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Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms

Geoscience Frontiers 2021年第5期12卷 81-92页

作者： Song-Shun Lin Shui-Long Shen Ning Zhang Annan Zhou Department of Civil Engineering School of Naval ArchitectureOceanand Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China MOE Key Laboratory of intelligent Manufacturing Technology College of EngineeringShantou UniversityShantouGuangdong 515063China Discipline of Civil and Infrastructure School of EngineeringRoyal Melbourne Institute of Technology(RMIT)Victoria 3001Australia Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure Shanghai Jiao Tong UniversityShanghai 200240China

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield *** artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are *** geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent *** prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>***,the Pearson correlation coefficient(PCC)is adopted for sensitivity *** results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field ***,the prediction performances of the intelligent models and the empirical method are *** results reveal that all the intelligent models perform better than the empirical method.

关键词： EPB shield machine Advancing speed prediction intelligent models Empirical analysis Tunnel excavation

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Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

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Computer Systems Science & Engineering 2023年第2期44卷 1419-1432页

作者： Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Majed Alsanea Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom 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

Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the *** an auto-mated soil prediction tool helps to considerably improve the efﬁcacy of the *** the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive *** other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering *** this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classiﬁca-tion(FCMCSO-ASC)*** goal of the FCMCSO-ASC technique is to identify and categorize seven different types of *** accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature *** addition,the FCMCSO model is applied for soil classiﬁcation and the weight values of the FCM model are optimally adjusted by the use of CSO *** exam-ining the enhanced soil classiﬁcation outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

关键词： Soil classiﬁcation intelligent models fuzzy cognitive maps cat swarm optimization fuzzy logic

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An intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

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Computers, Materials & Continua 2023年第2期74卷 3315-3332页

作者： Mesfer Al Duhayyim Saud S.Alotaibi Shaha Al-Otaibi Fahd N.Al-Wesabi Mahmoud Othman Ishfaq Yaseen Mohammed Rizwanullah Abdelwahed Motwakel Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and Arts at MuhayelKing Khalid UniversityMuhayel AseerSaudi 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

Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing ***,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management *** study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human *** goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous *** HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and *** addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 ***,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.

关键词： Hazardous waste image classification ensemble learning deep learning intelligent models human health weighted voting model

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intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

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Computers, Materials & Continua 2022年第4期71卷 1189-1203页

作者： Wafaa Alsaggaf Felwa Abukhodair Amani Tariq Jamal Sayed Abdel-Khalek Romany F.Mansour Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics and Statistics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly *** use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud ***,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT *** the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision *** this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC *** proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease *** addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic *** COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN *** order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI *** experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.

关键词： intelligent models healthcare systems disease diagnosis internet of things cloud computing clustering deep learning

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intelligent solubility estimation of gaseous hydrocarbons in ionic liquids

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Petroleum 2024年第1期10卷 109-123页

作者： Behnaz Basirat Fariborz Shaahmadi Seyed Sorosh Mirfasihi Abolfazl Jomekian Bahamin Bazooyar Ahvaz Faculty of Petroleum Engineering Petroleum University of Technology(PUT)Ahvaz***Iran School of Engineering&Physical Sciences Heriot Watt UniversityEdinburgh EH144ASUK Department of Fluids&Environment The University of ManchesterManchesterM139PLUK Department of Engineering Chemical Engineering GroupEsfarayen University of TechnologyIran Mechanical and Aerospace Engineering Brunel University LondonUxbridge UB83PHUK

The research focuses on evaluating how well new solvents attract light hydrocarbons,such as propane,methane,and ethane,in natural gas sweetening *** is important to accurately determine the solubility of hydrocarbons in these solvents to effectively manage the sweetening *** address this challenge,the study proposes using advanced empirical models based on artificial intelligence techniques like Multi-Layer Artificial Neural Network(ML-ANN),Support Vector Machines(SVM),and Least Square Support Vector Machine(LSSVM).The parameters for the SVM and LSSVM models are estimated using optimization methods like Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Shuffled Complex Evolution(SCE).Data on the solubility of propane,methane,and ethane in various ionic liquids are collected from reliable literature sources to create a comprehensive *** proposed artificial intelligence models show great accuracy in predicting hydrocarbon solubility in ionic *** these,the hybrid SVM models perform exceptionally well,with the PSO-SVM hybrid model being particularly efficient *** ensure a comprehensive analysis,different examples of hydrocarbons and their order are ***,a comparative analysis is conducted to compare the AI models with the thermodynamic COSMO-RS model for solubility *** results demonstrate the superiority of the AI models,as they outperform traditional thermodynamic models across a wide range of *** conclusion,this study introduces advanced artificial intelligence algorithms such as ML-ANN,SVM,and LSSVM in accurately estimating the solubility of hydrocarbons in ionic *** incorporation of optimization techniques and variations in hydrocarbon examples improves the accuracy,precision,and reliability of these intelligent *** findings highlight the significant potential of AI-based approaches in solubility analysis and emphasize their superiority over traditional thermodynami

关键词： Solubility Gaseous hydrocarbon intelligent models Ionic liquids

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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Computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： intelligent models skin lesion dermoscopic images smart healthcare internet of things

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intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

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Computers, Materials & Continua 2022年第12期73卷 5543-5557页

作者： Thavavel Vaiyapuri S.Srinivasan Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Applied Data Science Noroff University CollegeKristiansand4608Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillahBabil51001Iraq Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

In past decades,retinal diseases have become more common and affect people of all age grounds over the *** examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated *** analyze the retinal malady,the system proposes a multiclass and multi-label arrangement ***,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive ***,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)*** this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus *** proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological ***,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly *** addition,SqueezeNet based feature extractor is employed to generate a collection of feature ***,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal *** efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different *** experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

关键词： Multi-retinal disease computer aided diagnosis fundus images deep learning segmentation intelligent models

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Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

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Computer Systems Science & Engineering 2023年第9期46卷 3749-3765页

作者： Iyad Katib Mahmoud Ragab Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The concept of smart healthcare has seen a gradual increase with the expansion of information *** healthcare will use a new generation of information technologies,like artificial intelligence,the Internet of Things(IoT),cloud computing,and big data,to transformthe conventional medical system in an all-around way,making healthcare highly effective,more personalized,and more *** work designs a new Heap Based Optimization with Deep Quantum Neural Network(HBO-DQNN)model for decision-making in smart healthcare *** presented HBO-DQNN modelmajorly focuses on identifying and classifying healthcare *** the presented HBO-DQNN model,three stages of operations were *** normalization is applied to pre-process the input data at the initial ***,the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare *** last,the DQNN model is exploited for healthcare data classification.A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN *** extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05%and 95.72%under the colon cancer and lymphoma dataset.

关键词： Heap-based optimization smart healthcare decision making intelligent models artificial intelligence

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