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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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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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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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Computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： saeed masoud alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi 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 Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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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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URL Phishing Detection Using Particle Swarm Optimization and Data Mining

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Computers, Materials & Continua 2022年第12期73卷 5625-5640页

作者： saeed M.alshahrani Nayyar Ahmed Khan Jameel Almalki Waleed Al Shehri College of Computing and IT-Shaqra University Shaqra155572Saudi Arabia Department of Computer Science College of Computer in Al-LeithUmm Al-Qura UniversityMakkahSaudi Arabia

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for *** techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing *** selection based on various techniques to identify the phishing candidates from the URL is *** this approach,the features mined from the URL are extracted using data mining *** features are selected on the basis of URL *** classification of these features identified by the data mining rules is done using PSO *** selection of features with PSO optimization makes it possible to identify phishing *** a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this *** experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL ***,it can minimize processing time for identifying the phishing website ***,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.

关键词： Phishing particle swarm optimization feature selection data mining classification cloud application

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Patterns of Infertility and Abortion in Saudi Arabia: A Cross-Sectional Study

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Open Journal of Obstetrics and Gynecology 2024年第6期14卷 911-922页

作者： Majed saeed alshahrani Department of Obstetrics and Gynaecology College of Medicine Najran University Najran Saudi Arabia

Background: Infertility and abortion are significant reproductive health concerns globally, impacting individuals' well-being and healthcare systems. In Saudi Arabia, cultural, social, and religious factors influence perceptions and management of these issues, necessitating research to understand the patterns and associated factors. Study Aim: This cross-sectional study aimed to investigate the patterns of infertility and abortion among 458 women in Saudi Arabia, focusing on demographic profiles, reproductive histories, medical conditions, and lifestyle factors. Methodology: Participants were recruited from healthcare facilities, and data on age, BMI, reproductive history, medical conditions, assisted reproduction methods, and lifestyle behaviors were collected. Statistical analyses, including chi-square tests, were conducted to examine associations between variables. Results: The majority of participants were aged 30 - 39 years (47.4%), with 76.9% experiencing secondary infertility. Among those with offspring (76.9%), 31% reported using assisted reproduction methods, primarily ovarian stimulators (54.2%). Abortion history was reported by 39.5% of participants. Significant associations were found between age and infertility type (X2 = 5.8, p = 0.054), having offspring and infertility type (X2 = 458.0, p = 0.001), menstrual irregularity and infertility type (X2 = 11.4, p = 0.001), and abortion history and delayed fertility (X2 = 10.4, p = 0.001). Conclusion: Our study reveals significant associations between demographic, medical, and lifestyle factors with infertility patterns and abortion history among Saudi women. These findings emphasize the need for tailored interventions addressing medical conditions, age-related factors, and access to reproductive healthcare services.

关键词： Infertility Abortion Saudi Arabia Reproductive Health Assisted Reproduction Cross-Sectional Study

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Shallow-Water Origin of a Devonian Black Shale, Cleveland Shale Member (Ohio Shale), Northeastern Ohio, USA

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Open Journal of Geology 2014年第12期4卷 636-653页

作者： saeed alshahrani James E. Evans Department of Geology Bowling Green State University Bowling Green OH USA EXPEC Advanced Research Center Saudi ARAMCO Dhahran Saudi Arabia

Black shales are usually interpreted to require anoxic bottom waters and deeper water sedimentation. There has long been a debate about whether the Devonian Cleveland Shale Member of the Ohio Shale (CSM) was deposited in shallow- or deep-water depositional environments. This study looked at the CSM at 3 stratigraphic sections and 5 well cores in northeastern Ohio. The CSM mostly consists of sapropelite (interbedded carbonaceous black mudstones and gray calcareous claystones). The black and gray “shales” are rhythmically bedded at micro- (<1 cm thick), meso- (<10 cm thick) and macro-scales (10s of cm thick) and represent changes in organic matter content (ranging from 7% - 20% TOC). Three types of event layers are interbedded with the mudrocks: 1) tempestites, 2) proximal turbidites, and 3) hyperpycnites. Individual tempestites and turbidites are laterally continuous?≥35 km, while hyperpycnites are too thin (<1 cm) to trace laterally. Tempestites consist of hummocky stratified sandstones with groove casts and escape burrows overlain by planar laminated sandstones with wave ripples at the top. Tempestites average 13 cm thick, but can be amalgamated up to 45 cm thick, and are more common in the lower half of the unit. Turbidites are incomplete Bouma sequences that average 6 cm thick, show evidence of combined flow (“wave-modified turbidites”), and are more common toward the top of the unit. Hyperpycnites (density underflows from river discharge) consist of inverse-to-normal graded sandy or silty microlaminae that have been studied primarily by using petrography and SEM. Condensed sections in the CSM are probable firmgrounds with carbonate concretions, and indicate intervals of low sedimentation rates. The evidence shows that the CSM depositional environment was receiving siliciclastics from the northeast (e.g., Catskill delta), and that the coarser-grained clastic sediment was primarily transported as density underflows (turbidites and hyperpycnites). However, signific

关键词： Black Shales Tempestites Hyperpycnites Devonian Appalachian Basin Catskill Delta

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Systematic Survey on Big Data Analytics and Artificial Intelligence for COVID-19 Containment

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Computer Systems Science & Engineering 2023年第11期47卷 1793-1817页

作者： saeed M.alshahrani Jameel Almalki Waleed Alshehri Rashid Mehmood Marwan Albahar Najlaa Jannah Nayyar Ahmed Khan Department of Computer Science College Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Computer in Al-LithUmm Al-Qura UniversityMakkahSaudi Arabia High-Performance Computing Center King Abdulaziz UniversityJeddahSaudi Arabia

Artificial Intelligence(AI)has gained popularity for the containment of COVID-19 pandemic *** AI techniques provide efficient mechanisms for handling pandemic *** methods,protocols,data sets,and various validation mechanisms empower the users towards proper decision-making and procedures to handle the *** so many tools,there still exist conditions in which AI must go a long *** increase the adaptability and potential of these techniques,a combination of AI and Bigdata is currently gaining *** paper surveys and analyzes the methods within the various computational paradigms used by different researchers and national governments,such as China and South Korea,to fight against this *** process of vaccine development requires multiple medical *** process requires analyzing datasets from different parts of the *** learning and the Internet of Things(IoT)revolutionized the field of disease diagnosis and disease *** accurate observations from different datasets across the world empowered the process of drug development and drug *** overcome the issues generated by the pandemic,using such sophisticated computing paradigms such as AI,Machine Learning(ML),deep learning,Robotics and Bigdata is essential.

关键词： COVID-19 IoT artificial intelligence big data coronavirus deep learning robotics machine learning

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Patterns of Infertility and Abortion in Saudi Arabia: A Cross-Sectional Study

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Open Journal of Obstetrics and Gynecology（妇产科期刊） 2024年第6期 911页

作者： Majed saeed alshahrani

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Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

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Computer Systems Science & Engineering 2023年第10期47卷 855-871页

作者： Z.Faizal Khan saeed M.alshahrani Abdulrahman Alghamdi Someah Alangari Nouf Ibrahim Altamami Khalid A.Alissa Sana Alazwari Mesfer Al Duhayyim Fahd N.Al-Wesabi Department of Computer Engineering College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Science and Humanities DawadmiShaqra UniversityShaqraSaudi Arabia Department of Mathematics College of EducationShaqra UniversityShaqraSaudi Arabia Saudi Aramco Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAlaflaj16828Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

The Internet of Things(IoT)is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare,in health service to energy,and in developed to *** develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that *** progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence(AI)andMachine Learning(ML)devices are crucial fact to realize security in IoT *** can be required for minimizing the issues of security based on IoT devices ***,this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection fromthe cloud and IoT *** proposed MFORELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT *** accomplish this,the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful ***,the proposed models will receive the pre-processing data and carry out the classifier *** boosting the efficiency of the proposed models,theMFOtechnique was utilized to *** experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset,and the outcomes are examined under distinct aspects.

关键词： Mayfly optimization machine learning artificial intelligence cybersecurity threat detection

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