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Journal of Analysis and Testing 2018年第4期2卷 352-355页

作者： Amani AbdErahman Olalere Olusegun Abayomi abdelhafiz eltahir ahmed Abdurahman Hamid Nour Rosli bin Mohd Yunus Ghada Mohamed Ibrahim Nassereldeen ahmed Kabbashi Faculty of Science and Technology Omdurman Islamic UniversityOmdurmanSudan Faculty of Chemical and Natural Resources Engineering Universiti Malaysia PahangGambangMalaysia Faculty of Animal Production Bahri UniversityBahriSudan International Islamic University Malaysia MalaysiaKOEBTE EngineeringKuala LumpurMalaysia

The phenolic and antioxidant constituents in Acacia nilotica fruits have become an important source of medicinal and thera-peutic benefit with powerful biological *** study investigated the phenolic content and antioxidant capacity of powdered Acacia fruits with seeds and without *** phenolic content and antioxidant capacities in them were deter-mined using Folin-Ciocalteu and DPPH free radical-scavenging *** total phenolic and antioxidants of *** with seeds were spectrophotometrically determined to be 47.61 and 6.18%greater than when the seeds were removed from the dried fruits,*** LC-MS/QTOF analysis shows the presence of 282 and 214 phenolic compounds in the methanol extracts of *** with seeds and without seeds,*** present study,therefore,revealed that dried *** fruits with seeds have higher total phenolic content,antioxidant capacity,and bioactive constituents,which indi-cated that they have more medicinal value than fruits without seeds.

关键词： Acacia nilotica Liquid chromatography-mass spectrometry Total phenolic content DPPH antioxidant capacity

An energetic nano-fiber composite based on polystyrene and 1,3,5-trinitro-1,3,5-triazinane fabricated via electrospinning technique

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Defence Technology(防务技术) 2023年第9期27卷 1-8页

作者： Mahmoud abdelhafiz ahmed K.Hussein Waleed F.Khalil ahmed Elbeih Military TechnicalCollege(MTC) KobryElkobbahCairoEgypt Safety FuelCycleDepartment NRSRCEgyptianAtomicEnergyAuthority(EAEA)CairoEgypt

Electrospinning is a simple technique used to fabricate polymeric nano-fibrous *** nano-fibers have found a wide range of valuable applications in the biomedical ***,it has not been utilized with solid high explosives ***,the electrospinning technique has been used to fabricate polystyrene(PS)/1,3,5-trinitro-1,3,5-triazinane(RDX)composite *** governed electrospinning parameters,voltage,distance from the collector,flow rate,mandrel rotating speed,time,and solution concentration,that greatly affect the morphology of the obtained nanofibers were *** fabricated PS/RDX nano-fibers were characterized using scanning electron microscopy(SEM),X-ray diffractometer(XRD),and Fourier Transform Infrared(FTIR)*** impact and friction sensitivities of PS/RDX were also *** thermal behavior of the prepared composite and the pure materials were studied by the thermal gravimetric analysis technique(TGA).SEM results proved the fabrication of PS/RDX fibers in the nano-size via *** spectroscopy confirmed the existence of the characteristic functional groups of both PS and RDX in the composite *** sharp peaks showed the conversion of amorphous PS into crystalline shape via electrospinning and also confirmed the formation of PS/RDX *** PS fibers absorbed the heat and increased the onset decomposition of the pure RDX from 181.5 to 200.7℃in the case of PS/RDX ***,PS/RDX nano-fibers showed the relatively low impact and friction sensitivities of 100 J and 360 N *** results could introduce PS/RDX nanofibrous composite in the field of explosives detection with high levels of safety.

关键词： Electrospinning Nano-fibers 1,3,5-Trinitro-1,3,5-triazinane(RDX) Polystyrene(PS) Sensitivity

Diagnosing Breast Cancer Accurately Based on Weighting of HeterogeneousClassification Sub-Models

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Computer Systems Science & Engineering 2022年第9期42卷 1257-1272页

作者： Majdy Mohamed Eltayeb eltahir Tarig Mohammed ahmed Department of Information Systems College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Kingdom of Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdul-Aziz UniversityJeddah21589Kingdom of Saudi Arabia Department of Computer Sciences Faculty of Mathematical Sciences and InformaticsUniversity of KhartoumKhartoum11115Sudan

In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing lifeexpectancy, increasing urbanization and embracing Western lifestyles, the highprevalence of this cancer is noted in the developed world. This paper aims todevelop a novel model that diagnoses Breast Cancer by using heterogeneous datasets. The model can work as a strong decision support system to help doctors tomake the right decision in diagnosing breast cancer patients. The proposed modelis based on three datasets to develop three sub-models. Each sub-model worksindependently. The final diagnosis decision is taken by the three sub-models independently. The power of the model comes from the diversity checks of patientsand this reduces the risk of wrong diagnosing. The model has been developedby conducting intensive experiments. Several classification algorithms were usedto select the best one in each sub-model. As the final results, the sub-modelaccuracies were 72%, 74% and 97%.

关键词： Breast cancer data mining classification

Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

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Computers, Materials & Continua 2022年第12期73卷 6425-6441页

作者： Hadil Shaiba Jaber S.Alzahrani Majdy M.eltahir Radwa Marzouk Heba Mohsen Manar ahmed Hamza Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi 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

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,*** general,these kinds of cyberattacks are made at users through phone calls,emails,or instant *** anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage *** third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughMachine Learning(ML)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing *** tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification *** this background,the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification(HSOHDL-PDC)*** presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website *** addition,SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no ***,a hybrid Convolutional Neural Network-Long Short Term Memory(HCNN-LSTM)technique is also applied for identification and classification of *** hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved *** performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

关键词： Uniform resource locators phishing cyberattacks machine learning deep learning hyperparameter optimization

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

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Computers, Materials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Anwer Mustafa Hilal Majdy M.eltahir Manar ahmed Hamza Abu Sarwar Zamani Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and *** recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,*** the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop ***,earlier and precise apple leaf disease detection can minimize the spread of the *** works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf *** this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision *** proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal *** addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature ***,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet ***,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf *** design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the *** range of experiments was performed to showcase the supremacy of the AIE-ALDC *** experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augmentation

Novel Image Encryption and Compression Scheme for IoT Environment

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Computers, Materials & Continua 2022年第4期71卷 1443-1457页

作者： Mesfer Al Duhayyim Fahd N.Al-Wesabi Radwa Marzouk Manar ahmed Hamza Anwer Mustafa Hilal Majdy M.eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Latest advancements made in the processing abilities of smartdevices have resulted in the designing of Intelligent Internet of Things (IoT)environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless Multimedia Surveillance Networks (WMSNs) form a vital part in IoT-assistedenvironment since it contains visual sensors that examine the surroundingsfrom a number of overlapping views by capturing the images *** IoT devices generate a massive quantity of digital media, it is thereforerequired to save the media, especially images, in a secure way. In order toachieve security, encryption techniques as well as compression techniques areemployed to reduce the amount of digital data, being communicated overthe network. Encryption Then Compression (ETC) techniques pave a wayfor secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presentsa new ETC technique to accomplish image security in IoT *** proposed model involves three major processes namely, IoT-based imageacquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NMWOA)abbreviated as ST-NMWOA. The optimal key generation of signcryptiontechnique takes place with the help of NMWOA. Besides, the presented modelalso uses Discrete Fourier Transform (DFT) and Matrix Minimization (MM)algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed *** obtained values infer that the presented model is superior in terms of bothcompression efficiency and data secrecy in resource-limited IoT environment.

关键词： Data compression image security encryption signcryption optimal key generation

Optimal Hybrid Feature Extraction with Deep Learning for COVID-19 Classifications

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Computers, Materials & Continua 2022年第6期71卷 6257-6273页

作者： Majdy M.eltahir Ibrahim Abunadi Fahd NAl-Wesabi Anwer Mustafa Hilal Adil Yousif Abdelwahed Motwakel Mesfer Al Duhayyim Manar ahmed Hamza Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Novel coronavirus 2019(COVID-19)has affected the people’s health,their lifestyle and economical status across the *** application of advanced Artificial Intelligence(AI)methods in combination with radiological imaging is useful in accurate detection of the *** also assists the physicians to take care of remote villages *** current research paper proposes a novel automatedCOVID-19 analysismethod with the help ofOptimal Hybrid Feature Extraction(OHFE)and Optimal Deep Neural Network(ODNN)called OHFE-ODNN from chest x-ray *** objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray *** presented OHFE-ODNN method includes a sequence of procedures such as Median Filtering(MF)-based pre-processed,feature extraction and finally,binary(COVID/Non-COVID)and multiclass(Normal,COVID,SARS)***,in OHFE-based feature extraction,Gray Level Co-occurrence Matrix(GLCM)and Histogram of Gradients(HOG)are integrated *** presented OHFE-ODNN model includes Squirrel Search Algorithm(SSA)for finetuning the parameters of *** performance of the presented OHFEODNN technique is conducted using chest x-rays *** presented OHFE-ODNN method classified the binary classes effectively with a maximumprecision of 95.82%,accuracy of 94.01%and F-score of 96.61%.Besides,multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%,accuracy of 95.60%and an F-score of 95.73%.

关键词： COVID-19 classification deep learning radiological images

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

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

Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

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Computer Systems Science & Engineering 2023年第4期45卷 955-969页

作者： Anwer Mustafa Hilal Jaber S.Alzahrani Dalia H.Elkamchouchi Majdy M.eltahir ahmed S.Almasoud Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia

Recently,unmanned aerial vehicles(UAV)or drones are widely employed for several application areas such as surveillance,disaster management,*** UAVs are limited to energy,efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station(BS).Therefore,clustering can be employed as an effective way of accomplishing smart communication systems among multiple *** this aspect,this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system(GTOADL-SCS)technique for UAV *** proposed GTOADL-SCS model encompasses a two stage process namely clustering and *** the initial stage,the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads(CHs)and organize ***,the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs,average neighoring distance,and UAV *** classification process,the GTOADLSCS model applies pre-trained densely connected network(DenseNet201)feature extractor with gated recurrent unit(GRU)*** ensuring the enhanced performance of the GTOADL-SCS model,a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio(PDR)of 92.60%.

关键词： Unmanned aerial vehicles energy efficiency smart communication system deep learning