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IoT-Cloud Assisted Botnet Detection Using Rat swarm Optimizer with Deep Learning

Computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： saeed Masoud Alshahrani Fatma s.Alrayes Hamed alqahtani jaber s.Alzahrani Mohammed Maray sana Alazwari Mohamed A.shamseldin Mesfer Al Duhayyim Department of Computer science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat swarmOptimizer with Deep Learning(BDC-RsODL)*** presented BDC-RsODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RsO algorithm is exploited for effective selection of subset of ***,Long short TermMemory(LsTM)algorithm is utilized for both identification and classification of ***,sine Cosine Algorithm(sCA)is executed for fine-tuning the hyperparameters related to LsTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RsODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRsODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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A Robust Asynchrophasor in PMU Using second-Order Kalman Filter

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Computers, Materials & Continua 2023年第2期74卷 2557-2573页

作者： Nayef alqahtani Ali alqahtani Electrical and Computer Engineering Department School of Engineering and Computer ScienceOakland UniversityRochesterMI48326USA King Faisal University Al-Ahsa31982Kingdom of Saudi Arabia Department of Networks and Communications Engineering Najran UniversityNajran61441Kingdom of Saudi Arabia

Phasor Measurement Units(PMUs)provide Global Positioning system(GPs)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid *** synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of ***,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating *** paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using second-Order Kalman *** is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access *** MATLAB model has been created to implement the proposed method in the presence of Gaussian and *** results have shown the proposed method which is second-Order Kalman Filter outperforms the existing *** results were tested usingMean square Error(MsE).The proposed second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.

关键词： Distributed generation asynchrophasor kalman filter phasor estimation phasor measurement unit state variables mean square error and signal to noise ratio

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Assessment of Breast Cancer Prevention Practices among Women Attending Primary Health Care in Abha City, Aseer Region, saudi Arabia

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International Journal of Clinical Medicine 2024年第2期15卷 31-54页

作者： sabah Mohammed Asiri sultan Yahia Alfifii Tagreed Khairan Al-Rashidi sager Misfer alqahtani Faiz Abdulrahman Alshafa Fayez Mari Alamri Amal Mohammed Asiri Fatima Mohammed Ali Almagadi Thuraya Mohammed Asiri Department of Therapeutic Assistance Management General Directorate of Health Affairs Asser Region Abha Saudi Arabia Department of Health Affairs General Directorate of Health Affairs Asser Region Abha Saudi Arabia Department of Kidney King Khaled Hospital Hail Saudi Arabia Department of Nursing Sarat Abida Hospital Aseer Saudi Arabia Department of Radiation Oncology King Faisal Medical City Abha Saudi Arabia Department of Health Affairs KSA MOH Department of Kidney Dialysis Centers General Directorate of Health Affairs Aseer Region Abha Saudi Arabia Department of Operation Ahad Rafidah Hospital General Directorate of Health Affairs in Asseer Region Abha Saudi Arabia south Qunfudah General Hospital Jizan Saudi Arabia Armed Forces southern Region Hospital Aseer Saudi Arabia

Cancer is a leading cause of death worldwide, with breast cancer being the most common (2.26 million new cases and 685,000 deaths). In saudi Arabia, breast cancer ranked the first among females in 2014, accounting for 15.9% of all cancers reported among saudi nationals and 28.7% of all cancers reported among females of all ages. Early detection of breast cancer could decrease the risks, have a better prognosis, and have better outcomes/more successful treatments. Prevalence of breast cancer reached more than 25% of all diagnosed cancer in the kingdom among women. Aim: This study aims to assess the knowledge and performance of women attending primary care centers about breast self-examination and mammogram screening for prevention and early detection of breast cancer in Abha city primary healthcare centers, Kingdom of saudi Arabia. Research Method: cross sectional design was conducted by using questionnaire, which was distributed to primary care center nurses. The collected data was statistically analyzed using the statistical Package for social sciences, version 25. Results: The study found that participants had poor awareness and knowledge about breast self-examination, risk factors for breast cancer, and trends and practices in early diagnosis of breast cancer. Conclusion and Recommendations: It recommends increasing awareness campaigns and providing educational programs to improve knowledge and practices.

关键词： Assessment Breast Cancer Prevention Practices Women Attending Health Care Centers Abha City

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Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

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Computer systems science & Engineering 2023年第2期44卷 1433-1449页

作者： Tariq Mohammed alqahtani Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent years,huge volumes of healthcare data are getting generated in various *** advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and *** to such massive generation of big data,the utilization of new methods based on Big Data Analytics(BDA),Machine Learning(ML),and Artiﬁcial Intelligence(AI)have become *** this aspect,the current research work develops a new Big Data Analytics with Cat swarm Optimization based deep Learning(BDA-CsODL)technique for medical image classiﬁcation on Apache spark *** aim of the proposed BDA-CsODL technique is to classify the medical images and diagnose the disease ***-CsODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classiﬁ*** addition,BDA-CsODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical ***,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature *** Gradient Descent(sGD)model is used for parameter tuning ***,CsO with Long short-Term Memory(CsO-LsTM)model is employed as a classiﬁcation model to determine the appropriate class labels to *** sGD and CsO design approaches help in improving the overall image classiﬁcation performance of the proposed BDA-CsODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CsODL technique under different measures.

关键词： Big data analytics healthcare deep learning image classiﬁcation biomedical imaging machine learning

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Machine Learning Techniques for Intrusion Detection systems in sDN-Recent Advances,Challenges and Future Directions

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Computer Modeling in Engineering & sciences 2023年第1期134卷 89-119页

作者： Gulshan Kumar Hamed alqahtani shaheed Bhagat singh state University Firozpur152024India King Khalid University Abha61421Saudi Arabia

software-Defined Networking(sDN)enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting *** Machine Learning(ML)techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems(IDss)considering logically centralized control and global view of the network provided by *** IDss have developed using advances in machine learning and deep *** study presents a comprehensive review of recent work ofML-based IDs in context to *** presents a comprehensive study of the existing review papers in the *** is followed by introducing intrusion detection,ML techniques and their ***,we present a systematic study of recent works,discuss ongoing research challenges for effective implementation of ML-based intrusion detection in sDN,and promising future works in this field.

关键词： Controller intrusion detection intrusion detection system OpenFlow security software defined networking traffic analysis

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Development of Non-ABO Red Blood Cell Alloantibodies in Patient Undergoing Allogeneic Haematopoietic stem Cell Transplant

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Case Reports in Clinical Medicine 2024年第5期13卷 162-170页

作者： Ahmed Alsuhaibani Khalid Batarfi Ahmed Alharbi Haya Alwasel Abdullah Alenazi Abdulmohsen Alotaibi Jalal Hassan Rayyan Alotaibi Hajer Aziz Nourah Alharethi sara Alobaidi Maram Alonayzan sanad Alharthi Majd Alanazi sarah Alotaibi Ahmed shareefi Bandar alqahtani Department of Laboratory and Pathology King Abdulaziz Medical City of National Guard Riyadh Saudi Arabia

Alloantibodies that are non ABO Alloimmunization to protein antigens happens only after exposure, in contrast to ABO isohaemagglutinins, which are present naturally, even in the absence of prior exposure. It is recognized that while non-ABO RBC antibodies are less common than ABO antibodies, they generate essentially the same issues that lead to unfavorable clinical results. If non-ABO alloantibodies are identified early on, these issues related complications may be avoided This call for an in-depth understanding of the recipient and donor’s ABO-Rh grouping, antibody screening, and the phenotype of certain antigens. Equally important, the temporal association time between transplantation and hemolysis can help identify the underlying mechanism of hemolysis and direct appropriate management. Therefore, for that, it is crucial to identify the etiology of post-HsCT anemia for prevention and therapy, in addition to a thorough grasp of the mechanism of anemia in non-ABO-incompatible HsCT and the temporal link between HsCT and anemia. Finding the cause of post-HsCT anemia is essential for prevention and therapy, in addition to a thorough grasp of the mechanism of anemia in non-ABO-incompatible HsCT and the temporal link between HsCT and anemia. Therefore, for that, it is crucial to identify the etiology of post-HsCT anemia. In this case report review, we would like to highlight the vital role of transfusion medicine services and stem cell clinical teams in paying particular attention to the clinical significance of non-ABO alloantibodies involved to avoid causing overt hemolysis of incompatible donor RBCs or delayed erythropoiesis. Considering the fact that some of the Haematopoietic stem cell transplant centers do not give an attention to the other non-ABO RBC antigens.

关键词： Haematopoietic stem Cell Transplant (HsCT) Non-ABO Red Blood Cell Antibodies Alloantibodies

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Deep Learning-Based Cancer Detection-Recent Developments,Trend and Challenges

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Computer Modeling in Engineering & sciences 2022年第3期130卷 1271-1307页

作者： Gulshan Kumar Hamed alqahtani shaheed Bhagat singh state University FerozepurIndia King Khalid University AbhaSaudi Arabia

Cancer is one of the most critical diseases that has caused several deaths in today’s *** most cases,doctors and practitioners are only able to diagnose cancer in its later *** the later stages,planning cancer treatment and increasing the patient’s survival rate becomes a very challenging ***,it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery *** and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases,including cancer ***,manual interpretation of medical images is costly,time-consuming and ***,deep learning,a subset of artificial intelligence,is gaining increasing attention from practitioners in automatically analysing and interpreting medical images without their *** learning methods have reported extraordinary results in different fields due to their ability to automatically extract intrinsic features from images without any dependence on manually extracted *** study provides a comprehensive review of deep learning methods in cancer detection and diagnosis,mainly focusing on breast cancer,brain cancer,skin cancer,and prostate *** study describes various deep learningmodels and steps for applying deep learningmodels in detecting *** developments in cancer detection based on deep learning methods have been critically analysed and summarised to identify critical challenges in applying them for detecting cancer accurately in the early *** on the identified challenges,we provide a few promising future research directions for fellow researchers in the *** outcome of this study provides many clues for developing practical and accurate cancer detection systems for its early diagnosis and treatment planning.

关键词： Autoencoders(AEs) cancer detection convolutional neural networks(CNNs) deep learning generative adversarial models(GANs) machine learning

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A Fractional Fourier Based Medical Image Authentication Approach

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Computers, Materials & Continua 2022年第2期70卷 3133-3150页

作者： Fayez alqahtani Mohammed Amoon Walid El-shafai Department of software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Department of Computer science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Patient medical information in all forms is crucial to keep private and secure,particularly when medical data communication occurs through insecure ***,there is a bad need for protecting and securing the color medical images against impostors and *** this paper,an optical medical image security approach is *** is based on the optical bit-plane Jigsaw Transform(JT)and Fractional Fourier Transform(FFT).Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach.A preceding bit-plane scrambling process is conducted on the input color medical images prior to the JT and FFT processes to accomplish a tremendous level of robustness and *** confirm the efficiency of the suggested security approach for secure color medical image communication,various assessments on different color medical images are examined based on different statistical security ***,a comparative analysis is introduced between the suggested security approach and other conventional cryptography *** simulation outcomes acquired for performance assessment demonstrate that the suggested security approach is highly *** has excellent encryption/decryption performance and superior security results compared to conventional cryptography approaches with achieving recommended values of average entropy and correlation coefficient of 7.63 and 0.0103 for encrypted images.

关键词： Medical image security encryption JT FFT cryptosystem

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Discrete mass conservation and stability analysis of the ocean-atmosphere model with coupling conditions

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Journal of Ocean Engineering and science 2023年第6期8卷 577-589页

作者： Taj Munir Anwarud Din saif Ullah M.Y.Malik A.s.alqahtani Abus salam school of mathematical sciences GC University68-BLahore 54600Pakistan Department of Mathematics sun Yat-sen University Guangzhou 510275P.R.China Department of Mathematics Government College UniversityLahore54000Pakistan Department of Mathematics College of SciencesKing Khalid UniversityAbha61413 Kingdom of Sauidi Arabia

In this work we considered bi-domain partial differential equations(PDEs)with two coupling interface *** one domain is corresponding to the ocean and the second is to the *** two coupling conditions are used to linked the interaction between these two *** we know that almost every engineering problem modeled via *** analytical solutions of these kind of problems are not easy,so we use numerical *** this study we discuss the two essential properties,namely mass conservation and stability analysis of two types of coupling interface conditions for the oceanatmosphere *** coupling conditions arise in general circulation models used in climate *** two coupling conditions are the Dirichlet-Neumann and bulk interface *** the stability analysis,we use the Godunov-Ryabenkii theory of normal-mode *** main empha-sis of this work is to study the numerical properties of coupling conditions and an important point is to maintain conservativity of the overall ***,for the numerical approximation we use two methods,an explicit and implicit *** implicit coupling have further two algorithms,monolithic algorithm and partitioned iterative *** partitioned iterative approach is complex as compared to the monolithic *** addition,the comparison of the numerical results are exhibited through graphical illustration and simulation results are drafted in tabular form to validate our theoretical *** novel characteristics of the findings from this paper can be of great importance in science and ocean engineering.

关键词： stability analysis Mass conservation Explicit coupling methods Implicit coupling methods Ocean-atmosphere coupling

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state-of-Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Deep Neural Network

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Computers, Materials & Continua 2022年第12期73卷 6289-6306页

作者： M.Premkumar R.sowmya s.sridhar C.Kumar Mohamed Abbas Malak s.alqahtani Kottakkaran sooppy Nisar Department of Electrical and Electronics Engineering Dayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Department of Electrical and Electronics Engineering National Institute of TechnologyTiruchirapalli620015Tamil NaduIndia Department of Electrical and Electronics Engineering M S Ramaiah Institute of TechnologyBengaluru560054KarnatakaIndia Department of Electrical and Electronics Engineering M.Kumarasamy College of EngineeringKarur639113Tamil NaduIndia Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Computers and Communications Department College of EngineeringDelta University for Science and TechnologyGamasa35712Egypt Faculty of Technology The GatewayDe Montfort UniversityLeicesterLE19BHUnited Kingdom Department of Mathematics College of Arts and SciencesPrince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

It is critical to have precise data about Lithium-ion batteries,such as the state-of-Charge(soC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric *** strategies for estimating battery soC,such as by including the coulomb counting and Kalman filter,have been *** a result of the differences in parameter values between each cell,when these methods are applied to highcapacity battery packs,it has difficulties sustaining the prediction accuracy of overall *** a result of aging,the variation in the parameters of each cell is higher as more time is spent in *** is suggested in this study to establish an soC estimate model for a Lithium-ion battery by employing an enhanced Deep Neural Network(DNN)*** is because the proposed DNN has a substantial hidden layer,which can accurately predict the soC of an unknown driving cycle during training,making it ideal for soC *** evaluate the nonlinearities between voltage and current at various soCs and temperatures,the proposed DNN is *** current and voltage data measured at various temperatures throughout discharge/charge cycles is necessary for training and testing *** the method has been thoroughly trained with the data collected,it is used for additional cells cycle tests to predict their *** simulation has been conducted for two different Li-ion battery *** to the experimental data,the suggested DNN-based soC estimate approach produces a low mean absolute error and root-mean-square-error values,say less than 5%errors.

关键词： Artificial intelligence deep neural network Li-ion battery parameter variation soC estimation

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