Modeling approaches to pressure balance dynamic system in shield tunneling

In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network(ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling(MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.

Intelligent Cost Modeling Based on Soft Computing for Avionics Systems

In parametric cost estimating, objections to using statistical Cost Estimating Relationships （CERs） and parametric models include problems of low statistical significance due to limited data points, biases in the underlying data, and lack of robustness. Soft Computing （SC） technologies are used for building intelligent cost models. The SC models are systemically evaluated based on their training and prediction of the historical cost data of airborne avionics systems. Results indicating the strengths and weakness of each model are presented. In general, the intelligent cost models have higher prediction precision, better data adaptability, and stronger self-learning capability than the regression CERs.

Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

The Extreme Machine Learning Actuarial Intelligent Agricultural Insurance Based Automated Underwriting Model

The paper presents an innovative approach towards agricultural insurance underwriting and risk pricing through the development of an Extreme Machine Learning (ELM) Actuarial Intelligent Model. This model integrates diverse datasets, including climate change scenarios, crop types, farm sizes, and various risk factors, to automate underwriting decisions and estimate loss reserves in agricultural insurance. The study conducts extensive exploratory data analysis, model building, feature engineering, and validation to demonstrate the effectiveness of the proposed approach. Additionally, the paper discusses the application of robust tests, stress tests, and scenario tests to assess the model’s resilience and adaptability to changing market conditions. Overall, the research contributes to advancing actuarial science in agricultural insurance by leveraging advanced machine learning techniques for enhanced risk management and decision-making.

The Actuarial Data Intelligent Based Artificial Neural Network (ANN) Automobile Insurance Inflation Adjusted Frequency Severity Loss Reserving Model

This study proposes a novel approach for estimating automobile insurance loss reserves utilizing Artificial Neural Network (ANN) techniques integrated with actuarial data intelligence. The model aims to address the challenges of accurately predicting insurance claim frequencies, severities, and overall loss reserves while accounting for inflation adjustments. Through comprehensive data analysis and model development, this research explores the effectiveness of ANN methodologies in capturing complex nonlinear relationships within insurance data. The study leverages a data set comprising automobile insurance policyholder information, claim history, and economic indicators to train and validate the ANN-based reserving model. Key aspects of the methodology include data preprocessing techniques such as one-hot encoding and scaling, followed by the construction of frequency, severity, and overall loss reserving models using ANN architectures. Moreover, the model incorporates inflation adjustment factors to ensure the accurate estimation of future loss reserves in real terms. Results from the study demonstrate the superior predictive performance of the ANN-based reserving model compared to traditional actuarial methods, with substantial improvements in accuracy and robustness. Furthermore, the model’s ability to adapt to changing market conditions and regulatory requirements, such as IFRS17, highlights its practical relevance in the insurance industry. The findings of this research contribute to the advancement of actuarial science and provide valuable insights for insurance companies seeking more accurate and efficient loss reserving techniques. The proposed ANN-based approach offers a promising avenue for enhancing risk management practices and optimizing financial decision-making processes in the automobile insurance sector.

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

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five 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 used.Five 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 models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The 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 datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

In the present industrial revolution era,the industrial mechanical system becomes incessantly highly intelligent and composite.So,it is necessary to develop data-driven and monitoring approaches for achieving quick,trustable,and high-quality analysis in an automated way.Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery.The advent of deep learning(DL)methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals.This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network(IIFD-SOIR)Model.The proposed model operates on three major processes namely signal representation,feature extraction,and classification.The proposed model uses a Continuous Wavelet Transform(CWT)is for preprocessed representation of the original vibration signal.In addition,Inception with ResNet v2 based feature extraction model is applied to generate high-level features.Besides,the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer.Finally,a multilayer perceptron(MLP)is applied as a classification technique to diagnose the faults proficiently.Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset.The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6%and 99.64%on the applied gearbox dataset and bearing dataset.The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.

Intelligent back-looking distance driver model and stability analysis for connected and automated vehicles

The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.

Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any business.Since the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely utilized.This article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise.In addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature subsets.Moreover,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial data.Furthermore,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model.For enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

An Intelligent Master Model of Computer Aided Process Planning for Large Complicated Stampings

Process planning for large complicated stampings is more complicated, illegible and multiform than that for common stampings. In this paper, an intelligent master model of computer aided process planning （CAPP） for large complicated stampings has been developed based on knowledge based engineering （KBE） and feature technology. This innovative model consists of knowledge base （KB）, process control structure （PCS）, process information model （PIM）, multidisciplinary design optimization （MDO）, model link environment （MLE） and simulation engine （SE）, to realize process planning, optimization, simulation and management integrated to complete intelligent CAPP system. In this model, KBE provides knowledge base, open architecture and knowledge reuse ability to deal with the multi-domain and multi-expression of process knowledge, and forms an integrated environment. With PIM, all the knowledge consisting of objects, constraints, cxtmricncc and decision-makings is carried by object-oriented method dynamically for knowledge-reasoning. PCS makes dynamical knowledge modified and updated timely and accordingly. MLE provides scv. cral methods to make CAPP sysmm associated and integrated. SE provides a programmable mechanism to interpret simulation course and result. Meanwhile, collaborative optimization, one method of MDO, is imported to deal with the optimization distributed for multiple purposes. All these make CAPP sysmm integrated and open to other systems, such as dic design and manufacturing system.

Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

Human fall detection(FD)acts as an important part in creating sensor based alarm system,enabling physical therapists to minimize the effect of fall events and save human lives.Generally,elderly people suffer from several diseases,and fall action is a common situation which can occur at any time.In this view,this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection(IAOA-DLFD)model to identify the fall/non-fall events.The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality.Besides,the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors.In addition,the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters.Lastly,radial basis function(RBF)network is applied for determining the proper class labels of the test images.To showcase the enhanced performance of the IAOA-DLFD technique,a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.

Research on virtual entity decision model for LVC tactical confrontation of army units

According to the requirements of the live-virtual-constructive(LVC)tactical confrontation(TC)on the virtual entity(VE)decision model of graded combat capability,diversified actions,real-time decision-making,and generalization for the enemy,the confrontation process is modeled as a zero-sum stochastic game(ZSG).By introducing the theory of dynamic relative power potential field,the problem of reward sparsity in the model can be solved.By reward shaping,the problem of credit assignment between agents can be solved.Based on the idea of meta-learning,an extensible multi-agent deep reinforcement learning(EMADRL)framework and solving method is proposed to improve the effectiveness and efficiency of model solving.Experiments show that the model meets the requirements well and the algorithm learning efficiency is high.

Intelligent model of rehabilitation training program for stroke

In view of the uncertainty and complexity,the intelligent model of rehabilitation training program for stroke was proposed,combining with the case-based reasoning(CBR) and interval type-2 fuzzy reasoning(IT2FR).The model consists of two parts:the setting model based on CBR and the feedback compensation model based on IT2FR.The former presets the value of rehabilitation training program,and the latter carries on the feedback compensation of the preset value.Experimental results show that the average percentage error of two rehabilitation training programs is 0.074%.The two programs are made by the intelligent model and rehabilitation physician.That is,the two different programs are nearly identical.It means that the intelligent model can make a rehabilitation training program effectively and improve the rehabilitation efficiency.

Novel Approach to Energy Management via Performance Shaping Factors in Power Plants

The literature that a lack of integration between the performance shaping factors(PSFs)and the energy management performance(EMP)is one of the critical problems that prevent performance improvement and reduces the power plant’s efficiency.To solve this problem,this article aims to achieve two main objectives:(1)Systematically investigate and identify the critical success factors(CSFs)for integration with PSFs and EMP;(2)Develop a novel modelling approach to predict the performance of power plants based on innovative integrated strategies.The research methodology is grounded on the theoretical and practical approach to improving performance.The Newcastle Ottawa Scale(NOS)was used to assess the quality of the literature that met the criteria.To ensure the reliability and accuracy of the proposed model,the researchers developed a hypothesis and evaluated the CSFs via a case study in the Iraqi power plants.The findings of this study succeeded in developing a novel modeling approach to predict the performance by integrating the CSFs of both the PSFs and EMP to increase the positive interaction and energy efficiency of power plants.The results confirmed the validity of the selected hypotheses and verified the positive and important relationship with the success and improvement of the performance in power plants.However,the lack of consistency and balance in the current studies indicates that the performance strategy in power plants did not receive sufficient attention and needs further investigations.

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics

The law of vehicle movement has long been studied under the umbrella of microscopic traffic flow models,especially the car-following(CF)models.These models of the movement of vehicles serve as the backbone of traffic flow analysis,simulation,autonomous vehicle development,etc.Two-dimensional(2D)vehicular movement is basically stochastic and is the result of interactions between a driver's behavior and a vehicle's characteristics.Current microscopic models either neglect 2D noise,or overlook vehicle dynamics.The modeling capabilities,thus,are limited,so that stochastic lateral movement cannot be reproduced.The present research extends an intelligent driver model(IDM)by explicitly considering both vehicle dynamics and 2D noises to formulate a stochastic 2D IDM model,with vehicle dynamics based on the stochastic differential equation(SDE)theory.Control inputs from the vehicle include the steer rate and longitudinal acceleration,both of which are developed based on an idea from a traditional intelligent driver model.The stochastic stability condition is analyzed on the basis of Lyapunov theory.Numerical analysis is used to assess the two cases:(i)when a vehicle accelerates from a standstill and(ii)when a platoon of vehicles follow a leader with a stop-and-go speed profile,the formation of congestion and subsequent dispersion are simulated.The results show that the model can reproduce the stochastic 2D trajectories of the vehicle and the marginal distribution of lateral movement.The proposed model can be used in both a simulation platform and a behavioral analysis of a human driver in traffic flow.

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

In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly healthcare.The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server.Therefore,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices.At the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision making.With this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC technique.The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease diagnosis.In 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 process.The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model.In 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 repository.The 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.

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial ones.Since nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes.The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate.With this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for WSN.The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes.In addition,the IAOAB-NLS model is stimulated by the behaviour of Aquila.The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network.For guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS model.The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

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

In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated diagnosis.To analyze the retinal malady,the system proposes a multiclass and multi-label arrangement method.Therefore,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 datasets.Therefore,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)models.With this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus images.The proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological Myopia.Besides,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 detected.In addition,SqueezeNet based feature extractor is employed to generate a collection of feature vectors.Finally,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal diseases.The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different classes.The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.