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Analysis and Assessment of Wind Energy Potential of Almukalla in Yemen

Computers, materials & Continua 2022年第8期72卷 3113-3129页

作者： murad A.A.Almekhlafi Fahd N.Al-Wesabi majdy m.Eltahir Anwer mustafa Hilal Amin m.El-Kustaban Abdelwahed motwakel Ishfaq Yaseen manar Ahmed hamza Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayelAseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayelAseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Electronic Department Faculty of EngineeringUniversity of Science and TechnologySana’aYemen

Energy is an essential element for any civilized country’s social and economic development,but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the *** Republic of Yemen has very good potential to use renewable ***,we find few studies on renewable wind energy in *** the lack of a similar analysis for the coastal city,this research newly investigates wind energy’s potential near the Almukalla area by analyzing wind ***,evaluation,model identification,determination of available energy density,computing the capacity factors for several wind turbines and calculation of wind energy were extracted at three heights of 15,30,and *** wind speeds were obtained only for the currently available data of five recent years,2005–*** study involves a preliminary assessment of Almukalla’s wind energy potential to provide a primary base and useful insights for wind engineers and *** research aims to provide useful assessment of the potential of wind energy in Almukalla for developing wind energy and an efficient wind *** Weibull distribution shows a perfect approximation for estimating the intensity of Yemen’s wind *** on both theWeibullmodel and the results of the annual wind speed data analysis for the study site in mukalla,the capacity factor for many turbines was also calculated,and the best suitable turbine was *** to the International Wind Energy Rating criteria,Almukalla falls under Category 7,which is,rated“Superb”most of the year.

关键词： Almukalla energy potential Rayleigh distribution Weibull distribution wind power density wind speed

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Cuckoo Optimized Convolution Support Vector machine for Big Health Data Processing

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Computers, materials & Continua 2022年第11期73卷 3039-3055页

作者： Eatedal Alabdulkreem Jaber S.Alzahrani majdy m.Eltahir Abdullah mohamed manar Ahmed hamza Abdelwahed motwakel mohamed I.Eldesouki mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha 62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many *** cloud-based IoT health providers have been described in the literature ***,there are a number of issues related to time consumed and overall network performance when it comes to big data *** the existing method,less performed optimization algorithms were used for optimizing the *** the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector machine(CSVm)was *** research presents a method for analyzing healthcare information that uses in future *** major goal is to take a variety of data while improving efficiency and minimizing process *** suggested method employs a hybrid method that is divided into two *** the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor *** the second stage,CSVm is used which combines the benefits of convolutional neural network(CNN)and *** CSVm modifies CNN’s convolution product to learn hidden deep inside data *** improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog *** results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.

关键词： Healthcare convolutional support vector machine feature selection chaotic cuckoo optimization accuracy processing time convolutional neural network

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Blockchain Driven metaheuristic Route Planning in Secure Vehicular Adhoc Networks

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Computers, materials & Continua 2022年第12期73卷 6461-6477页

作者： Siwar Ben Haj Hassine Saud SAlotaibi Hadeel Alsolai Reem Alshahrani Lilia Kechiche mrim m.Alnfiai Amira Sayed A.Aziz manar Ahmed hamza Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi 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 Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Sciences and Technology Taif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Nowadays,vehicular ad hoc networks(VANET)turn out to be a core portion of intelligent transportation systems(ITSs),that mainly focus on achieving continual Internet connectivity amongst vehicles on the *** VANET was utilized to enhance driving safety and build an ITS in modern *** safety is a main portion of VANET,the privacy and security of these messages should be *** this aspect,this article presents a blockchain with sunflower optimization enabled route planning scheme(BCSFO-RPS)for secure *** presented BCSFO-RPSmodel focuses on the identification of routes in such a way that vehicular communication is *** addition,the BCSFO-RPS model employs SFO algorithm with a fitness function for effectual identification of ***,the proposed BCSFO-RPS model derives an intrusion detection system(IDS)encompassing two processes namely feature selection and *** detect intrusions,correlation based feature selection(CFS)and kernel extreme machine learning(KELm)classifier is *** performance of the BCSFO-RPS model is tested using a series of experiments and the results reported the enhancements of the BCSFO-RPS model over other approaches with maximum accuracy of 98.70%.

关键词： VANET sunflower optimization machine learning blockchain intrusion detection

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Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

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Computers, materials & Continua 2022年第12期73卷 5441-5458页

作者： Heba G.mohamed Saud S.Alotaibi majdy m.Eltahir Heba mohsen manar Ahmed hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi 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 Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time *** several applications comprise IoD in real time environment,significant interest has been received by research *** IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD *** article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(mFSOSAEID)in the IoD *** major intention of the mFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD *** do so,the proposed mFSOSAE-ID technique firstly pre-processes the input data into a compatible *** addition,the presented mFSOSAEID technique designs a moth flame optimization based feature selection(mFOFS)technique to elect appropriate ***,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of *** performance validation of the mFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the mFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

关键词： Internet of drones unmanned aerial vehicles security intrusion detection machine learning

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Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

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Computers, materials & Continua 2022年第7期72卷 1195-1212页

作者： Ibrahim Abunadi maha m.Althobaiti Fahd N.Al-Wesabi Anwer mustafa Hilal mohammad medani manar Ahmed hamza mohammed Rizwanullah Abu Serwar Zamani Department of Information Systems Prince Sultan UniversityRafha StreetRiyadh11586Saudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia

The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,***,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(UAVs),which are treated as an efficient remote sensing technique to gather data from large *** are commonly employed in the industrial sector to solve several issues and help decision *** the strict regulations leading to data privacy possibly hinder data sharing across autonomous *** learning(FL)becomes a recent advancement of machine learning(mL)which aims to protect user *** this aspect,this study designs federated learning with blockchain assisted image classification model for clustered UAV networks(FLBIC-CUAV)on IIoT *** proposed FLBIC-CUAV technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image *** UAV cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the UAVs for effective *** addition,blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud ***,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV *** experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.

关键词： UAV networks clustering secure communication blockchain federated learning image classification

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Revisiting the Bjerrum's correction factor:Use of the liquidity index for assessing the effect of soil plasticity on undrained shear strength

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Journal of Rock mechanics and Geotechnical Engineering 2015年第6期7卷 716-721页

作者： Kamil Kayabali Ozgur Akturk mustafa Fener Orhan Dikmen Furkan hamza Harputlugil Department of Geological Engineering Ankara University Department of Geological Engineering Akdeniz University

The undrained shear strength （su） of fine-grained soils that can be measured in situ and in laboratory isone of the key geotechnical parameters. The unconfined compression test （UCT） is widely used in laboratoryto measure this parameter due to its simplicity; however, it is severely affected by sampledisturbance. The vane shear test （VST） technique that is less sensitive to sample disturbance involves acorrection factor against the soil plasticity, commonly known as the Bjerrum＇s correction factor, m. Thisstudy aims to reevaluate the Bjerrum＇s correction factor in consideration of a different approach and arelatively new method of testing. Atterberg limits test, miniature VST, and reverse extrusion test （RET）were conducted on 120 remolded samples. The effect of soil plasticity on undrained shear strength wasexamined using the liquidity index instead of Bjerrum＇s correction factor. In comparison with the resultobatined using the Bjerrum＇s correction factor, the undrained shear strength was better representedwhen su values were correlated with the liquidity index. The results were validated by the RET, whichwas proven to take into account soil plasticity with a reliable degree of accuracy. This study also showsthat the RET has strong promise as a new tool for testing undrained shear strength of fine-grained soils.

关键词： Soil plasticity Undrained shear strength Bjerrum's correction factor Vane shear test(VST) Reverse extrusion test(RET)

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modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

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Computers, materials & Continua 2022年第5期71卷 3953-3968页

作者： mesfer Al Duhayyim Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Hiba m.Burbur mohammad Alamgeer Anwer mustafa Hilal manar Ahmed hamza mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer Science College of Science and Arts in Al-BukairiyahQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Information Systems College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods.

关键词： Smart cities artificial intelligence urban transportation deep learning weather condition TFP

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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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Trends in incidence and management of cancer of the ampulla of Vater

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World Journal of Gastroenterology 2014年第29期20卷 10144-10150页

作者： Florian Rostain Samia hamza Antoine Drouillard Jean Faivre Anne-marie Bouvier C?me Lepage Registre Bourguignon des Cancers Digestifs INSERM U866CHU de DijonFaculté de Médecine21079 Dijon CedexFrance

AIm: To provide trends in incidence, management and survival of cancer of the ampulla of Vater in a well-defined French population.

关键词： Cancer of the ampulla of Vater Incidence Survival Treatment Epidemiology

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A Controlled Composition Kinetic Study Dissolution of Barium Fluoride

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人工晶体学报 1990年第2期19卷 140-145页

作者： S.m.hamza Chemistry Department Faculty of ScienceEl-Menoufia UniversiyShebin El-komEgypt

A constant-composition technique has been used to study thekinetic of dissolution of barium fluoride in which the activities of the latticeions were maintained constant during the experiments. The method enableskinetic data to be obtained at very low undersaturation. A striking change inthe mechanism of dissolution is observed as the driving force is changed. Athigher driving forces (relative undersaturation 0.15--0.20) the process appearsto follow normal diffusion-controlled dissolution, while at very low undersatu-ration (0.04--0.12) a surface controlled reaction predominates. The tworegions of undersaturation a show marked difference in reactions.

关键词： barium fluoride constant-composition technique kinetic

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