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A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

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computers, Materials & Continua 2023年第5期75卷 4633-4648页

作者： Sameh Abd El-Ghany A.A.Abd El-Aziz Department of information Systems College of Computer and Information SciencesJouf UniversityKSA Department of information Systems Faculty of Computers and InformationMansoura UniversityEgypt Department of information Systems and Technology Faculty of Graduate Studies for Statistical ResearchCairo UniversityEgypt

Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart *** breakdown of coronary cardiovascular disease is one of the principal sources of death all over *** deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained *** heart diseases happen without apparent signs,high-level detection is *** paper proposed a robust and tuned random forest model using the randomized grid search technique to *** proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between ***,the healthcare industry has a lot of data but needs to gain more *** proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham *** datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection ***,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two ***,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)*** randomized grid search selected the best parameters and got the ideal CAD *** proposed framework was evaluated and compared with traditional *** proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

关键词： Coronary artery disease tuned random forest randomized grid search classifier

Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

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computers, Materials & Continua 2023年第5期75卷 3065-3089页

作者： Ali Arshad Muhammad Nadeem Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Zaid Alzaid Rana Alabdan Badr Almutairi Sultan Almotairi Department of computer Science National University of TechnologyIslamabadPakistan Department of Computing Abasyn UniversityIslamabadPakistan Department of computer Science and information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of computer Science Faculty of Computer and Information SystemsIslamic university of MadinahMedinah42351Saudi Arabia Department of information Systems Faculty of Computer and Information Sciences CollegeMajmaah University11952Saudi Arabia Department of information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah 11952Saudi Arabia Department of Natural and Applied sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an *** cryptography is the best way to transmit data in a secure and reliable *** researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data *** algorithm has some data security *** some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original *** this paper,various data security techniques are developed,which can be used to protect the data from attackers ***,a customized American Standard Code for information Interchange(ASCII)table is *** value of each Index is defined in a customized ASCII *** an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the *** that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original *** the number of cipher values is double the original data,the attacker tries to decrypt each *** of getting the original data,the attacker gets such data that has no relation to the original *** that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain *** boundaries of each Hill text work up to that *** techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorit

关键词： Cryptography symmetric cipher text encryption matrix cipher encoding decoding hill matrix 64-bit encryption

An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

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computers, Materials & Continua 2023年第3期74卷 5641-5661页

作者： Afia Zafar Muhammad Aamir Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi faculty of computer Science and information Technology Universiti Tun Hussein Onn MalaysiaMalaysia School of Electronics Computing and MathematicsUniversity of DerbyUK Department of computer Science National University of TechnologyIslamabadPakistan Department of information Technology College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of computer Science and information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia Department of information Systems Faculty of Computer and Information SciencesIslamic University of MadinahMadinah42351Saudi Arabia

In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solve image processing problems ***,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher *** technique is time consuming and requires a lot of work and domain *** a convolutional neural network architecture is a classic NP-hard optimization *** the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and *** approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random *** address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized *** study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN *** addition,different types and parameter ranges of existing genetic algorithms are *** study was conducted with various state-of-the-art methodologies and *** have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

关键词： Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization

Integrating a Market-Based Model in Trust-Based Service Systems

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Tsinghua Science and Technology 2013年第6期18卷 554-567页

作者： Suronapee Phoomvuthisarn Yan Liu Liming Zhu Ross Jeffery faculty of information sciences and Technology Mahanakorn University of Technology faculty of Engineering and computer Science Concordia University National ICT Australia and the School of computer Science and Engineering University of New South Wales

The reputation-based trust mechanism is a way to assess the trustworthiness of offered services, based on the feedback obtained from their users. In the absence of appropriate safeguards, service users can still manipulate this feedback. Auction mechanisms have already addressed the problem of manipulation by market- trading participants. When auction mechanisms are applied to trust systems, their interaction with the trust systems and associated overhead need to be quantitatively evaluated. This paper proposes two distributed architectures based on centralized and hybrid computing for integrating an auction mechanism with the trust systems. The empirical evaluation demonstrates how the architectures help to discourage users from giving untruthful feedback and reduce the overhead costs of the auction mechanisms.

关键词： trust systems software architecture economic mechanisms

Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network

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computers, Materials & Continua 2021年第7期68卷 117-135页

作者： Sameh Abd ElGhany Mai Ramadan Ibraheem Madallah Alruwaili Mohammed Elmogy Department of information Systems College of Computers and Information SciencesJouf UniversitySakakaSaudi Arabia Department of information Systems Faculty of Computers and InformationMansoura UniversityEgypt Department of information Technology Faculty of Computers and InformationKafrelsheikh UniversityEgypt Department of computer Engineering and Networks College of Computers and Information SciencesJouf UniversitySaudi Arabia Department of information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

With the massive success of deep networks,there have been signi-cant efforts to analyze cancer diseases,especially skin *** this purpose,this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion *** paper aims to develop and ne-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic ***-tuning is a powerful method to obtain enhanced classication results by the customized pre-trained ***,batch normalization,and hyperparameter optimization are performed for ne-tuning the proposed deep *** proposed ne-tuned ResNet50 model successfully classied 7-respective classes of dermoscopic lesions using the publicly available HAM10000 *** developed deep model was compared against two powerful models,i.e.,InceptionV3 and VGG16,using the Dice similarity coefcient(DSC)and the area under the curve(AUC).The evaluation results show that the proposed model achieved higher results than some recent and robust models.

关键词： Deep learning model multiclass diagnosis dermatoscopic images analysis ResNet50 network

Rough Sets Hybridization with Mayfly Optimization for Dimensionality Reduction

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computers, Materials & Continua 2022年第10期73卷 1087-1108页

作者： Ahmad Taher Azar Mustafa Samy Elgendy Mustafa Abdul Salam Khaled M.Fouad faculty of computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of computer and information sciences Prince Sultan UniversityRiyadh11586Saudi Arabia faculty of computer Studies Arab Open UniversityCairoEgypt faculty of information Technology and computer Science Nile UniversityShikh ZaidEgypt

Big data is a vast amount of structured and unstructured data that must be dealt with on a regular *** reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed *** tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning *** achieve dimensionality reduction for huge data sets,this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS.A novel hybrid strategy based on the Mayfly algorithm(MA)and the rough set(RS)is proposed in *** performance of the novel hybrid algorithm MA-RS is evaluated by solving six different data sets from the *** simulation results and comparison with common reduction methods demonstrate the proposed MARS algorithm’s capacity to handle a wide range of data ***,the rough set approach,as well as the hybrid optimization techniques PSO-RS and MARS,were applied to deal with the massive data ***-hybrid RS’s method beats other classic dimensionality reduction techniques,according to the experimental results and statistical testing studies.

关键词： Dimensionality reduction metaheuristics optimization algorithm mayfly particle swarm optimizer feature selection

Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer

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computers, Materials & Continua 2022年第5期71卷 4019-4031页

作者： Ahmed Elaraby Walid Hamdy Madallah Alruwaili Department of computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt Department of Math. &Computer Science Faculty of SciencePort Said UniversityEgypt College of computer and information sciences Jouf UniversitySakakaAljoufKingdom of Saudi Arabia

Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still *** losses owing to illnesses are a severe problem for both large farming structures and rural communities,motivating our *** of the large range of diseases,identifying and classifying diseases with human eyes is not only time-consuming and labor intensive,but also prone to being mistaken with a high error *** learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and *** proposed work describes a deep learning approach for detection plant ***,we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf *** our research,we focused on detecting plant diseases in five crops divided into 25 different types of classes(wheat,cotton,grape,corn,and cucumbers).In this task,we used a public image database of healthy and diseased plant leaves acquired under realistic *** our work,a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics(accuracy,specificity,Sensitivity,precision,and Fscore)of the tested deep learning networks achieves an accuracy of 98.83%,specificity of 98.56%,Sensitivity of 98.78%,precision of 98.67%,and F-score of 98.47%,demonstrating the feasibility of this approach.

关键词： Deep neural networks plant diseases detection classification AlexNet PSO

Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

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computers, Materials & Continua 2023年第2期74卷 3333-3350页

作者： Akram Ali Alhadad Omar Tarawneh Reham R.Mostafa Hazem M.El-Bakry information Systems Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt computer Science Department Ibb UniversityIbbYemen SE Department Faculty of Computer Sciences and InformaticsAmman Arab UniversityAmman11953Jordan

COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in *** the infected people is the most important factor in the fight against the *** gold-standard test to diagnose COVID-19 is polymerase chain reaction(PCR),but it takes 5–6 h and,in the early stages of infection,may produce false-negative *** Computed Tomography(CT)images to diagnose patients infected with COVID-19 has become an urgent *** this study,we propose a residual attention deep support vector data description SVDD(RADSVDD)approach to diagnose *** is a novel approach combining residual attention with deep support vector data description(DSVDD)to classify the CT *** the best of our knowledge,we are the first to combine residual attention with DSVDD in general,and specifically in the diagnosis of *** attention with DSVDD naively may cause model *** in the proposed RADSVDD guides the network during training and enables quick learning,residual connectivity prevents vanishing *** approach consists of three models,each model is devoted to recognizing one certain disease and classifying other diseases as *** models learn in an end-to-end *** proposed approach attained high performance in classifying CT images into intact,COVID-19,and non-COVID-19 *** evaluate the proposed approach,we created a dataset from published datasets and had it assessed by an experienced *** proposed approach achieved high performance,with the normal model attained sensitivity(0.96–0.98),specificity(0.97–0.99),F1-score(0.97–0.98),and area under the receiver operator curve(AUC)0.99;the COVID-19 model attained sensitivity(0.97–0.98),specificity(0.97–0.99),F1-score(0.97–0.99),and AUC 0.99;and the non-COVID pneumoniamodel attained sensitivity(0.97–1),specificity(0.98–0.99),F1-score(0.97–0.99),and AUC 0.99.

关键词： Deep learning deep SVDD residual attention anomaly detection COVID-19 Coronavirus

Hybrid Global Optimization Algorithm for Feature Selection

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computers, Materials & Continua 2023年第1期74卷 2021-2037页

作者： Ahmad Taher Azar Zafar Iqbal Khan Syed Umar Amin Khaled M.Fouad faculty of computers and Artificial Intelligence Benha UniversityBenha13511Egypt College of computer and information sciences Prince Sultan UniversityRiyadh11586Saudi Arabia faculty of information Technology and computer Science Nile UniversitySheikh ZaidEgypt

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization *** experimental study is performed in two stages to assess the proposed *** I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO *** based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)*** phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT *** phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical *** experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable *** from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

关键词： Particle swarm optimization(PSO) time-variant acceleration coefficients(TVAC) genetic algorithms differential evolution feature selection medical data