Frequency Division Multiplexing(OFDM)relaying *** MIMO-OFDM is highly valuable for modern high-speed wireless communication systems,especially in the medical sciences,its performance degrades in multi-hop relay ***,in this paper,an approximate closed-form expression is derived for an end-to-end ergodic capacity of multi-hop DF MIMO-OFDM wireless communication system has been ***-Carlo simulations are conducted to verify the performance of the presented analysis regarding the capacity(bits/s/Hz)for different SNR-dB values for single,2×2,4×4,and multi-hop DF MIMOOFDM *** presented results provide useful insights for the research on the end-to-end ergodic capacity evaluation.

关键词： Multiple-input multiple-output system decode-and-forward protocol orthogonal frequency division multiplexing ergodic capacity

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Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

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Computers, Materials & Continua 2023年第9期76卷 3483-3498页

作者： Asma A.Alhashmi Abdulbasit A.Darem Sultan M.alanazi Abdullah M.Alashjaee Bader Aldughayfiq Fuad A.Ghaleb Shouki A.Ebad Majed A.alanazi Department of Computer Science Northern Border UniversityArar9280Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Information Systems College of Computer Science and InformationJouf UniversitySakakaAljoufSaudi Arabia School of Computing University Teknologi Malaysia81310 UTM Johor BahruJohor81310Malaysia Department of Computer and Electronic Engineering Sana’a Community CollegeSana’a5695Yemen

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

关键词： API-based hybrid malware detection model static and dynamic analysis malware detection

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Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

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Computers, Materials & Continua 2022年第2期70卷 4123-4145页

作者： Shahab Ul Islam Saifullah Jan Abdul Waheed Gulzar Mehmood Mahdi Zareei Faisal alanazi Department of Computer Science IQRA National UniversityPeshawar25124Pakistan Department of Computer Science City University of Science&Information TechnologyPeshawar25124Pakistan Department of Information Technology Hazara UniversityMansehra21120Pakistan School of Electrical and Computer Engineering Seoul National UniversitySeoul08826Korea Department of Computer Science and Information Technology University of Malakand23021Pakistan Tecnologico de Monterrey School of Engineering and SciencesZapopan45201Mexico Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management ***,a change in infrastructure has a direct impact on other land-cover and climatic *** study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and *** this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were ***,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were ***,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were *** Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under *** composition of red,green,and near infra-red bands was used for supervised *** images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to *** has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

关键词： Remote sensing temperature extraction urbanization satellite image classification artificial neural network support vector machine LU/LC land surface temperature

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Exploration of preschool hearing screening at primary healthcare centers in Riyadh,Saudi Arabia:A cross-sectional study

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Journal of Otology 2024年第4期19卷 214-219页

作者： Ahmad A.alanazi Rayan A.Almutairi khalid S.Alsuhaibani Yazzan Alruwaily Hellal A.Alqudiey Mohammed A.Alabbas Shuaa R.alanazi Department of Audiology and Speech Pathology College of Applied Medical SciencesKing Saud bin Abdulaziz University for Health SciencesRiyadhSaudi Arabia king Abdullah International Medical Research Center RiyadhSaudi Arabia College of Medicine King Saud bin Abdulaziz University for Health SciencesRiyadhSaudi Arabia AlRawda Primary Healthcare Center-2 Riyadh Second Health ClusterMinistry of HealthRiyadhSaudi Arabia

The hearing status of children should be examined throughout early childhood,even if they have passed the newborn hearing loss because hearing loss can occur at any time and may affect their ability to *** hearing screening(PHS)is vital to continue screening throughout early *** current practice of PHS in the primary healthcare centers(PHCs)in Saudi Arabia is *** purpose of this study was to investigate PHS in the *** cross-sectional descriptive study used an in-person-administered questionnaire to collect data.A total of 106 out of 120 participants(male=61;female=45)representing the PHCs in Riyadh were *** of the participants were aged 31–40 years and held a bachelor’s degree as the highest academic qualification with limited years of *** was mostly performed through subjective measures by asking the parents(71.7%)and the child(65.1%).The audiometric evaluation was conducted for preschool children in only half of the *** practitioners and nurses usually perform *** gaps in the practice of PHS were *** lack of training and appropriate instruments and the need for audiological services were the main *** robust and objective protocols for PHS into the educational system is a valuable strategy for identifying hearing loss early and reducing its impact through the establishment of effective intervention plans.

关键词： Audiology Hearing loss Preschool children Primary healthcare center Screening Saudi Arabia

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Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

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Computers, Materials & Continua 2021年第7期68卷 1341-1357页

作者： Shabib Aftab Saad alanazi Munir Ahmad Muhammad Adnan khan Areej Fatima Nouh Sabri Elmitwally School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Riphah School of Computing&Innovation Riphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of Computers and Articial IntelligenceCairo University12613Egypt

Diabetes mellitus,generally known as diabetes,is one of the most common diseases *** is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin *** is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/*** is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood *** and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of *** primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing ***,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of ***,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare ***,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

关键词： Machine learning fusion articial neural network decision trees naïve Bayes diabetes prediction

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Prevalence and associated factors of clubfoot in the eastern province of Saudi Arabia: A hospital-based study

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World Journal of Orthopedics 2024年第7期15卷 635-641页

作者： Ammar k Alomran Bandar A Alzahrani Bader S alanazi Mohammed A Alharbi Loay M Bojubara Eman M Alyaseen Department of Orthopedic College of MedicineImam Abdulrahman bin Faisal UniversityDammam 34212Saudi Arabia College of Medicine Imam Abdulrahman bin Faisal UniversityDammam 34212Saudi Arabia College of Medicine Arabian Gulf UniversityManama 329Bahrain

BACkGROUND Clubfoot,or congenital talipes equinovarus,is a widely recognized cause of disability and congenital deformity worldwide,which significantly impacts the quality of *** management of clubfoot requires long-term,multidiscip-linary *** is important to understand how common this condition is in order to assess its impact on the ***,few studies have investigated the prevalence of clubfoot in Saudi *** To determine the prevalence of clubfoot in Saudi Arabia via the patient population at king Fahad University Hospital(kFUH).METHODS This was a retrospective study conducted at one of the largest hospitals in the country and located in one of the most densely populated of the administrative *** Of the 7792 births between 2015 to 2023 that were included in the analysis,42 patients were diagnosed with clubfoot,resulting in a prevalence of 5.3 per 1000 live births at *** The observed prevalence of clubfoot was significantly higher than both global and local estimates,indicating a substantial burden in the study population.

关键词： Clubfoot Talipes equinovarus Congenital talipes equinovarus Prevalence Saudi Arabia

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Human-Computer Interaction Using Deep Fusion Model-Based Facial Expression Recognition System

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Computer Modeling in Engineering & Sciences 2023年第5期135卷 1165-1185页

作者： Saiyed Umer Ranjeet kumar Rout Shailendra Tiwari Ahmad Ali AlZubi Jazem Mutared alanazi kulakov Yurii Department of Computer Science&Engineering Aliah UniversityKolkata700156India Department of Computer Science and Engineering National Institute of TechnologySrinagarJammu and Kashmir190006India Department of Computer Science&Engineering Thapar UniversityPatiala147004India Computer Science Department King Saud UniversityRiyadh11451Saudi Arabia Department of Computer Engineering National Technical University of UkraineIgor Sikorsky Kyiv Polytechnic InstituteKyiv03056Ukraine

A deep fusion model is proposed for facial expression-based human-computer Interaction ***,image preprocessing,i.e.,the extraction of the facial region from the input image is ***,the extraction of more discriminative and distinctive deep learning features is achieved using extracted facial *** prevent overfitting,in-depth features of facial images are extracted and assigned to the proposed convolutional neural network(CNN)*** CNN models are then ***,the performance of each CNN model is fused to obtain the final decision for the seven basic classes of facial expressions,i.e.,fear,disgust,anger,surprise,sadness,happiness,*** experimental purposes,three benchmark datasets,i.e.,SFEW,Ck+,and kDEF are *** performance of the proposed systemis compared with some state-of-the-artmethods concerning each *** performance analysis reveals that the proposed system outperforms the competitive methods in terms of various performance ***,the proposed deep fusion model is being utilized to control a music player using the recognized emotions of the users.

关键词： Deep learning facial expression emotions recognition CNN

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Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network

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Computers, Materials & Continua 2021年第5期67卷 2585-2600页

作者： Gulzar Ahmad Saad alanazi Madallah Alruwaili Fahad Ahmad Muhammad Adnan khan Sagheer Abbas Nadia Tabassum School of Computer Science NCBA&ELahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Department of Basic Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan

Security is a significant issue for everyone due to new and creative ways to commit *** Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect *** monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one ***,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV *** weapons are identified,the cameras sound an *** the proposed IADC system,CNN was used to detect firearms and *** CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data ***,this system generates an early warning through detection of ammunition before conditions become *** the faster and earlier the prediction,the lower the response time,loses and potential *** proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3.

关键词： CCTV CNN IADC deep learning intelligent ammunition detection DnCNN

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