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CryptoNight Mining Algorithm with YAC Consensus for Social Media Marketing Using Blockchain

Computers, Materials & Continua 2022年第5期71卷 3921-3936页

作者： Anwer mustafa Hil Fahd N.Al-Wesabi Hadeel Alsolai Ola Abdelgney Omer Ali Nadhem Nemri Manar Ahmed Hamza abu Sarwar Zamani Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia&Faculty of Computer and ITSana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

Social media is a platform in which user can create,share and exchange the knowledge/*** media marketing is to identify the different consumer’s demands and engages them to create marketing *** popular social media platforms are Microsoft,Snapchat,Amazon,Flipkart,Google,eBay,Instagram,Facebook,Pin interest,and *** main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their *** technology is replacing old approaches in the social media marketing to new technology-based ***,this disruptive technology creates some issues like fake news,insecure,inconsistency,inaccuracy and so *** issues contribute economic instability in the society,diminishing the level of *** overcome these issues,this paper we present blockchain as disruptive technology for social media *** plays a vital role on social media marketing by providing secure to the company page in the *** properties of disruptive potential of blockchain on social media marketing is transparency,security,reliability and *** paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm[BCDSMM-CNYAC].This mining algorithm provides high CPU efficiency,high dimensionality of secure and detecting falsifying data attack in the social media *** the data analysis we proposed ANOVA analysis method regarding to the factors of age,time,frequency visiting times of social media *** reliability analysis of data Cronbach’s alpha tests are implemented.

关键词： Cryptonight disruptive technology smart contracts social media security YAC

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Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

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Computers, Materials & Continua 2022年第5期71卷 3853-3867页

作者： Anwer mustafa Hilal Imène ISSAOUI Marwa Obayya Fahd N.Al-Wesabi Nadhem NEMRI Manar Ahmed Hamza Mesfer Al Duhayyim abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharj16278Saudi Arabia Department of Natural and Applied Sciences Community CollegeQassim UniversityBuraydahSaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a31220Yemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence(XAI),a process that explains how prediction is done in AI *** mental disorder,i.e.,Autism Spectral Disorder(ASD)needs to be identified and classified at early stage itself in order to reduce health *** this background,the current paper presents XAI-based ASD diagnosis(XAI-ASD)model to detect and classify ASD *** proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization(BFO)-based Feature Selection(FS)*** addition,Whale Optimization Algorithm(WOA)with Deep Belief Network(DBN)model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of *** order to ensure a better ASD diagnostic outcome,a series of simulation process was conducted on ASD dataset.

关键词： Explainable artificial intelligence autism spectral disorder feature selection data classification machine learning metaheuristics

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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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Systemic inflammatory markers in elderly patients undergoing transcatheter aortic valve replacement

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Journal of Geriatric Cardiology 2022年第11期19卷 811-821页

作者： Haitham abu Khadija Mechael Welt Gera Gendelman Ahamd Hamode Lion Poles Michael Jonas mustafa Jaber Gal Sella Omar Ayyad Sagi Tshori Alex Blatt Jacob George Kaplan Heart Center Kaplan Medical CenterRehovotaffiliated with the Hebrew UniversityJerusalemIsrael

BACKGROUND Transcatheter aortic valve replacement(TAVR)provokes an early injury response,partially represented by dynamic changes in inflammatory *** greatly benefits the elderly and we aimed to determine whether increased inflammatory markers post-TAVR in octagenerians were different than their younger counterparts and whether it was associated with adverse clinical *** Patients with severe symptomatic aortic stenosis who underwent transfemoral TAVR from January 2010 to December 2021 were *** white blood cells(WBC)count and subpopulation dynamics were *** Five-hundred and seven patients were finally included in the study,65%of these patients were 80 or more years old(54%female,median age 84[82-87])years,with severe symptomatic aortic *** patients aged above 80 years(patients≥80),we noticed significant kinetic changes in the WBC and their differential cellular subpopulations(P<0.0001)between admission and early days *** was evident by a significant increase in total WBC(median 7.1 to 9.4)and absolute neutrophil count(median 4.7 to 7.4),neutrophil-lymphocyte(NL)ratio(median 2.82 to 7.21),and a meaningful decrease in absolute lymphocyte count(median 1.5 to 1.0).Implantation of self-expandable valves(SEVs)was associated with a more pronounced inflammatory response than balloon-expandable valves(BEVs).Higher WBC and neutrophil counts were associated with higher mortality and major vascular complications at 30 days,in addition,higher neutrophil counts and NL ratios were found to be correlated to arrhythmia at 30 days with P values of 0.04 and 0.028,*** This is the first description of a differential age-related inflammatory response in patients after TAVR,which shows an association between inflammatory markers post procedure and clinical ***,survival rates were similar in the elderly population and in younger patients,despite the presence of comorbid conditions.

关键词： patients younger absolute

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Malware Detection Using Decision Tree Based SVM Classifier for IoT

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Computers, Materials & Continua 2022年第7期72卷 713-726页

作者： Anwer mustafa Hilal Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Nadhem Nemri Mohamed K.Nour Abdelwahed Motwakel abu Sarwar Zamani Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The development in Information and Communication Technology has led to the evolution of new computing and communication *** revolution with Internet of Things(IoTs)has developed various applications in almost all domains from health care,education to entertainment with sensors and smart *** of the subsets of IoT is Internet of Medical things(IoMT)which connects medical devices,hardware and software applications through *** enables secure wireless communication over the Internet to allow efficient analysis of medical *** these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical *** work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in *** proposed Machine Learning based Block Chain Technology malware detection scheme(MLBCT-Mdetect)is implemented in three steps namely:feature extraction,Classification and *** extraction is performed by calculating the weight of each feature and reduces the features with less *** Vector Machine classifier is employed in the second step to classify the malware and benign ***,third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and ***-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.

关键词： Blockchain malware detection classification feature selection internet of medical things

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Manta Ray Foraging Optimization with Machine Learning Based Biomedical Data Classification

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Computers, Materials & Continua 2022年第11期73卷 3275-3290页

作者： Amal Al-Rasheed Jaber S.Alzahrani Majdy M.Eltahir Abdullah Mohamed Anwer mustafa Hilal Abdelwahed Motwakel abu Sarwar Zamani Mohamed I.Eldesouki Department of Information Systems 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 UniversityAbha62529Saudi Arabia Research Centre Future 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

The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various *** developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification ***,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data *** presented OSAE-LSTM model intends to classify the biomedical data for the existence of ***,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform *** by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical *** last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization *** utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM *** simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.

关键词： Biomedical data classification deep learning manta ray foraging optimization healthcare machine learning artificial intelligence

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Tritrophic interactions between parasitoids and cereal aphids are mediated by nitrogen fertilizer

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Insect Science 2015年第6期22卷 813-820页

作者： Muhammad A. Aqueel abu-bakar M. Raza Rashad M. Balal Muhammad A. Shahid Irfan mustafa Muhammad M. Javaid simon R. Leather Division of Biology Imperial College London Silwood Park Campus Ascot SL5 7PY UK University College of Agriculture Department of Biological Sciences University of Sargodha Pakistan Department of Crop and Environment Sciences Harper Adams UniversityShropshire TFIO 8NB UK

Host plant nutritional quality can directly and indirectly affect the third trophic levels. The aphid-parasitoid relationship provides an ideal system to investigate tritrophic interactions （as the parasitoids are completely dependent for their development upon their hosts） and assess the bottom up forces operating at different concentrations of nitrogen applications. The effects of varying nitrogen fertilizer on the performance of Aphidius colemani （V.） reared on Sitobion avenae （E） and Aphidius rhopalosiphi （D.） reared on Rhopalosiphum padi （L.） were measured. Parasitism and percent emergence ofparasitoids were positively affected by nitrogen fertilizer treatments while developmental duration （egg, larval, and pupal stages） was not affected by increasing nitrogen inputs. In males and females of both parasitoid species, adult longevity increased with the increasing nitrogen fertilizer. Hind tibia length and mummy weight of both parasitoid species increased with nitrogen fertilizer concentrations, as a result of larger aphids. This study showed that nitrogen application to the soil can have important consequences for aboveground multitrophic interactions.

关键词： cereal aphid parasitoids nitrogen fertilization tritrophic interactions

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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

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Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

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Computers, Materials & Continua 2022年第7期72卷 181-195页

作者： Mesfer Al Duhayyim Majdy M.Eltahir Imène Issaoui Fahd N.Al-Wesabi Anwer mustafa Hilal Fuad Ali Mohammed Al-Yarimi Manar Ahmed Hamza abu Sarwar Zamani Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences Community CollegeQassim UniversityAl-Bukairiyah52571Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

The grid environment is a dynamic,heterogeneous,and changeable computing system that distributes various services amongst different *** attain the benefits of collaborative resource sharing in Grid computing,a novel and proficient grid resource management system(RMS)is ***,detection of an appropriate resource for the presented task is a difficult *** scientists have presented algorithms for mapping tasks to the *** of them focus on fault tolerance,user fulfillment,and load *** this motivation,this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm(DHOA),called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid *** IGSS-DHOA technique is mainly based on the hunting nature of humans toward *** also derives an objective function with candidate solution(schedule)as input and the outcome is the makespan value denoting the quality of the candidate *** simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.

关键词： Grid services grid scheduling resources makespan np hard problem metaheuristics

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Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

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Computers, Materials & Continua 2022年第7期72卷 109-124页

作者： Khalil Ibrahim Mohammad abuzanouneh Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Mesfer Al Duhayyim M.Al-Shabi Anwer mustafa Hilal Manar Ahmed Hamza abu Sarwar Zamani K.Muthulakshmi Department of Information Technology College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a61101Yemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Management Information System College of Business and AdministrativeTaibah UniversityMedina42353Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electronics and Communication Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India

Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable *** use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision *** achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be *** this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision *** proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for *** proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and ***,the sensed data are transmitted to the cloud server for processing and decision ***,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for ***,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly *** performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

关键词： Automatic irrigation precision agriculture smart farming machine learning cloud computing decision making internet of things

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