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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data
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作者 Uddagiri Sirisha Parvathaneni Naga Srinivasu +4 位作者 Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz 《Computers, Materials & Continua》 SCIE EI 2024年第8期2301-2330,共30页
Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To kn... Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process. 展开更多
关键词 Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering
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Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable 被引量:1
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作者 Mengqi Wang Y.T.Feng +1 位作者 Shaoheng Guan Tongming Qu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第6期2198-2218,共21页
One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural ne... One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials. 展开更多
关键词 Granular materials History-dependence multi-layer perceptron(MLP) Discrete element method FEM-DEM Machine learning
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Digital modulation classification using multi-layer perceptron and time-frequency features
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作者 Yuan Ye Mei Wenbo 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第2期249-254,共6页
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio... Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals: The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio (SNR) variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron (MLP) classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier. 展开更多
关键词 Digital modulation classification Time-frequency feature Time-frequency distribution multi-layer perceptron.
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Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network 被引量:10
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作者 Bhatawdekar Ramesh Murlidhar Hoang Nguyen +4 位作者 Jamal Rostami XuanNam Bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2021年第6期1413-1427,共15页
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t... In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models. 展开更多
关键词 Flyrock Harris hawks optimization(HHO) multi-layer perceptron(MLP) Random forest(RF) Support vector machine(SVM) Whale optimization algorithm(WOA)
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Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data 被引量:2
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作者 Lun Gao Ran-Hong Xie +2 位作者 Li-Zhi Xiao Shuai Wang Chen-Yu Xu 《Petroleum Science》 SCIE CAS CSCD 2022年第2期570-580,共11页
In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and ca... In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%. 展开更多
关键词 Low-resistivity-low-contrast(LRLC)pay zones Conventional well logging Machine learning DBSCAN algorithm multi-layer perceptron
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Prediction of Logistics Demand via Least Square Method and Multi-Layer Perceptron 被引量:1
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作者 WEI Leqin ZHANG Anguo 《Journal of Donghua University(English Edition)》 EI CAS 2020年第6期526-533,共8页
To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross ... To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy. 展开更多
关键词 logistics demand least square method(LSM) multi-layer perceptron(MLP) PREDICTION strategic planning
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Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies 被引量:1
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作者 Patrice Wira Thien Minh Nguyen 《Journal of Electrical Engineering》 2017年第5期219-230,共12页
This main contribution of this work is to propose a new approach based on a structure of MLPs (multi-layer perceptrons) for identifying current harmonics in low power distribution systems. In this approach, MLPs are... This main contribution of this work is to propose a new approach based on a structure of MLPs (multi-layer perceptrons) for identifying current harmonics in low power distribution systems. In this approach, MLPs are proposed and trained with signal sets that arc generated from real harmonic waveforms. After training, each trained MLP is able to identify the two coefficients of each harmonic term of the input signal. The effectiveness of the new approach is evaluated by two experiments and is also compared to another recent MLP method. Experimental results show that the proposed MLPs approach enables to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. The new approach can be applied in harmonic compensation strategies with an active power filter to ensure power quality issues in electrical power systems. 展开更多
关键词 Power quality harmonic identification MLP multi-layer perceptron Fourier series active power filtering.
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Building up Multi-Layered Perceptrons as Classifier System for Decision Support
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作者 Cat Jun, Zhai Fan & Feng Shan (Inst. of Sys. Eng., Huazhong University of Science and Technology, Wuhan 430074, China) 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 1995年第2期32-39,共8页
This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configura... This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented. 展开更多
关键词 multi-layered perceptron Decision support system classification ability SELF-CONFIGURATION Comprehensive evaluation.
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Implementing Semantic Deduction of Propositional Knowledge in an Extension Multi-layer Perceptron
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作者 HUANG Tian-min,PEI Zheng (Department of Applied Mathematics, Southwest Jiaotong Universi ty,Chengdu 610031,China) 《Chinese Quarterly Journal of Mathematics》 CSCD 2003年第3期247-257,共11页
The paper presents an extension multi-laye r p erceptron model that is capable of representing and reasoning propositional know ledge base. An extended version of propositional calculus is developed, and its some prop... The paper presents an extension multi-laye r p erceptron model that is capable of representing and reasoning propositional know ledge base. An extended version of propositional calculus is developed, and its some properties is discussed. Formulas of the extended calculus can be expressed in the extension multi-layer perceptron. Naturally, semantic deduction of prop ositional knowledge base can be implement by the extension multi-layer perceptr on, and by learning, an unknown formula set can be found. 展开更多
关键词 multi-layer perceptron extension multi-layer perce p tron propositional calculus propositional knowledge buse semantic deduction
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DM-L Based Feature Extraction and Classifier Ensemble for Object Recognition
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作者 Hamayun A. Khan 《Journal of Signal and Information Processing》 2018年第2期92-110,共19页
Deep Learning is a powerful technique that is widely applied to Image Recognition and Natural Language Processing tasks amongst many other tasks. In this work, we propose an efficient technique to utilize pre-trained ... Deep Learning is a powerful technique that is widely applied to Image Recognition and Natural Language Processing tasks amongst many other tasks. In this work, we propose an efficient technique to utilize pre-trained Convolutional Neural Network (CNN) architectures to extract powerful features from images for object recognition purposes. We have built on the existing concept of extending the learning from pre-trained CNNs to new databases through activations by proposing to consider multiple deep layers. We have exploited the progressive learning that happens at the various intermediate layers of the CNNs to construct Deep Multi-Layer (DM-L) based Feature Extraction vectors to achieve excellent object recognition performance. Two popular pre-trained CNN architecture models i.e. the VGG_16 and VGG_19 have been used in this work to extract the feature sets from 3 deep fully connected multiple layers namely “fc6”, “fc7” and “fc8” from inside the models for object recognition purposes. Using the Principal Component Analysis (PCA) technique, the Dimensionality of the DM-L feature vectors has been reduced to form powerful feature vectors that have been fed to an external Classifier Ensemble for classification instead of the Softmax based classification layers of the two original pre-trained CNN models. The proposed DM-L technique has been applied to the Benchmark Caltech-101 object recognition database. Conventional wisdom may suggest that feature extractions based on the deepest layer i.e. “fc8” compared to “fc6” will result in the best recognition performance but our results have proved it otherwise for the two considered models. Our experiments have revealed that for the two models under consideration, the “fc6” based feature vectors have achieved the best recognition performance. State-of-the-Art recognition performances of 91.17% and 91.35% have been achieved by utilizing the “fc6” based feature vectors for the VGG_16 and VGG_19 models respectively. The recognition performance has been achieved by considering 30 sample images per class whereas the proposed system is capable of achieving improved performance by considering all sample images per class. Our research shows that for feature extraction based on CNNs, multiple layers should be considered and then the best layer can be selected that maximizes the recognition performance. 展开更多
关键词 DEEP Learning Object Recognition CNN DEEP multi-layer Feature Extraction Principal Component Analysis classifier ENSEMBLE Caltech-101 BENCHMARK Database
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Recommendation System Based on Perceptron and Graph Convolution Network
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作者 Zuozheng Lian Yongchao Yin Haizhen Wang 《Computers, Materials & Continua》 SCIE EI 2024年第6期3939-3954,共16页
The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combinatio... The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms. 展开更多
关键词 Recommendation system graph convolution network attention mechanism multi-layer perceptron
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Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar 被引量:5
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作者 ZHOU Hui-lin TIAN Mao CHEN Xiao-li 《Wuhan University Journal of Natural Sciences》 EI CAS 2005年第6期1009-1012,共4页
Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper ... Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented. 展开更多
关键词 ground penetrating radar nonstationary signal dyadic wavelet transform feed-forward multi-layer perceptron back propagation algorithm
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Environmental Sound Classification Using Deep Learning 被引量:7
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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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Static Digits Recognition Using Rotational Signatures and Hu Moments with a Multilayer Perceptron 被引量:1
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作者 Francisco Solís Margarita Hernández +1 位作者 Amelia Pérez Carina Toxqui 《Engineering(科研)》 2014年第11期692-698,共7页
This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and f... This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and frequency domains, and color space transformations. First system used rotational signatures based on a correlation operator;minimum distance was used for the classification task. Second system computed the seven Hu invariants from binary images;these descriptors fed to a Multi-Layer Perceptron (MLP) in order to recognize the 9 different classes. First system achieves 100% of recognition rate with leaving-one-out validation and second experiment performs 96.7% of recognition rate with Hu moments and 100% using 36 normalized moments and k-fold cross validation. 展开更多
关键词 SIGN Language Recognition ROTATIONAL SIGNATURES HU MOMENTS multi-layer perceptron
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Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron
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作者 D.Elangovan V.Subedha 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期2797-2808,共12页
The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Face... The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy). 展开更多
关键词 Firefly algorithm feature selection feature extraction multi-layer perceptron automatic sentiment analysis
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Classification and Identification of Nuclear, Biological or Chemical Agents Taken from Remote Sensing Image by Using Neural Network
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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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A hybrid constriction coefficientbased particle swarm optimization and gravitational search algorithm for training multi-layer perceptron 被引量:2
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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)
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Camera-Radar Fusion Sensing System Based on Multi-Layer Perceptron 被引量:1
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作者 YAO Tong WANG Chunxiang QIAN Yeqiang 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第5期561-568,共8页
Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems... Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fusion methods.To solve these issues,we proposed a machine learning-based fusion sensing system that uses a camera and radar,and that can be used in intelligent vehicles.First,the object detection algorithm is used to detect the image obtained by the camera;in sequence,the radar data is preprocessed,coordinate transformation is performed,and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed.The proposed fusion sensing system was verified by comparative experiments in a real-world environment.The experimental results show that the system can effectively integrate camera and radar data results,and obtain accurate and comprehensive object information in front of intelligent vehicles. 展开更多
关键词 intelligent vehicle environmental perception system sensor fusion multi-layer perceptron
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Prediction of diabetes and hypertension using multi-layer perceptron neural networks 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
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Reconstructing shock front of unstable detonations based on multi-layer perceptron
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作者 Lin Zhou Honghui Teng +2 位作者 Hoi Dick Ng Pengfei Yang Zonglin Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2021年第11期1610-1623,I0001,共15页
The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research.Advanced laser-optical visualization of detonati... The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research.Advanced laser-optical visualization of detonation structure may provide certain information of its reactive front,but the corresponding lead shock needs to be reconstructed building the complete flow field.Using the multi-layer perceptron(MLP)approach,we propose a shock front reconstruction method which can predict evolution of the lead shock wavefront from the state of the reactive front.The method is verified through the numerical results of one-and two-dimensional unstable detonations based on the reactive Euler equations with a one-step irreversible chemical reaction model.Results show that the accuracy of the proposed method depends on the activation energy of the reactive mixture,which influences prominently the cellular detonation instability and hence,the distortion of the lead shock surface.To select the input variables for training and evaluate their influence on the effectiveness of the proposed method,five groups,one with six variables,and the other with four variables,are tested and analyzed in the MLP model.The trained MLP is tested in the cases with different activation energies,demonstrates the inspiring generalization capability.This paper offers a universal framework for predicting detonation frontal evolution and provides a novel way to interpret numerical and experimental results of detonation waves. 展开更多
关键词 Cellular detonation Lead shock evolution multi-layer perceptron Numerical simulations
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