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An Optimized Approach to Deep Learning for Botnet Detection and Classifica.ion for Cybersecurity in internet of Things Environment

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Computers, Materials & Continua 2024年第8期80卷 2331-2349页

作者： Abdulrahman alzahrani Department of Computer Science and Engineering College of Computer Science and EngineeringUniversity of Hafr Al BatinP.O.Box 1803Hafr Al Batin39524Saudi Arabia

The recent development of the internet of Things(ioTs)resulted in the growth of ioT-based DDoS *** detection of Botnet in ioT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected *** detection models evaluate transmission patterns,network traffic,and device beha.iour to detect devia.ions from usual *** learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device *** addition,intrusion detection systems(iDSs)and signature-based techniques are applied to recognize known malware signatures related to *** ML and deep learning(DL)techniques have been developed to detect botnet attacks in ioT *** overcome security issues in an ioT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classifica.ion(GTODL-BADC)*** GTODL-BADC technique follows feature selection(FS)with optimal DL-based classifica.ion for accomplishing security in an ioT *** data preprocessing,the min-max data norma.iza.ion approach is prima.ily *** GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature ***,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet ***,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM *** experimental va.ida.ion of the GTODL-BADC technique can be tested on a benchmark *** simula.ion results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.

关键词： Botnet detection internet of things gorilla troops optimizer hyperparameter tuning intrusion detection system

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Evalua.ing the Efficacy of Latent Va.iables in Mitiga.ing Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1635-1654页

作者： Shahad alzahrani Ha.im Alsuwat Emad Alsuwat independent Researcher Specia.izing in Data Security and Privacy Taif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia

Bayesian networks are a powerful class of graphical decision models used to represent causal rela.ionships among ***,the relia.ility and integrity of learned Bayesian network models are highly dependent on the qua.ity of incoming data *** of the primary challenges with Bayesian networks is their vulnera.ility to adversa.ial data poisoning attacks,wherein ma.icious data.is injected into the training dataset to nega.ively influence the Bayesian network models and impair their *** this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning *** framework utilizes latent va.iables to quantify the amount of belief between every two nodes in each causal model over *** use our innova.ive methodology to tackle an important issue with data poisoning assaults in the context of Bayesian *** regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependa.ility of Bayesian network structure learning techniques,such as the PC *** doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky ***,our research investigates one particular use case,the“Visit to a.ia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost *** results demonstrate the promising efficacy of latent va.iables in detecting and mitiga.ing the threat of data poisoning ***,our proposed latent-based framework proves to be sensitive in detecting ma.icious data poisoning attacks in the context of stream data.

关键词： Bayesian networks data poisoning attacks latent va.iables structure learning algorithms adversa.ial attacks

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A Hybrid Classifica.ion and identifica.ion of Pneumonia Using African Buffalo Optimiza.ion and CNN from Chest X-Ray images

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2497-2517页

作者： Nasser Alalwan Ahmed i.Taloba Amr Abozeid Ahmed ibra.im alzahrani a.i H.Al-Bayatti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia informa.ion System Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathema.ics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology institute(CTi) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflamma.ion in the *** there is so much informa.ion available fromva.ious X-ray images,diagnosing pneumonia has typically proven *** improve image qua.ity and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automa.ic classifica.ion and identifica.ion of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimiza.ion(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classifica.ion and identifica.ion of *** results on va.ious datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classifica.ion *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimiza.ion convolutional neural network pneumonia X-ray

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A review of mecha.ism of action of outdoor exposure in preventing myopia.incidence and progression

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interna.ional Journal of Ophthalmology(English edition) 2024年第6期17卷 1144-1155页

作者： Chikezie Grand ihesiulor Khaled alzahrani Hema Rhadhakrishnan Division of Pharmacy&Optometry School of Health SciencesFaculty of BiologyMedicine and HealthThe University of ManchesterManchester M139PLUnited Kingdom Department of Ophthalmology King Fahad Armed Forces HospitalJeddah 23311Saudi Arabia

Va.ious studies have suggested several environmental,pharmacological,medical,and optical interventions and some are in use but their efficacy in myopia control may be transient,and the cellular,molecular,and biochemical mecha.isms involved *** exposure is currently regarded as an effective and enduring strategy in the control of myopia development and ***,the mecha.ism behind the effect of outdoor exposure and its associa.ion with genetic predisposition and other rela.ively more significant environmental factors on myopia.is still a *** review focuses on survey-based and intervention-based studies carried out to propose a mecha.ism that accounts for myopia development and important for its control.

关键词： outdoor exposure daylight exposure myopia control myopia

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry ima.ing and Edge Computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barna.i Krishan Kumar Neeraj Kumar Bander alzahrani Amal Almansour Faculty of Computing and informa.ion Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in va.ious regions around the world,with countless buried landmines affecting numerous human *** detona.ion of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anoma.ies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applica.ions like landmine *** processing data locally on a nearby edge server,edge computing can reduce communica.ion latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-ma.ing and more efficient landmine detection,potentially sa.ing lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry ima.ing based Classifica.ion System(Ma.iCS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automa.ic image classifica.ion using magnetometry dataset *** simula.ing the proposal in va.ious network scena.ios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a ma.imum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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implica.ions of electrode modifica.ions in aqueous orga.ic redox flow batteries

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Journal of Energy Chemistry 2024年第8期95卷 499-510,I0011页

作者： Za.id Manzoor Bhat mohammad Furquan Muhammad Aurang Zeb Gul Sial Umair Alam a.if Saeed alzahrani mohammad Qamar interdisciplinary Research Center for Hydrogen Technologies and Carbon Management(iRC-HTCM) King Fahd University of Petroleum&Minerals(KFUPM)Dhahran 31261Saudi Arabia Department of Materials Science and Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia interdisciplinary Research Center for Sustainable Energy Systems(iRC-SES) King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia

Aqueous orga.ic redox flow batteries(RFBs)exhibit favorable characteristics,such as tuna.ility,multielectron transfer capa.ility,and sta.ility of the redox active molecules utilized as anolytes and catholytes,ma.ing them very viable contenders for large-scale grid storage *** attention has been paid on the development of efficient redox-active molecules and their performance optimiza.ion through chemical substitutions at va.ious places on the backbone as part of the pursuit for high-performance *** the fact that electrodes are vital to optimal performance,they have not garnered significant *** research has been conducted on the effects of electrode modifica.ions to improve the performance of *** primary empha.is has been given on the impact of electrode engineering to augment the efficiency of aqueous orga.ic *** overview of electron transfer at the electrode-electrolyte interface is *** implica.ions of electrode modifica.ion on the performance of redox flow batteries,with a particular focus on the anodic and cathodic half-cells separately,are then *** each section,significant discrepancies surrounding the effects of electrode engineering are thoroughly exa.ined and ***,we have presented a comprehensive assessment along with our perspectives on the future trajectory.

关键词： Redox flow batteries Electrode modifica.ion Orga.ic redox molecules Outer sphere and inner sphere

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The User Analysis of Amazon Using Artificial intelligence at Customer Churn

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Journal of Data Analysis and informa.ion Processing 2024年第1期12卷 40-48页

作者： moha.med a.i alzahrani informa.ion Technology Department Computing and ICT Faculty Taif University Taif Saudi Arabia

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the ba.is of our customer churn to deduct the mea.ing full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review ha.ing more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. in this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scena.io. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall compa.ison.

关键词： Customer Churn Machine Learning Amazon Fine Food Reviews Data Science Artificial intelligence

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Burden of routine orthopedic implant removal a single center retrospective study

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World Journal of Orthopedics 2024年第2期15卷 139-146页

作者： Ammar K AlOmran Nader Alosaimi Ahmed A Alshaikhi Omar M Bakhurji Kha.id J alzahrani Ba.il Ziyad Salloot Ta.im Omar Alabduladhem Ahmed i AlMulhim Arwa Alumran Department of Orthopedic College of MedicineImam Abdulrahman Bin Faisal UniversityDammam 31441Saudi Arabia Health informa.ion Management and Technology Department Imam Abdulrahman Bin Faisal UniversityDammam 31441Saudi Arabia

BACKGROUND Open reduction and internal fixa.ion represent prevalent orthopedic procedures,sparking ongoing discourse over whether to retain or remove asymptoma.ic *** consensus on this matter is paramount for orthopedic *** study aims to quantify the impact of routine implant removal on pa.ients and healthcare fa.ilities.A retrospective analysis of implant removal cases from 2016 to 2022 at King Fahad Hospital of the University(KFHU)was conducted and subjected to sta.istical *** these cases,44%necessitated hospita.iza.ion exceeding one day,while 56%required only a single *** exhibited a 55%need for extended hospital stays,contrasting with 22.8%among the pediatric *** complica.ion rate was 6%,with all pa.ients experiencing at least one ***,34.1%required sick leave and 4.8%exceeded 14 *** anesthesia was predominant(88%).Routine implant removal introduces unwarranted complica.ions,particularly in adults,potentially prolonging *** procedure strains hospital resources,tying up the opera.ing room that could otherwise accommodate critical *** defined institutional guidelines are impera.ive to regulate this *** To measure the burden of routine implant removal on the pa.ients and *** This is a retrospective analysis study of 167 routine implant removal cases treated at KFHU,a tertiary hospital in Saudi *** were collected in the orthopedic department at KFHU from February 2016 to August 2022,which includes routine asymptoma.ic implant removal cases across all age *** indica.ions such as infection,pain,implant failure,malunion,nonunion,restricted range of motion,and prominent hardware were *** who had external fixators removed or joints replaced were also *** Between February 2016 and August 2022,360 implants were retrieved;however,only 167 of those who met the inclusion criteria were included in this

关键词： implant removal Healed fracture Orthopedic implant Complica.ions Healthcare system

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UNet Based onMulti-Object Segmenta.ion and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Muda.i Abdulwahab Alazeb Asaad Algarni Hamdan A.alzahrani Jeongmin Park Department of informa.ion Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia informa.ion Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our a.ility to identify multiple objects and understand complex *** technologies,such as augmented rea.ity-driven scene integra.ion,robotic na.iga.ion,autonomous driving,and guided tour systems,hea.ily rely on this type of scene *** paper presents a novel segmenta.ion approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmenta.ion using the fine-tuned UNet ***,we use an annota.ion tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmenta.ion and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classifica.ion stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evalua.ion performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmenta.ion blob fourier transform convolution neural network

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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus images

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Computer Systems Science & Engineering 2024年第2期48卷 511-528页

作者： Anas Bilal Azhar imran Talha.imtiaz Baig Xiaowen Liu Haixia Long Abdulkareem alzahrani Muhammad Sha.iq College of informa.ion Science and Technology Hainan Normal UniversityHaikou571158China Department of Crea.ive Technologies Air UniversityIslamabadPakistan School of Life Science and Technology University of Electronic Science and Technology of China UESTCChengduChina School of Science and Technology University of Management and Technology UMTLahorePunjabPakistan Faculty of Computer Science&informa.ion Technology Al Baha UniversityAl BahaSaudi Arabia School of informa.ion Engineering Qujing Normal UniversityQujingChina

Artificial intelligence(Ai)is being increa.ingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a lea.ing cause of visual impairment and blindness ***,previous automated VTDR detection methods have mainly relied on manual feature extraction and classifica.ion,lea.ing to *** paper proposes a novel VTDR detection and classifica.ion model that combines different models through majority *** proposed methodology involves preprocessing,data augmenta.ion,feature extraction,and classifica.ion *** use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for *** tested our model on the iDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evalua.ion tests,*** proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

关键词： Diabetic retinopathy(DR) fundus images(Fis) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classifica.ion

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