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Machine learning for pore-water pressure time-series prediction:Application of recurrent neural networks 认领 引用 被引量：31
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作者 Xin Wei Lulu Zhang +2 位作者 Hao-Qing Yang Limin Zhang Yang-Ping Yao 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第1期453-467,共15页
Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicabilit... Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy. 展开更多
关键词 Pore-water pressure Slope Multi-layer perceptron Recurrent neural networks Long short-term memory Gated recurrent unit
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An Improved SPSA Algorithm for System Identification Using Fuzzy Rules for Training Neural Networks 认领 引用 被引量：2
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作者 Ahmad T.Abdulsadda Kamran Iqbal 《International Journal of Automation and computing》 2011年第3期333-339,共7页
Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper descri... Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error. 展开更多
关键词 Nonlinear system identification simultaneous perturbation stochastic approximation （SPSA） neural networks （NNs）,fuzzy rules multi-layer perceptron （MLP）.
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Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Populationphysic-based Algorithm 认领 引用 被引量：7
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作者 Sajjad Afrakhteh Mohammad-Reza Mosavi +1 位作者 Mohammad Khishe Ahmad Ayatollahi 《International Journal of Automation and computing》 CSCD 2020年第1期108-122,共15页
A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their... A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their spatial distributions.Multi-layer perceptron neural networks(MLP-NNs)are commonly used for classification.Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently.Conventional methods for training NNs,such as gradient descent and recursive methods,have some disadvantages including low accuracy,slow convergence speed and trapping in local minimums.In this paper,in order to overcome these issues,the MLP-NN trained by a hybrid population-physics-based algorithm,the combination of particle swarm optimization and gravitational search algorithm(PSOGSA),is proposed for our classification problem.To show the advantages of using PSOGSA that trains NNs,this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization(PSO),gravitational search algorithm(GSA)and new versions of PSO.The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics.The results show that the proposed algorithm in most subjects of encephalography(EEG)dataset has very better or acceptable performance compared to others. 展开更多
关键词 Brain-computer interface(BCI) classification electroencephalography(EEG) gravitational search algorithm(GSA) multi-layer perceptron neural network(MLP-NN) particle swarm optimization
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Improvement of Shape Recognition Performance of Sendzimir Mill Control Systems Using Echo State Neural Networks 认领 引用 被引量：2
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作者 Jung-hyun PARK Seong-ik HAN Jong-shik KIM 《Journal of Iron and Steel Research International》 SCIE CAS CSCD 2014年第3期321-327,共7页
High rigidity twenty-high Sendzimir mills （ZRMs） are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a s... High rigidity twenty-high Sendzimir mills （ZRMs） are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a small diameter can be bent easily, strips often have complex shapes with mixed quarter and deep edge waves in the shape of plates. In order to solve this problem, fuzzy neural network controls are generally used for shape： recognition in ZRM control systems. Among various neural network types, the multi-layer perceptron （MLP） is typically used in current ZRMs. However, an MLP causes the loss of a large amount of shape recognition data. To improve the shape recognition per- formance of ZRM control systems, echo state networks （ESNs） are proposed to be used. Through simulation re- sults, it is found that shape recognition performance could be improved using the proposed ESN method. 展开更多
关键词 Sendzimir mill neural network multi-layer perceptron echo state network shape recognition
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Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners 认领 引用 被引量：1
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作者 Tahani S. Gendy Salwa A. Ghoneim Amal S. Zakhary 《World Journal of Engineering and Technology》 2020年第1期121-143,共23页
The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabi... The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30° and 60° and another frustum cone of 60°/30° inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances (x/dj) were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x/dj) on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R2 and F Ratio are 0.868 - 0.947 and 231.7 - 864.1 for RSM method compared to 0.964 - 0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms. 展开更多
关键词 Stabilized Turbulent Flames Bluff-Body Burners Thermal Structure Modeling Artificial Neural Network Response Surface Methodology Multi-Layer Perceptron Feed Forward Neural Network
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TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional Networks 认领 引用 被引量：1
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作者 Baoquan Liu Xi Chen +2 位作者 Qingjun Yuan Degang Li Chunxiang Gu 《Computers, Materials & Continua》 SCIE EI 2025年第2期3179-3201,共23页
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based... With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%. 展开更多
关键词 Encrypted traffic classification deep learning graph neural networks multi-layer perceptron graph convolutional networks
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Design an Artificial Neural Network by MLP Method;Analysis of the Relationship between Demographic Variables, Resilience, COVID-19 and Burnout 认领 引用
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作者 Chao-Hsi Huang Tsung-Shun Hsieh +2 位作者 Hsiao-Ting Chien Ehsan Eftekhari-Zadeh Saba Amiri 《International Journal of Mental Health Promotion》 2022年第6期825-841,共17页
In addition to the effect that the COVID-19 pandemic has had on the physical and mental health of individuals,it has also led to a change in the mental and emotional state of many employees.Especially among businesses... In addition to the effect that the COVID-19 pandemic has had on the physical and mental health of individuals,it has also led to a change in the mental and emotional state of many employees.Especially among businesses and private companies,which faced many restrictions due to the special conditions of the pandemic.Therefore,the present study aimed to design an artificial neural network with MLP technique to analyze the relationship between demographic variables,resilience,COVID-19 and burnout in start-ups in Iran.The research method was quantitative.Managers and employees of start-ups formed the statistical population of the study,based on the statistical sample size of the unlimited community,384 of them were tested.For data gathering,standard questionnaires include of MBI-GS and BRCS and researcher-made questionnaire of stress caused by COVID-19 were used.The validity of the questionnaires was confirmed by a panel of experts and their reliability was confirmed by Cronbach’s alpha coefficient.The number of neurons in the input layer was equal to 10,the number of neurons in the 1st hidden layer was equal to 7,the number of neurons in the output layer was equal to 1,and the number of epochs was equal to 500.70%of the data were used for training and 30%for testing.In the designed artificial neural network,all experiment data except one were correctly predicted and the obtained MAE error was less than 0.012%.Finally,he precision and correction of the presented model was confirmed by the obtained results. 展开更多
关键词 Burnout artificial neural network multi-layer perceptron COVID-19 resilience
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Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics 认领 引用 被引量：9
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作者 Bassam Daya Shadi Khawandi Mohamed Akoum 《Journal of Software Engineering and Applications》 2010年第3期230-239,共10页
One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexi... One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP) with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion) faced when using the Inverse Geometric Models implementation (IGM) in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach. 展开更多
关键词 Inverse Geometric Model Neural Network Multi-Layered Perceptron Robotic System Arm
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Identification and Prediction of Internet Traffic Using Artificial Neural Networks 认领 引用 被引量：8
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作者 Samira Chabaa Abdelouhab Zeroual Jilali Antari 《Journal of Intelligent Learning Systems and Applications》 2010年第3期147-155,共9页
This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time seri... This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times. 展开更多
关键词 Artificial Neural Network Multi-Layer Perceptron Training Algorithms Internet Traffic
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Fingerprint Identification by Artificial Neural Network 认领 引用
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作者 Mustapha Boutahri Said El Yamani Samir Zeriouh Abdenabi Bouzid Ahmed Roukhe 《Journal of Physical Science and Application》 2014年第6期381-384,共4页
Biometric techniques require critical operations of digital processing for identification of individuals. In this context, this paper aims to develop a system for automatic processing of fingerprint identification by ... Biometric techniques require critical operations of digital processing for identification of individuals. In this context, this paper aims to develop a system for automatic processing of fingerprint identification by their minutiae using Artificial Neural Networks （ANN）, which reveals to be highly effective. The ANN method implemented is a based on Multi-Layer Perceptron （MLP） model, which utilizes the algorithm of retro-propagation of gradient during the learning process. In such a process, the mean square error generated represents the specific parameter for the identification phase by comparing a fingerprint taken from a crime scene with those of a reference database. 展开更多
关键词 Fingerprint artificial neural network minutiae identification multi-layer perceptron back-propagation of the gradient.
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Classification and Identification of Nuclear, Biological or Chemical Agents Taken from Remote Sensing Image by Using Neural Network 认领 引用
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作者 Said El Yamani Samir Zeriouh Mustapha Boutahri Ahmed Roukhe 《Journal of Physical Science and Application》 2014年第3期177-182,共6页
In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural n... In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient. 展开更多
关键词 Artificial neural networks classification identification principal component analysis multi-layer perceptron back propagation of the gradient.
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Prediction of diabetes and hypertension using multi-layer perceptron neural networks 认领 引用 被引量：1
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作者 Hani Bani-Salameh Shadi MAlkhatib +4 位作者 Moawyiah Abdalla Mo’taz Al-Hami Ruaa Banat Hala Zyod Ahed J Alkhatib 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2021年第2期120-137,共18页
Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed wel... Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in advance.Objective:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.Sample:This work used two online datasets which consist of data collected from 768 individuals.We applied neural network algorithms to predict if the individuals have those two diseases based on some factors.Diabetes prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and smoking.Method:A model based on the Multi-Layer Perceptron Neural Network(MLP)was implemented.The inputs of the network were the factors for each disease,while the output was the prediction of the disease’s occurrence.The model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of MLP.Also,a tool was implemented to help diagnose the diseases and to understand the results.Result:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for hypertension.The results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and KNN.This shows MLPs effectiveness in early disease prediction. 展开更多
关键词 Artificial Neural Network(ANN) Multi-Layer Perceptron(MLP) SVM KNN decision-making prediction tools diabetes blood pressure hypertension software tools
Sensitivity Analysis of Radial Basis Function Networks for River Stage Forecasting 认领 引用 被引量：1
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作者 Christian Walker Dawson 《Journal of Software Engineering and Applications》 2020年第12期327-347,共21页
Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling... Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models. 展开更多
关键词 Artificial Neural Networks Backward Chaining Multi-Layer Perceptron Partial Derivative Radial Basis Function Sensitivity Analysis River Stage Forecasting
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A hybrid constriction coefficientbased particle swarm optimization and gravitational search algorithm for training multi-layer perceptron 认领 引用 被引量：3
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作者 Sajad Ahmad Rather P.Shanthi Bala 《International Journal of Intelligent Computing and Cybernetics》 EI 2020年第2期129-165,共37页
Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcom... Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of MLP.Design/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GSA)and optimization of candidate solutions,i.e.exploitation is performed by particle swarm optimization(PSO).For training the multi-layer perceptron(MLP),CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error.Secondly,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.Findings-The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems.Besides,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating algorithms.Moreover,CPSOGSA also provides very competitive results for other datasets.Originality/value-The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP.Basically,CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power.In the research literature,a little work is available where CPSO and GSA have been utilized for training MLP.The only related research paper was given by Mirjalili et al.,in 2012.They have used standard PSO and GSA for training simple FNNs.However,the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms.In this paper,eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs.In addition,a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5%significance level to statistically validate the simulation results.Besides,eight state-of-the-art metaheuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup. 展开更多
关键词 Neural network Feedforward neural network(FNN) Gravitational search algorithm(GSA) Particle swarm optimization(PSO) Hybridization CPSOGSA Multi-layer perceptron(MLP)
Environmental Sound Classification Using Deep Learning 认领 引用 被引量：9
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作者 SHANTHAKUMAR S SHAKILA S +1 位作者 SUNETH Pathirana JAYALATH Ekanayake 《Instrumentation》 2020年第3期15-22,共8页
Perhaps hearing impairment individuals cannot identify the environmental sounds due to noise around them.However,very little research has been conducted in this domain.Hence,the aim of this study is to categorize soun... Perhaps hearing impairment individuals cannot identify the environmental sounds due to noise around them.However,very little research has been conducted in this domain.Hence,the aim of this study is to categorize sounds generated in the environment so that the impairment individuals can distinguish the sound categories.To that end first we define nine sound classes--air conditioner,car horn,children playing,dog bark,drilling,engine idling,jackhammer,siren,and street music--typically exist in the environment.Then we record 100 sound samples from each category and extract features of each sound category using Mel-Frequency Cepstral Coefficients(MFCC).The training dataset is developed using this set of features together with the class variable;sound category.Sound classification is a complex task and hence,we use two Deep Learning techniques;Multi Layer Perceptron(MLP)and Convolution Neural Network(CNN)to train classification models.The models are tested using a separate test set and the performances of the models are evaluated using precision,recall and F1-score.The results show that the CNN model outperforms the MLP.However,the MLP also provided a decent accuracy in classifying unknown environmental sounds. 展开更多
关键词 Mel-Frequency Cepstral Coefficients MFCC Multi-Layer Perceptron MLP Convolutional Neural Network CNN
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Parkinson’s Disease Detection Using Biogeography-Based Optimization 认领 引用 被引量：1
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作者 Somayeh Hessam Shaghayegh Vahdat +4 位作者 Irvan Masoudi Asl Mahnaz Kazemipoor Atefeh Aghaei Shahaboddin Shamshirband Timon Rabczuk 《Computers, Materials & Continua》 SCIE EI 2019年第7期11-26,共16页
In recent years,Parkinson’s Disease(PD)as a progressive syndrome of the nervous system has become highly prevalent worldwide.In this study,a novel hybrid technique established by integrating a Multi-layer Perceptron ... In recent years,Parkinson’s Disease(PD)as a progressive syndrome of the nervous system has become highly prevalent worldwide.In this study,a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network(MLP)with the Biogeography-based Optimization(BBO)to classify PD based on a series of biomedical voice measurements.BBO is employed to determine the optimal MLP parameters and boost prediction accuracy.The inputs comprised of 22 biomedical voice measurements.The proposed approach detects two PD statuses:0-disease status and 1-good control status.The performance of proposed methods compared with PSO,GA,ACO and ES method.The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection.The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms,and consequently,it served as a promising new robust tool with excellent PD diagnosis performance. 展开更多
关键词 Parkinson’s disease(PD) biomedical voice measurements multi-layer perceptron neural network(MLP) biogeography-based optimization(BBO) medical diagnosis bio-inspired computation
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Impact of Portable Executable Header Features on Malware Detection Accuracy 认领 引用 被引量：1
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作者 Hasan H.Al-Khshali Muhammad Ilyas 《Computers, Materials & Continua》 SCIE EI 2023年第1期153-178,共26页
One aspect of cybersecurity,incorporates the study of Portable Executables(PE)files maleficence.Artificial Intelligence(AI)can be employed in such studies,since AI has the ability to discriminate benign from malicious... One aspect of cybersecurity,incorporates the study of Portable Executables(PE)files maleficence.Artificial Intelligence(AI)can be employed in such studies,since AI has the ability to discriminate benign from malicious files.In this study,an exclusive set of 29 features was collected from trusted implementations,this set was used as a baseline to analyze the presented work in this research.A Decision Tree(DT)and Neural Network Multi-Layer Perceptron(NN-MLPC)algorithms were utilized during this work.Both algorithms were chosen after testing a few diverse procedures.This work implements a method of subgrouping features to answer questions such as,which feature has a positive impact on accuracy when added?Is it possible to determine a reliable feature set to distinguish a malicious PE file from a benign one?when combining features,would it have any effect on malware detection accuracy in a PE file?Results obtained using the proposed method were improved and carried few observations.Generally,the obtained results had practical and numerical parts,for the practical part,the number of features and which features included are the main factors impacting the calculated accuracy,also,the combination of features is as crucial in these calculations.Numerical results included,finding accuracies with enhanced values,for example,NN_MLPC attained 0.979 and 0.98;for DT an accuracy of 0.9825 and 0.986 was attained. 展开更多
关键词 AI driven cybersecurity artificial intelligence cybersecurity Decision Tree Neural Network Multi-Layer Perceptron Classifier portable executable(PE)file header features
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Integrating the effect of abutments in estimating the average vertical stress of elastic hard rock pillars by combining numerical modelling and artificial neural networks 认领 引用
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作者 Nevaid Dzimunya Yoshiaki Fujii Youhei Kawamura 《Underground Space》 SCIE EI CSCD 2023年第6期121-135,共15页
Estimating average vertical pillar stresses is a critical step in designing room-and-pillar mines.Several analytical methods can be used to estimate the vertical stresses acting on the pillars.However,the present anal... Estimating average vertical pillar stresses is a critical step in designing room-and-pillar mines.Several analytical methods can be used to estimate the vertical stresses acting on the pillars.However,the present analytical methods fail to adequately account for the influence of abutments on the distribution of vertical stresses,especially when applied to narrow panel widths and pillar layouts comprising evenly spaced barriers.In this study,a multi-layer perceptron neural network(MLPNN)was applied to predict the vertical loads of regular pillars more accurately.Hundreds of room-and-pillar mine layouts were modeled using a displacement discontinuity method(DDM),and a database of 2355 sampled pillar cases was compiled.The MLPNN was trained based on this database,and its prediction capabilities were further validated using simulations by a finite difference code(i.e.,FLAC3D).The model predictions and the FLAC3D simulations reasonably agreed with a regression coefficient of 0.99.The model was also adapted for mine cases with evenly spaced barrier pillars,and its application to a real case study mine has shown to provide accurate pillar stress estimations;hence,this model is suitable for practical use at mines.Even though the MLPNN model cannot be applied universally to all mine situations,it seems as a significant improvement over existing analytical techniques in terms of accounting for the influence of abutments on pillar stresses. 展开更多
关键词 Pillar stress Abutments Multi-layer perceptron neural network Numerical simulation Room-and-pillar mine
Voice to Face Recognition Using Spectral ERB-DMLP Algorithms 认领 引用
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作者 Fauzi A.Bala Osman N.Ucan Oguz Bayat 《Computers, Materials & Continua》 SCIE EI 2022年第10期2187-2204,共18页
Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented... Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment.This authentication is also vital to prevent any security threats or risks like compromises of business server,release of confidential data etc.Though conventional works attempted to accomplish better authentication,they lacked with respect to accuracy.Hence,the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning(DL)due to its ability to perform effective learning.The study proposes Equivalent Rectangular Bandwidth and Deep Multi-Layer Perceptron(ERB-DMLP)as it has the ability to perform efficient and relevant feature extraction and faster classification.This algorithm also has the ability to establish effective correlation between voices and images and achieve the semantic relationship between them.Voice preprocessing is initially performed to make it suitable for further processing by removing the noise and enhancing the quality of signal.This process is also vital to minimize the extra computations so that the overall efficacy of the system can be made flexible by considering the audio files as features and the images as labels to identify a person’s voice by classifying the extracted features from the ERB Feature Extraction.This is then passed as the input into DMLP model to classify the persons,and trained the model to make an accurate classification of audio with corresponding image labels,and perform the performance test based on the trained model.Flexibility,relevant feature extraction and faster classification ability of the proposed work has made it explore better outcomes that is confirmed through results. 展开更多
关键词 Authentication system power plant equivalent rectangular bandwidth deep multi-layer perceptron convolution neural network
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Design and analysis of control system using neural network for regulated DC power supply 认领 引用
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作者 Z I DAFALLA Jihad Alkhalaf BANI-YOUNIS L K WAH 《Frontiers of Electrical and Electronic Engineering in China》 CSCD 2011年第4期567-574,共8页
Conventional control systems used for regulated power supplies,including the proportional integral and derivation(PID)controller,have some serious disadvantages.The PID controller has a delayed feedback associated wit... Conventional control systems used for regulated power supplies,including the proportional integral and derivation(PID)controller,have some serious disadvantages.The PID controller has a delayed feedback associated with the control action and requires a lot of mathematical derivations.This paper presents a novel controlling system based on the artificial neural network(ANN),which can be used to regulate the output voltage of the DC power supply.Using MATLABTM,the designed control system was tested and analyzed with two types of back-propagation algorithms.This paper presents the results of the simulation that includes sum-squared error(SSE)and mean-squared error(MSE),and gives a detailed comparison of these values for the two algorithms.Hardware verification of the new system,using RS232 interface and Microsoft Visual Basic 6.0,was implemented,showing very good consistency with the simulation results.The proposed control system,compared to PID and other conventional controllers,requires less mathematical derivation in design and it is easier to implement. 展开更多
关键词 regulated power supply neural network proportional integral and derivation(PID)controller multi-layer perceptron(MLP)network
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