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Business Blockchain Suitability Determinants: Decision-Making through an Intuitionistic Fuzzy Method

Computer Systems Science & Engineering 2023年第11期47卷 1665-1690页

作者： Tomader Almeshal Jawad Berri Tarifa Almulhim areej alhogail Emam Ahmed Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Department of Quantitative Methods School of BusinessKing Faisal UniversityAl-Ahsa31982Saudi Arabia Faculty of Computer Science and Engineering King Salman International UniversitySouth SinaiEgypt

Blockchain is one of the innovative and disruptive technologies that has a wide range of applications in multiple industries beyond *** widespread adoption of blockchain technology in various industries has shown its potential to solve challenging business problems,as well as the possibility to create new business models which can increase a firm’s *** to the novelty of the technology,whereby many companies are still exploring potential use cases,and considering the complexity of blockchain technology,which may require huge changes to a company’s existing systems and processes,it is important for companies to carefully evaluate suitable use cases and determine if blockchain technology is the best solution for their specific *** research aims to provide an evaluation framework that determines the important dimensions of blockchain suitability assessment by identifying the key determinants of suitable use cases in a business *** this paper,a novel approach that utilizes both qualitative(Delphi method)and quantitative(fuzzy set theory)methods has been proposed to objectively account for the uncertainty associated with data collection and the vagueness of subjective *** work started by scanning available literature to identify major suitability dimensions and collected a range of criteria,indicators,and factors that had been previously identified for related *** opinions were then gathered using a questionnaire to rank the importance and relevance of these elements to suitability ***,the data were analyzed and we proceeded to integrate multi-criteria group decision-making(MCGDM)and intuitionistic fuzzy set(IFS)*** findings demonstrated a high level of agreement among experts,with the model being extremely sensitive to variances in expert ***,the results helped to refine and select the most relevant suitability determinants under three important dimensions:fu

关键词： Blockchain business blockchain use cases suitability assessment multi-criteria group decision-making fuzzy Delphi intuitionistic fuzzy set

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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Computer Systems Science & Engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari areej alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi 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

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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Fuzzy Logic Inference System for Managing Intensive Care Unit Resources Based on Knowledge Graph

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Computers, Materials & Continua 2023年第12期77卷 3801-3816页

作者： Ahmad F Subahi areej Athama Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare *** systems can manage and manipulate this massive amount of health-related data and encourage different decision-making *** can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality *** intensive care unit(ICU)is one of the most important hospital ***,there are limited rooms and resources in most *** times of seasonal diseases and pandemics,ICUs face high admission *** line with this increasing number of admissions,determining health risk levels has become an essential and imperative *** creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of ***,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related *** system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to *** proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.

关键词： Fuzzy logic role-based expert system decision-support system knowledge graph Internet of Things ICU resource management Neo4J graph database

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A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

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Computer Systems Science & Engineering 2023年第1期44卷 895-912页

作者： Sara A.Alameen areej M.Alhothali Department of Computer Science King Abdulaziz UniversityJeddah21589Kingdom of Saudi Arabia

Today,fatalities,physical injuries,and significant economic losses occur due to car *** the leading causes of car accidents is drowsiness behind the wheel,which can affect any *** and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential *** paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from *** model depends on integrating a 3D convolutional neural network(3D-CNN)and long short-term memory(LSTM).The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent *** learned features are then used as the input of the LSTM component for modeling high-level temporal *** addition,we investigate how the training of the proposed model can be affected by changing the position of the batch normalization(BN)layers in the 3D-CNN *** BN layer is examined in two different placement settings:before the non-linear activation function and after the non-linear activation *** study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD.3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the *** show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the *** a result,the model achieves a test detection accuracy of 96%,93%,and 90%on YawDD,Side-3MDAD,and Front-3MDAD,respectively.

关键词： 3D-CNN deep learning driver drowsiness detection LSTM spatiotemporal features

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Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

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Computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring System

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Computers, Materials & Continua 2022年第3期70卷 6141-6158页

作者： Bander Alzahrani Ahmed Barnawi Azeem Irshad areej Alhothali Reem Alotaibi Muhammad Shafiq Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Software Engineering International Islamic University IslamabadPakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial *** example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real ***,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management *** addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication ***,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD ***,the AKA scheme involves many vulnerabilities in terms of security and *** this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling *** scheme supports key security features,including anti-forgery attacks,and confirms user *** security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle *** simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.

关键词： IoT unmanned aerial vehicles authentication crowd monitoring

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Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification

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Computers, Materials & Continua 2022年第8期72卷 2859-2875页

作者： areej A.Malibari Siwar Ben Haj Hassine Abdelwahed Motwakel Manar Ahmed Hamza Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayilAsirSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Atherosclerosis diagnosis is an inarticulate and complicated cognitive *** on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical *** the medical diagnostic outcomes need to be prompt and accurate,the recently developed artificial intelligence(AI)and deep learning(DL)models have received considerable attention among research *** study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification(MDL-BADDC)*** proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing,feature selection,classification,and parameter ***,the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer(QOBMO)based feature selection ***,the deep stacked autoencoder(DSAE)based classification model is designed for the detection and classification of atherosclerosis ***,the krill herd algorithm(KHA)based parameter tuning technique is applied to properly adjust the parameter *** order to showcase the enhanced classification performance of the MDL-BADDC technique,a wide range of simulations take place on three benchmarks biomedical *** comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.

关键词： Atherosclerosis disease biomedical data data classification machine learning disease diagnosis deep learning

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Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

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Computers, Materials & Continua 2021年第7期68卷 1341-1357页

作者： Shabib Aftab Saad Alanazi Munir Ahmad Muhammad Adnan Khan areej Fatima Nouh Sabri Elmitwally School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Riphah School of Computing&Innovation Riphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of Computers and Articial IntelligenceCairo University12613Egypt

Diabetes mellitus,generally known as diabetes,is one of the most common diseases *** is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin *** is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/*** is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood *** and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of *** primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing ***,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of ***,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare ***,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

关键词： Machine learning fusion articial neural network decision trees naïve Bayes diabetes prediction

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Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

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Computers, Materials & Continua 2023年第7期76卷 895-914页

作者： Shah Faisal Kashif Javed Sara Ali areej Alasiry Mehrez Marzougui Muhammad Attique Khan Jae-Hyuk Cha Department of Robotics and Artificial Intelligence SMME NUSTIslamabadPakistan College of Computer Science King Khalid UniversityAbha61413Saudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality *** vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural *** paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational *** most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification *** transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant *** evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,*** identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.

关键词： Citrus diseases classification deep learning transfer learning efficientNetB3 mobileNetV2 ResNet50 InceptionV3

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Hydrogeochemical Characteristics of Groundwater in the Bassit Ophiolitic Area, Northwestern Syria

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Journal of Geoscience and Environment Protection 2022年第12期10卷 373-392页

作者： areej Adra Department of Environmental Chemistry Higher Institute for Environmental Research Tishreen University Latakia Syria

In order to evaluate the hydrogeochemical properties and the quality of groundwater in the Bassit ophiolitic area (Northwestern Syria), 27 groundwater samples were collected from springs and wells during dry and wet seasons. Physical-chemical parameters and heavy metal concentrations of groundwater samples were measured. The analytical results showed that the groundwater is very hard and slightly alkaline in nature. The concentrations of major ions are in the following order: Mg2+ > Ca2+ > Na+ > K+ and HCO32- > Cl- > SO42- > NO3-. The magnesium ion (Mg2+) has exceeded the permissible limit for drinking purpose, reflecting an influence of ophiolitic rocks on the local groundwater chemistry. The hydrogeochemical facies are mainly (Mg-HCO3) and (Mg/Ca-HCO3) types with a minor occurrence of Ca-HCO3 type. According to Gibbs diagrams, all samples fall in the rock dominance field and the chemical quality of groundwater is related to the lithology of the area. The maximum concentrations of some heavy metals in groundwater samples for two seasons are relatively low (16.9 μg/l Cr, 19.27 μg/l Ni, and 1.78 μg/l Co), which are under the permissible limit for a drinking purpose. Also to evaluate the groundwater suitability for irrigation purposes, Salinity hazard (EC), Sodium hazard (SAR, Na%) and magnesium hazard (MAR) were appraised. It is found that 89% of groundwater samples in two seasons were mainly classified as high salinity/low sodium, which suggests that this groundwater is unsuitable for irrigation. Only (11%) of samples were classified as medium salinity/low sodium and as such suitable for irrigation. The magnesium hazard showed that 82% of the samples had high values (MAR > 50%) and as such unsuitable for irrigation. Soil irrigated with such groundwater will not be exposed to any alkali hazard, but will s

关键词： Hydrogeochemistry Groundwater Quality Heavy Metals Ophiolitic Area

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