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Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Computers, Materials & Continua 2023年第2期74卷 2725-2738页

作者： Mohammad Alamgeer Amal Al-Rasheed Ahmad Alhindi Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemMecca24382Saudi Arabia 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

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data *** same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall *** this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)*** proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental ***,data pre-processing and correlation matrix(CM)based feature selection processes are carried *** addition,deep belief network(DBN)model is applied for the effective prediction of rainfall ***,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO ***,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN *** demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct *** simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

关键词： Data mining rainfall prediction deep learning correlation matrix hyperparameter tuning metaheuristics

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Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

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Computers, Materials & Continua 2022年第11期73卷 4277-4290页

作者： Areej A.Malibari Reem M.Alshehri Fahd N.Al-Wesabi Noha Negm Mesfer Al Duhayyim Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In bioinformatics applications,examination of microarray data has received significant interest to diagnose *** gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of *** data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern *** paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics *** proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform ***,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical ***,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)*** utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification *** examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark *** extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

关键词： Bioinformatics data science microarray gene expression data classification deep learning metaheuristics

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Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

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Computers, Materials & Continua 2022年第8期72卷 3799-3813页

作者： Areej A.Malibari Reem Alshahrani Fahd N.Al-Wesabi Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini Anwer Mustafa Hilal Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of *** resonance imaging(MRI)is a widely utilized tool for the classification and detection of prostate *** the manual screening process of prostate cancer is difficult,automated diagnostic methods become *** study develops a novel Deep Learning based Prostate Cancer Classification(DTL-PSCC)model using MRI *** presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature *** addition,the fuzzy k-nearest neighbour(FKNN)model is utilized for classification process where the class labels are allotted to the input MRI ***,the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm(KHA)which results in improved classification *** order to demonstrate the good classification outcome of the DTL-PSCC technique,a wide range of simulations take place on benchmark MRI *** extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

关键词： MRI images prostate cancer deep learning medical image processing metaheuristics krill herd algorithm

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Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

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Computer Systems Science & Engineering 2023年第4期45卷 939-953页

作者： Sindhuja Manickavasagam Poonkuzhali Sugumaran Department of Information Technology Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia

The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of *** efforts are used by researchers for detecting and providing primary phase insights as to cancer *** cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the *** disease is the unrestrained progress of irregular cells which begin off in one or both *** previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging *** study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate *** proposed model aims to identify and classify the presence of lung ***,the proposed model undergoes min-max data normalization approach to preprocess the input ***,the ALO algorithm gets executed to choose an optimal subset of *** addition,the DBN model receives the chosen features and performs lung cancer ***,the optimizer is utilized for hyperparameter optimization of the DBN *** order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

关键词： Lung cancer feature selection ant lion optimization classification disease diagnosis metaheuristics

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Metaheuristic Based Resource Scheduling Technique for Distributed Robotic Control Systems

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Computer Systems Science & Engineering 2022年第8期42卷 795-811页

作者： P.Anandraj S.Ramabalan Department of Computer Science and Engineering E.G.S.Pillay Engineering CollegeNagapattinam611002India Department of Mechanical Engineering E.G.S.Pillay Engineering CollegeNagapattinam611002India

The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function *** distributed robotic control system can be used in real time for resolving various challenges such as localization,motion controlling,mapping,route planning,*** distributed robotic control system can manage different kinds of heterogenous *** a distributed robotic control system is a challenging process as it needs to operate effectually under different hardware configurations and varying computational *** instance,scheduling of resources(such as communication channel,computation unit,robot chassis,or sensor input)to the various system components turns out to be an essential requirement for completing the tasks on ***,resource scheduling is necessary for ensuring effective *** this regard,this paper introduces a novel chaotic shell game optimization algorithm(CSGOA)for resource scheduling,known as the CSGOA-RS technique for the distributed robotic control system *** CSGOA technique is based on the integration of the chaotic maps concept to the SGO algorithm for enhancing the overall *** CSGOA-RS technique is designed for allocating the resources in such a way that the transfer time is minimized and the resource utilization is *** CSGOA-RS technique is applicable even for the unpredicted environment where the resources are to be allotted dynamically based on the early *** validating the enhanced performance of the CSGOA-RS technique,a series of simulations have been carried out and the obtained results have been examined with respect to a selected set of *** resultant outcomes highlighted the promising performance of the CSGOA-RS technique over the other resource scheduling techniques.

关键词： Distributed robotic control system resource scheduling load balancing resource utilization metaheuristics shell game optimization

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Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

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Computers, Materials & Continua 2022年第10期73卷 327-342页

作者： Hanan Abdullah Mengash Radwa Marzouk Siwar Ben Haj Hassine Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial intelligence(AI)techniques have received significant attention among research communities in the field of networking,image processing,natural language processing,robotics,*** the same time,a major problem in wireless sensor networks(WSN)is node localization,which aims to identify the exact position of the sensor nodes(SN)using the known position of several anchor *** comprises a massive number of SNs and records the position of the nodes,which becomes a tedious ***,the SNs might be subjected to node mobility and the position alters with ***,a precise node localization(NL)manner is required for determining the location of the *** this view,this paper presents a new quantum bird migration optimizer-based NL(QBMA-NL)technique for *** goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor *** QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating *** addition,an objective function is derived based on the received signal strength indicator(RSSI)and Euclidean distance from the known to unknown *** demonstrating the improved performance of the QBMA-NL technique,a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.

关键词： Artificial intelligence wireless communication wireless sensor networks metaheuristics quantum computing node localization

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Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2395-2409页

作者： Ashit Kumar Dutta Basit Qureshi Yasser Albagory Majed Alsanea Manal Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake *** recently developed machine learning(ML)models can be employed for the detection and classification of fake *** study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and *** goal of the CAS-WELM technique is to discriminate news into fake and *** CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding ***,N-gram based feature extraction technique is derived to gen-erate feature ***,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS *** performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several *** experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

关键词： Cybersecurity cybercrime fake news data classification machine learning metaheuristics

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IRKO:An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

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Computers, Materials & Continua 2022年第3期70卷 4803-4827页

作者： R.Manjula Devi M.Premkumar Pradeep Jangir Mohamed Abdelghany Elkotb Rajvikram Madurai Elavarasan Kottakkaran Sooppy Nisar Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Electrical&Electronics Engineering Dayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Rajasthan Rajya Vidyut Prasaran Nigam Sikar332025RajasthanIndia Mechanical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Mechanical Engineering Department Faculty of EngineeringKafrelsheikh UniversityEgypt Clean and Resilient Energy Systems(CARES)Laboratory Texas A&MUniversityGalveston77553TXUSA College of Arts and Sciences Prince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost,gains,energy,mass,and so *** order to solve optimization problems,metaheuristic algorithms are *** of these techniques are influenced by collective knowledge and natural *** is no such thing as the best or worst algorithm;instead,there are more effective algorithms for certain ***,in this paper,a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization(RKO)algorithm,called Improved Runge-Kutta Optimization(IRKO)algorithm,is suggested for solving optimization *** IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO *** performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization *** outcomes of IRKO are compared with seven state-of-the-art algorithms,including the basic RKO *** to other algorithms,the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization *** runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems,including real-world optimization problems.

关键词： Engineering design global optimization local escaping operator metaheuristics Runge-Kutta optimization algorithm

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SCChOA:Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

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Computers, Materials & Continua 2023年第12期77卷 3057-3075页

作者： Shanshan Wang Quan Yuan Weiwei Tan Tengfei Yang Liang Zeng School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China Xiangyang Industrial Institute of Hubei University of Technology Xiangyang441100China

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification ***,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search ***,the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm(SCChOA)to address the feature selection *** this approach,firstly,a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm(SCA)and the Chimp Optimization Algorithm(ChOA),enabling a more effective search in the objective ***,an S-shaped transfer function is introduced to perform binary transformation on ***,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection *** evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are ***,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for *** results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection *** is capable of maximizing the reduction in the number of selected features while maintaining a high classification ***,the results of statistical tests also confirm the significant effectiveness of this method.

关键词： metaheuristics chimp optimization algorithm sine-cosine algorithm feature selection and classification

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Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

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Computers, Materials & Continua 2022年第9期72卷 4589-4601页

作者： Souad Larabi-Marie-Sainte Eatedal Alabdulkreem Mohammad Alamgeer Mohamed K Nour Anwer Mustafa Hilal Mesfer Al Duhayyim Abdelwahed Motwakel Ishfaq Yaseen Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Electroencephalography(EEG)eye state classification becomes an essential tool to identify the cognitive state of *** can be used in several fields such as motor imagery recognition,drug effect detection,emotion categorization,seizure detection,*** the latest advances in deep learning(DL)models,it is possible to design an accurate and prompt EEG EyeState classification *** this view,this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification(CBADL-BEESC)*** major intention of the CBADL-BEESC technique aims to categorize the presence of EEG *** CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature *** addition,extreme learning machine autoencoder(ELM-AE)model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using *** experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.

关键词： Biomedical signals EEG EyeState classification deep learning metaheuristics

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