Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limite...Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation.Methods:We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model.External validation was conducted using an independent cohort of patients.A graph neural network architecture,termed the pathway-aware multi-layered hierarchical network-Western and Asian(P-NETwa),was developed and trained on combined genomic profiles from Chinese and Western cohorts.The model employed a multilayer perceptron(MLP)to identify key signature genes from multiomics data,enabling precise prediction of PCa metastasis.Results:The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets.The application of integrated Chinese and Western population data improved the accuracy to 0.88,achieving an F1-score of 0.75.The analysis identified 18 signature genes implicated in PCa progression,including established markers(AR and TP53)and novel candidates(MUC16,MUC4,and ASB12).For clinical adoption,the model was optimized for commercially available gene panels while maintaining high classification accuracy.Additionally,a user-friendly web interface was developed to facilitate real-time prediction of primary versus metastatic status using the pre-trained P-NETwa-MLP model.Conclusion:The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID,enhancing its potential for seamless integration into clinical workflows.展开更多
Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy sys...Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.展开更多
The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthca...The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.展开更多
This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential p...This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.展开更多
To improve the performance of multilayer perceptron(MLP)neural networks activated by conventional activation functions,this paper presents a new MLP activated by univariate Gaussian radial basis functions(RBFs)with ad...To improve the performance of multilayer perceptron(MLP)neural networks activated by conventional activation functions,this paper presents a new MLP activated by univariate Gaussian radial basis functions(RBFs)with adaptive centers and widths,which is composed of more than one hidden layer.In the hidden layer of the RBF-activated MLP network(MLPRBF),the outputs of the preceding layer are first linearly transformed and then fed into the univariate Gaussian RBF,which exploits the highly nonlinear property of RBF.Adaptive RBFs might address the issues of saturated outputs,low sensitivity,and vanishing gradients in MLPs activated by other prevailing nonlinear functions.Finally,we apply four MLP networks with the rectified linear unit(ReLU),sigmoid function(sigmoid),hyperbolic tangent function(tanh),and Gaussian RBF as the activation functions to approximate the one-dimensional(1D)sinusoidal function,the analytical solution of viscous Burgers’equation,and the two-dimensional(2D)steady lid-driven cavity flows.Using the same network structure,MLP-RBF generally predicts more accurately and converges faster than the other threeMLPs.MLP-RBF using less hidden layers and/or neurons per layer can yield comparable or even higher approximation accuracy than other MLPs equipped with more layers or neurons.展开更多
The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical c...The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.展开更多
The Multilayer Perceptron Neural Network (MLPNN) induction technique has been successfully applied to a variety of machine learning tasks, including the extraction and classification of image features. However, not mu...The Multilayer Perceptron Neural Network (MLPNN) induction technique has been successfully applied to a variety of machine learning tasks, including the extraction and classification of image features. However, not much has been done in the application of MLPNN on images obtained by remote sensing. In this article, two automatic classification systems used in image feature extraction and classification from remote sensing data are presented. The first is a combination of two models: a MLPNN induction technique, integrated under ENVI (Environment for Visualizing Images) platform for classification, and a pre-processing model including dark subtraction for the calibration of the image, the Principal Components Analysis (PCA) for band selections and Independent Components Analysis (ICA) as blind source separator for feature extraction of the Landsat image. The second classification system is a MLPNN induction technique based on the Keras platform. In this case, there was no need for pre-processing model. Experimental results show the two classification systems to outperform other typical feature extraction and classification methods in terms of accuracy for some lithological classes including Granite1 class with the highest class accuracies of 96.69% and 92.69% for the first and second classification system respectively. Meanwhile, the two classification systems perform almost equally with the overall accuracies of 53.01% and 49.98% for the first and second models respectively </span><span style="font-family:Verdana;">though the keras model has the advantage of not integrating the pre-processing</span><span style="font-family:Verdana;"> model, hence increasing its efficiency. The application of these two systems to the study area resulted in the generation of an updated geological mapping with six lithological classes detected including the Gneiss, the Micaschist, the Schist and three versions of Granites (Granite1, Granite2 and Granite3).展开更多
This paper introduces a novel hybrid optimization algorithm,Adaptive Hybrid PSO-Embedded GA(AHPEGA),which dynamically adapts to optimization performance by integrating Particle Swarm Optimization(PSO)and Genetic Algor...This paper introduces a novel hybrid optimization algorithm,Adaptive Hybrid PSO-Embedded GA(AHPEGA),which dynamically adapts to optimization performance by integrating Particle Swarm Optimization(PSO)and Genetic Algorithms(GA).The primary objective is to enhance the neuroevolutionary training of multilayer perceptron-based controllers(MLPCs)through the joint optimization of model parameters and structural hyperparameters.Traditional training methods frequently encounter issues such as premature convergence and limited generalization.AHPEGA addresses these limitations through an adaptive training strategy that dynamically adjusts parameters during the evolutionary process,thereby improving convergence speed and solution quality.By effectively reducing entrapment in local minima and balancing exploration and exploitation,AHPEGA improves the quality of neural controller design.The algorithm’s performance is evaluated against conventional optimization methods,demonstrating significant improvements in accuracy,convergence speed,and consistency across multiple runs.The practical applicability of the proposed method is demonstrated through simulation in the context of a VSC-based islanded microgrid(MG),where ensuring reliable and effective control under variable operating conditions is critical.This highlights AHPEGA’s capability to optimize intelligent control strategies in MG systems,particularly under dynamic and uncertain conditions,reinforcing its practical value in real-world energy environments.展开更多
The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite ...The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.展开更多
Purpose-Software defect prediction(SDP)is a critical aspect of software quality assurance,aiming to identify and manage potential defects in software systems.In this paper,we have proposed a novel hybrid approach that...Purpose-Software defect prediction(SDP)is a critical aspect of software quality assurance,aiming to identify and manage potential defects in software systems.In this paper,we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection(GWOFS)and multilayer perceptron(MLP)for SDP.The GWOFS-MLP hybrid model is designed to optimize feature selection,ultimately enhancing the accuracy and efficiency of SDP.Grey Wolf Optimization,inspired by the social hierarchy and hunting behavior of grey wolves,is employed to select a subset of relevant features from an extensive pool of potential predictors.This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.Design/methodology/approach-The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets.This feature selection process harnesses the cooperative hunting behavior of wolves,allowing for the exploration of critical feature combinations.The selected features are then fed into an MLP,a powerful artificial neural network(ANN)known for its capability to learn intricate patterns within software metrics.MLP serves as the predictive engine,utilizing the curated feature set to model and classify software defects accurately.Findings-The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness.The model achieves a remarkable training accuracy of 97.69%and a testing accuracy of 97.99%.Additionally,the receiver operating characteristic area under the curve(ROC-AUC)score of 0.89 highlights themodel’s ability to discriminate between defective and defect-free software components.Originality/value-Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions.The goal is to enhance SDP’s accuracy,relevance and efficiency,ultimately improving software quality assurance processes.The confusion matrix further illustrates the model’s performance,with only a small number of false positives and false negatives.展开更多
Background Various methods can be applied to build predictive models for the clinical data with binary outcome variable. This research aims to explore the process of constructing common predictive models, Logistic reg...Background Various methods can be applied to build predictive models for the clinical data with binary outcome variable. This research aims to explore the process of constructing common predictive models, Logistic regression （LR）, decision tree （DT） and multilayer perceptron （MLP）, as well as focus on specific details when applying the methods mentioned above： what preconditions should be satisfied, how to set parameters of the model, how to screen variables and build accuracy models quickly and efficiently, and how to assess the generalization ability （that is, prediction performance） reliably by Monte Carlo method in the case of small sample size.展开更多
Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards the...Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, 'maturing data' (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data’. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data’ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data’ phenomena.展开更多
Exact determination of pressure-volume-temperature(PVT)properties of the reservoir oils is necessary for reservoir calculations,reservoir performance prediction,and the design of optimal production conditions.The obje...Exact determination of pressure-volume-temperature(PVT)properties of the reservoir oils is necessary for reservoir calculations,reservoir performance prediction,and the design of optimal production conditions.The objective of this study is to develop intelligent and reliable models based on multilayer perceptron(MLP)and radial basis function(RBF)neural networks for estimating the solution gas–oil ratio as a function of bubble point pressure,reservoir temperature,oil gravity(API),and gas specific gravity.These models were developed and tested using a total of 710 experimental data sets representing the samples of crude oil from various geographical locations around the world.Performance of the developed MLP and RBF models were evaluated and investigated against a number of well-known empirical correlations using statistical and graphical error analyses.The results indicated that the proposed models outperform the considered empirical correlations,providing a strong agreement between predicted and experimental values,However,the developed RBF exhibited higher accuracy and efficiency compared to the proposed MLP model.展开更多
Introduction:Due to the health effects caused by airborne pollutants in urban areas,forecasting of air quality parameters is one of the most important topics of air quality research.During recent years,statistical mod...Introduction:Due to the health effects caused by airborne pollutants in urban areas,forecasting of air quality parameters is one of the most important topics of air quality research.During recent years,statistical models based on artificial neural networks(ANNs)have been increasingly applied and evaluated for forecasting of air quality.Methods:The development of ANN and multiple linear regressions(MLRs)has been applied to short-term prediction of the NO_(2) and NO_(x) concentrations as a function of meteorological conditions.The optimum structure of ANN was determined by a trial and error method.We used hourly NO_(x) and NO_(2) concentrations and metrological parameters,automatic monitoring network during October and November 2012 for two monitoring sites(Abrasan and Farmandari sites)in Tabriz,Iran.Results:Designing of the network architecture is based on the approximation theory of Kolmogorov,and the structure of ANN with 30 neurons had the best performance.ANN trained by scaled-conjugate-gradient(trainscg)training algorithm has implemented to model.It also demonstrates that MLP neural networks offer several advantages over linear MLR models.The results show that the correlation coefficient(R2)values are 0.92 and 0/94 for NO_(2) and NO_(x) concentrations,respectively.But in MLR model,R2 values were 0.41 and 0.44 for NO_(2) and NO_(x) concentrations,respectively.Conclusions:This work shows that MLP neural networks can accurately model the relationship between local meteorological data and NO_(2) and NO_(x) concentrations in an urban environment compared to linear models.展开更多
Speckle noise in synthetic-aperture radar (SAR) images severely hindersremote sensing applications;therefore, the appropriate removal ofspeckle noise is crucial. This paper elaborates on the multilayerperceptron (MLP)...Speckle noise in synthetic-aperture radar (SAR) images severely hindersremote sensing applications;therefore, the appropriate removal ofspeckle noise is crucial. This paper elaborates on the multilayerperceptron (MLP) neural-network model for SAR image despeckling byusing a time series of SAR images. Unlike other filtering methods thatuse only a single radar intensity image to derive their parameters andfilter that single image, this method can be trained using archivedimages over an area of interest to self-learn the intensitycharacteristics of image patches and then adaptively determine theweights and thresholds by using a neural network for imagedespeckling. Several hidden layers are designed for feedforwardnetwork training, and back-propagation stochastic gradient descent isadopted to reduce the error between the target output and neuralnetwork output. The parameters in the network are automaticallyupdated in the training process. The greatest advantage of MLP is thatonce the despeckling parameters are determined, they can be used toprocess not only new images in the same area but also images incompletely different locations. Tests with images from TerraSAR-X inselected areas indicated that MLP shows satisfactory performance withrespect to noise reduction and edge preservation. The overall imagequality obtained using MLP was markedly higher than that obtainedusing numerous other filters. In comparison with other recentlydeveloped filters, this method yields a slightly higher image quality,and it demonstrates the powerful capabilities of computer learningusing SAR images, which indicate the promising prospect of applyingMLP to SAR image despeckling.展开更多
This paper discusses the issues of computational efforts and the accuracy of solutions of differential equations using multilayer perceptron and Chebyshev polynomials-based functional link artificial neural networks.S...This paper discusses the issues of computational efforts and the accuracy of solutions of differential equations using multilayer perceptron and Chebyshev polynomials-based functional link artificial neural networks.Some ordinary and partial differential equations have been solved by both these techniques and pros and cons of both these type of feedforward networks have been discussed in detail.Apart from that,various factors that affect the accuracy of the solution have also been analyzed.展开更多
Learning of the feedforward multilayer perceptron (MLP) networks is to adapt all synaptic weights in such a way that the discrepancy between the actual output signals and the desired signals, averaged over all learnin...Learning of the feedforward multilayer perceptron (MLP) networks is to adapt all synaptic weights in such a way that the discrepancy between the actual output signals and the desired signals, averaged over all learning examples (training patterns), is as small as possible. The backpropagation, or variations thereof, is a standard method applied to adjust the synaptic weights in the network in order to minimize a given cost function. However as a steepest descent approach, BP algorithm is too slow for many applications. Since late 1980s lots of efforts have been reported in the literature aimed at improving the efficiency of the algorithm. Among them a recently proposed learning strategy based on linearization of the nonlinear activation functions and optimization of the multilayer perceptron layer by layer (OLL) seems promising. In this paper a modified learning procedure is presented which tries to find a weight change vector at each trial iteration in the OLL algorithm more efficiently. The proposed learning procedure can save expensive computation efforts and yield better convergence rate as compared to the original OLL learning algorithms especially for large scale networks. The improved OLL learning algorithm is applied to the time series prediction problems presented by the OLL authors, and demonstrates a faster learning capability.展开更多
In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by pred...In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.展开更多
Existing icing detection technologies face challenges when applied to small and medium-sized aircraft,especially electric vertical take-off and landing(eVTOL)aircraft that meet the needs of low-altitude economic devel...Existing icing detection technologies face challenges when applied to small and medium-sized aircraft,especially electric vertical take-off and landing(eVTOL)aircraft that meet the needs of low-altitude economic development.This study proposes a data-driven icing detection method based on rotor performance evolution.Through dry-air baseline tests and dynamic icing comparative experiments(wind speed 0—30 m/s,rotational speed 0—3000 r/min,collective pitch 0°—8°)of a 0.6 m rotor in the FL-61 icing wind tunnel,a multi-source heterogeneous dataset containing motion parameters,aerodynamic parameters,and icing state identifiers is constructed.An innovative signal processing architecture combining adaptive Kalman filtering and moving average cascading is adopted.And a comparative study is conducted on the performance of support vector machine(SVM),multilayer perceptron(MLP),and random forest(RF)algorithms,achieving real-time identification of icing states in rotating components.Experimental results demonstrate that the method exhibits a minimum detection latency of 6.9 s and 96%overall accuracy in reserved test cases,featuring low-latency and low false-alarm,providing a sensor-free lightweight solution for light/vertical takeoff and landing aircraft.展开更多
Considering the three-dimensional(3D) U-Net lacks sufficient local feature extraction for image features and lacks attention to the fusion of high-and low-level features, we propose a new model called 3DMAU-Net based ...Considering the three-dimensional(3D) U-Net lacks sufficient local feature extraction for image features and lacks attention to the fusion of high-and low-level features, we propose a new model called 3DMAU-Net based on the 3D U-Net architecture for liver region segmentation. Our model replaces the last two layers of the 3D U-Net with a sliding window-based multilayer perceptron(SMLP), enabling better extraction of local image features. We also design a high-and low-level feature fusion dilated convolution block that focuses on local features and better supplements the surrounding information of the target region. This block is embedded in the entire encoding process, ensuring that the overall network is not simply downsampling. Before each feature extraction, the input features are processed by the dilated convolution block. We validate our experiments on the liver tumor segmentation challenge 2017(Lits2017) dataset, and our model achieves a Dice coefficient of 0.95, which is an improvement of 0.015 compared to the 3D U-Net model. Furthermore, we compare our results with other segmentation methods, and our model consistently outperforms them.展开更多
基金supported by the National Key R&D Program of China(2022YFA1305700 to Li J)the“Dawn”Program of Shanghai Education Commission,China(21SG33 to Li J)The National Natural Science Foundation of China(82272793 to Gao X).
文摘Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation.Methods:We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model.External validation was conducted using an independent cohort of patients.A graph neural network architecture,termed the pathway-aware multi-layered hierarchical network-Western and Asian(P-NETwa),was developed and trained on combined genomic profiles from Chinese and Western cohorts.The model employed a multilayer perceptron(MLP)to identify key signature genes from multiomics data,enabling precise prediction of PCa metastasis.Results:The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets.The application of integrated Chinese and Western population data improved the accuracy to 0.88,achieving an F1-score of 0.75.The analysis identified 18 signature genes implicated in PCa progression,including established markers(AR and TP53)and novel candidates(MUC16,MUC4,and ASB12).For clinical adoption,the model was optimized for commercially available gene panels while maintaining high classification accuracy.Additionally,a user-friendly web interface was developed to facilitate real-time prediction of primary versus metastatic status using the pre-trained P-NETwa-MLP model.Conclusion:The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID,enhancing its potential for seamless integration into clinical workflows.
基金the Deanship of Graduate Studies and Scientific Research at University of Bisha,Saudi Arabia for funding this research work through the Promising Program under Grant Number(UB-Promising-42-1445).
文摘Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.
基金funded by King Saud University through Researchers Supporting Program Number (RSP2024R499).
文摘The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.
文摘This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.
基金This work was partially supported by the research grant of the National University of Singapore(NUS),Ministry of Education(MOE Tier 1).
文摘To improve the performance of multilayer perceptron(MLP)neural networks activated by conventional activation functions,this paper presents a new MLP activated by univariate Gaussian radial basis functions(RBFs)with adaptive centers and widths,which is composed of more than one hidden layer.In the hidden layer of the RBF-activated MLP network(MLPRBF),the outputs of the preceding layer are first linearly transformed and then fed into the univariate Gaussian RBF,which exploits the highly nonlinear property of RBF.Adaptive RBFs might address the issues of saturated outputs,low sensitivity,and vanishing gradients in MLPs activated by other prevailing nonlinear functions.Finally,we apply four MLP networks with the rectified linear unit(ReLU),sigmoid function(sigmoid),hyperbolic tangent function(tanh),and Gaussian RBF as the activation functions to approximate the one-dimensional(1D)sinusoidal function,the analytical solution of viscous Burgers’equation,and the two-dimensional(2D)steady lid-driven cavity flows.Using the same network structure,MLP-RBF generally predicts more accurately and converges faster than the other threeMLPs.MLP-RBF using less hidden layers and/or neurons per layer can yield comparable or even higher approximation accuracy than other MLPs equipped with more layers or neurons.
文摘The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.
文摘The Multilayer Perceptron Neural Network (MLPNN) induction technique has been successfully applied to a variety of machine learning tasks, including the extraction and classification of image features. However, not much has been done in the application of MLPNN on images obtained by remote sensing. In this article, two automatic classification systems used in image feature extraction and classification from remote sensing data are presented. The first is a combination of two models: a MLPNN induction technique, integrated under ENVI (Environment for Visualizing Images) platform for classification, and a pre-processing model including dark subtraction for the calibration of the image, the Principal Components Analysis (PCA) for band selections and Independent Components Analysis (ICA) as blind source separator for feature extraction of the Landsat image. The second classification system is a MLPNN induction technique based on the Keras platform. In this case, there was no need for pre-processing model. Experimental results show the two classification systems to outperform other typical feature extraction and classification methods in terms of accuracy for some lithological classes including Granite1 class with the highest class accuracies of 96.69% and 92.69% for the first and second classification system respectively. Meanwhile, the two classification systems perform almost equally with the overall accuracies of 53.01% and 49.98% for the first and second models respectively </span><span style="font-family:Verdana;">though the keras model has the advantage of not integrating the pre-processing</span><span style="font-family:Verdana;"> model, hence increasing its efficiency. The application of these two systems to the study area resulted in the generation of an updated geological mapping with six lithological classes detected including the Gneiss, the Micaschist, the Schist and three versions of Granites (Granite1, Granite2 and Granite3).
基金financial support from the Swedish International Development Cooperation Agency(SIDA)through the research capacity-building program between Addis Ababa University and Swedish universities.
文摘This paper introduces a novel hybrid optimization algorithm,Adaptive Hybrid PSO-Embedded GA(AHPEGA),which dynamically adapts to optimization performance by integrating Particle Swarm Optimization(PSO)and Genetic Algorithms(GA).The primary objective is to enhance the neuroevolutionary training of multilayer perceptron-based controllers(MLPCs)through the joint optimization of model parameters and structural hyperparameters.Traditional training methods frequently encounter issues such as premature convergence and limited generalization.AHPEGA addresses these limitations through an adaptive training strategy that dynamically adjusts parameters during the evolutionary process,thereby improving convergence speed and solution quality.By effectively reducing entrapment in local minima and balancing exploration and exploitation,AHPEGA improves the quality of neural controller design.The algorithm’s performance is evaluated against conventional optimization methods,demonstrating significant improvements in accuracy,convergence speed,and consistency across multiple runs.The practical applicability of the proposed method is demonstrated through simulation in the context of a VSC-based islanded microgrid(MG),where ensuring reliable and effective control under variable operating conditions is critical.This highlights AHPEGA’s capability to optimize intelligent control strategies in MG systems,particularly under dynamic and uncertain conditions,reinforcing its practical value in real-world energy environments.
基金supported by the National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154)the Key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)+1 种基金the Young and Middle-Aged Scientific and Technological Innovation Team Plan in Higher Education Institutions in Hubei Province,China(Grant No.T2023007)the National Natural Science Foundation of China(Grant No.U23A20318).
文摘The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.
文摘Purpose-Software defect prediction(SDP)is a critical aspect of software quality assurance,aiming to identify and manage potential defects in software systems.In this paper,we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection(GWOFS)and multilayer perceptron(MLP)for SDP.The GWOFS-MLP hybrid model is designed to optimize feature selection,ultimately enhancing the accuracy and efficiency of SDP.Grey Wolf Optimization,inspired by the social hierarchy and hunting behavior of grey wolves,is employed to select a subset of relevant features from an extensive pool of potential predictors.This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.Design/methodology/approach-The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets.This feature selection process harnesses the cooperative hunting behavior of wolves,allowing for the exploration of critical feature combinations.The selected features are then fed into an MLP,a powerful artificial neural network(ANN)known for its capability to learn intricate patterns within software metrics.MLP serves as the predictive engine,utilizing the curated feature set to model and classify software defects accurately.Findings-The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness.The model achieves a remarkable training accuracy of 97.69%and a testing accuracy of 97.99%.Additionally,the receiver operating characteristic area under the curve(ROC-AUC)score of 0.89 highlights themodel’s ability to discriminate between defective and defect-free software components.Originality/value-Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions.The goal is to enhance SDP’s accuracy,relevance and efficiency,ultimately improving software quality assurance processes.The confusion matrix further illustrates the model’s performance,with only a small number of false positives and false negatives.
基金This work was supported by the grants from National Natural Science Foundation of China （No. 21003077）, College of Public Health of Tianjin Medical University in China （No. GWKY-2010-01）, the Open Project of Key Laboratory of Advanced Energy Materials Chemistry （No. KLAEMC- OP201101） and Natural Science Foundation of Tianjin China （No. 08JCZDJC21400）.
文摘Background Various methods can be applied to build predictive models for the clinical data with binary outcome variable. This research aims to explore the process of constructing common predictive models, Logistic regression （LR）, decision tree （DT） and multilayer perceptron （MLP）, as well as focus on specific details when applying the methods mentioned above： what preconditions should be satisfied, how to set parameters of the model, how to screen variables and build accuracy models quickly and efficiently, and how to assess the generalization ability （that is, prediction performance） reliably by Monte Carlo method in the case of small sample size.
文摘Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, 'maturing data' (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data’. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data’ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data’ phenomena.
文摘Exact determination of pressure-volume-temperature(PVT)properties of the reservoir oils is necessary for reservoir calculations,reservoir performance prediction,and the design of optimal production conditions.The objective of this study is to develop intelligent and reliable models based on multilayer perceptron(MLP)and radial basis function(RBF)neural networks for estimating the solution gas–oil ratio as a function of bubble point pressure,reservoir temperature,oil gravity(API),and gas specific gravity.These models were developed and tested using a total of 710 experimental data sets representing the samples of crude oil from various geographical locations around the world.Performance of the developed MLP and RBF models were evaluated and investigated against a number of well-known empirical correlations using statistical and graphical error analyses.The results indicated that the proposed models outperform the considered empirical correlations,providing a strong agreement between predicted and experimental values,However,the developed RBF exhibited higher accuracy and efficiency compared to the proposed MLP model.
文摘Introduction:Due to the health effects caused by airborne pollutants in urban areas,forecasting of air quality parameters is one of the most important topics of air quality research.During recent years,statistical models based on artificial neural networks(ANNs)have been increasingly applied and evaluated for forecasting of air quality.Methods:The development of ANN and multiple linear regressions(MLRs)has been applied to short-term prediction of the NO_(2) and NO_(x) concentrations as a function of meteorological conditions.The optimum structure of ANN was determined by a trial and error method.We used hourly NO_(x) and NO_(2) concentrations and metrological parameters,automatic monitoring network during October and November 2012 for two monitoring sites(Abrasan and Farmandari sites)in Tabriz,Iran.Results:Designing of the network architecture is based on the approximation theory of Kolmogorov,and the structure of ANN with 30 neurons had the best performance.ANN trained by scaled-conjugate-gradient(trainscg)training algorithm has implemented to model.It also demonstrates that MLP neural networks offer several advantages over linear MLR models.The results show that the correlation coefficient(R2)values are 0.92 and 0/94 for NO_(2) and NO_(x) concentrations,respectively.But in MLR model,R2 values were 0.41 and 0.44 for NO_(2) and NO_(x) concentrations,respectively.Conclusions:This work shows that MLP neural networks can accurately model the relationship between local meteorological data and NO_(2) and NO_(x) concentrations in an urban environment compared to linear models.
文摘Speckle noise in synthetic-aperture radar (SAR) images severely hindersremote sensing applications;therefore, the appropriate removal ofspeckle noise is crucial. This paper elaborates on the multilayerperceptron (MLP) neural-network model for SAR image despeckling byusing a time series of SAR images. Unlike other filtering methods thatuse only a single radar intensity image to derive their parameters andfilter that single image, this method can be trained using archivedimages over an area of interest to self-learn the intensitycharacteristics of image patches and then adaptively determine theweights and thresholds by using a neural network for imagedespeckling. Several hidden layers are designed for feedforwardnetwork training, and back-propagation stochastic gradient descent isadopted to reduce the error between the target output and neuralnetwork output. The parameters in the network are automaticallyupdated in the training process. The greatest advantage of MLP is thatonce the despeckling parameters are determined, they can be used toprocess not only new images in the same area but also images incompletely different locations. Tests with images from TerraSAR-X inselected areas indicated that MLP shows satisfactory performance withrespect to noise reduction and edge preservation. The overall imagequality obtained using MLP was markedly higher than that obtainedusing numerous other filters. In comparison with other recentlydeveloped filters, this method yields a slightly higher image quality,and it demonstrates the powerful capabilities of computer learningusing SAR images, which indicate the promising prospect of applyingMLP to SAR image despeckling.
基金The second author of this article is grateful to the National Board for Higher Mathematics(NBHM)Mumbai,Department of Atomic Energy,Government of India for providing financial support through its project,sanction order number:2/48(1)2016/NBHM(R.P.)R&D-11/13847.
文摘This paper discusses the issues of computational efforts and the accuracy of solutions of differential equations using multilayer perceptron and Chebyshev polynomials-based functional link artificial neural networks.Some ordinary and partial differential equations have been solved by both these techniques and pros and cons of both these type of feedforward networks have been discussed in detail.Apart from that,various factors that affect the accuracy of the solution have also been analyzed.
基金supported by the National Natural Sciences Foundation of China.
文摘Learning of the feedforward multilayer perceptron (MLP) networks is to adapt all synaptic weights in such a way that the discrepancy between the actual output signals and the desired signals, averaged over all learning examples (training patterns), is as small as possible. The backpropagation, or variations thereof, is a standard method applied to adjust the synaptic weights in the network in order to minimize a given cost function. However as a steepest descent approach, BP algorithm is too slow for many applications. Since late 1980s lots of efforts have been reported in the literature aimed at improving the efficiency of the algorithm. Among them a recently proposed learning strategy based on linearization of the nonlinear activation functions and optimization of the multilayer perceptron layer by layer (OLL) seems promising. In this paper a modified learning procedure is presented which tries to find a weight change vector at each trial iteration in the OLL algorithm more efficiently. The proposed learning procedure can save expensive computation efforts and yield better convergence rate as compared to the original OLL learning algorithms especially for large scale networks. The improved OLL learning algorithm is applied to the time series prediction problems presented by the OLL authors, and demonstrates a faster learning capability.
基金supported by National Natural Science Foundation of China(Nos.62266054 and 62166050)Key Program of Fundamental Research Project of Yunnan Science and Technology Plan(No.202201AS070021)+2 种基金Yunnan Fundamental Research Projects(No.202401AT070122)Yunnan International Joint Research and Development Center of China-Laos-Thailand Educational Digitalization(No.202203AP140006)Scientific Research Foundation of Yunnan Provincial Department of Education(No.2024Y159).
文摘In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.
基金supported in part by the National Key R&D Program of China(No.2022YFE0203700)the Aeronautical Science Foundation of China(No.2023Z010027001)。
文摘Existing icing detection technologies face challenges when applied to small and medium-sized aircraft,especially electric vertical take-off and landing(eVTOL)aircraft that meet the needs of low-altitude economic development.This study proposes a data-driven icing detection method based on rotor performance evolution.Through dry-air baseline tests and dynamic icing comparative experiments(wind speed 0—30 m/s,rotational speed 0—3000 r/min,collective pitch 0°—8°)of a 0.6 m rotor in the FL-61 icing wind tunnel,a multi-source heterogeneous dataset containing motion parameters,aerodynamic parameters,and icing state identifiers is constructed.An innovative signal processing architecture combining adaptive Kalman filtering and moving average cascading is adopted.And a comparative study is conducted on the performance of support vector machine(SVM),multilayer perceptron(MLP),and random forest(RF)algorithms,achieving real-time identification of icing states in rotating components.Experimental results demonstrate that the method exhibits a minimum detection latency of 6.9 s and 96%overall accuracy in reserved test cases,featuring low-latency and low false-alarm,providing a sensor-free lightweight solution for light/vertical takeoff and landing aircraft.
基金supported by the Shandong Provincial Natural Science Foundation (Nos.ZR2023MF062 and ZR2021MF115)the Introduction and Cultivation Program for Young Innovative Talents of Universities in Shandong (No.2021QCYY003)。
文摘Considering the three-dimensional(3D) U-Net lacks sufficient local feature extraction for image features and lacks attention to the fusion of high-and low-level features, we propose a new model called 3DMAU-Net based on the 3D U-Net architecture for liver region segmentation. Our model replaces the last two layers of the 3D U-Net with a sliding window-based multilayer perceptron(SMLP), enabling better extraction of local image features. We also design a high-and low-level feature fusion dilated convolution block that focuses on local features and better supplements the surrounding information of the target region. This block is embedded in the entire encoding process, ensuring that the overall network is not simply downsampling. Before each feature extraction, the input features are processed by the dilated convolution block. We validate our experiments on the liver tumor segmentation challenge 2017(Lits2017) dataset, and our model achieves a Dice coefficient of 0.95, which is an improvement of 0.015 compared to the 3D U-Net model. Furthermore, we compare our results with other segmentation methods, and our model consistently outperforms them.
