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An Enhanced Task Migration Technique Based on Convolutional Neural Network in Machine Learning Framework 认领 引用
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作者 Hamayun Khan Muhammad Atif Imtiaz +5 位作者 Hira Siddique Muhammad Tausif Afzal Rana Arshad Ali Muhammad Zeeshan Baig Saif ur Rehman Yazed Alsaawy 《Computer Systems Science & Engineering》 2025年第1期317-331,共15页
The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address th... The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling(EA-EDF).ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system.The proposed system model allocates processors to the ready task set in such a way that their deadlines are guaranteed.A full task migration policy is also integrated to ensure proper task mapping that ensures inter-process linkage among the arrived tasks with the same deadlines.The execution of a task can halt on one CPU and reschedule the execution on a different processor to avoid delay and ensure meeting the deadline.Our approach shows promising potential for machine-learning-based schedulability analysis enables a comparison between different ML models and shows a promising reduction in energy as compared with other ML-aware task migration techniques for SoC like Multi-Layer Feed-Forward Neural Networks(MLFNN)based on convolutional neural network(CNN),Random Forest(RF)and Deep learning(DL)algorithm.The Simulations are conducted using super pipelined microarchitecture of advanced micro devices(AMD)XScale PXA270 using instruction and data cache per core 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors(ui)12%,31%and 50%.The proposed approach consumes 5.3%less energy when almost half of the CPU is running and on a lower workload consumes 1.04%less energy.The proposed design accumulatively gives significant improvements by reducing the energy dissipation on three clock rates by 4.41%,on 624 MHz by 5.4%and 5.9%on applications operating on 416 and 312 MHz standard operating frequencies. 展开更多
关键词 Convolutional neural network(CNN) energy conversation dynamic thermal management optimization methods ANN multiprocessor systems-on-chips artificial neural networks artificial intelligence multi-layer feed-forward neural network(MLFNN) random forest(RF)and deep learning(DL)
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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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Recommendation System Based on Perceptron and Graph Convolution Network 认领 引用
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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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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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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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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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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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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
基于特征融合的神经影像数据分类模型 认领 引用
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作者 李修军 王圆圆 +2 位作者 葛雄心 杨菁菁 佟丹 《吉林大学学报（工学版）》 EI CAS CSCD 北大核心 2026年第5期1407-1417,共11页
为了解决三维卷积神经网络(3D CNN)在神经影像数据分类预测中难以捕捉数据全局特征的问题,引入有效建模全局关系的多层感知混合器(MLP-Mixer),提出了一种基于特征融合的混合模型,通过结合3D CNN和MLP-Mixer的优势,提高对神经影像数据的... 为了解决三维卷积神经网络(3D CNN)在神经影像数据分类预测中难以捕捉数据全局特征的问题,引入有效建模全局关系的多层感知混合器(MLP-Mixer),提出了一种基于特征融合的混合模型,通过结合3D CNN和MLP-Mixer的优势,提高对神经影像数据的分类性能。首先,在特征提取阶段,将3D CNN用于从静息态功能性磁共振成像(rs-fMRI)数据中提取局部空间特征,将MLP-Mixer用于捕捉全局关系特征;其次,在融合分类模块,采用缩放点积自注意力技术结合空间特征和全局关系特征,动态地权衡和整合不同特征的重要性,增强模型的表达能力,并通过全连接层完成分类任务。实验结果显示,该混合模型在处理神经影像数据的二分类和多分类任务时,分类准确率分别提高了4.12%和5.16%,在召回率、F1分数等指标上也显著优于其他网络模型,表明本文混合模型能有效提升分类准确性,为神经影像数据的自动化分类提供了一种新的技术途径,具有广泛的工程应用潜力。 展开更多
关键词 计算机技术 三维卷积神经网络 多层感知混合器 功能性磁共振成像 特征融合
基于神经网络与密度峰值聚类的岩体结构面智能识别方法 认领 引用
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作者 陈卓 张晓平 +3 位作者 解玄 刘勇斌 王坤 苏鹏 《隧道建设（中英文）》 EI CSCD 北大核心 2026年第2期359-370,共12页
为解决现有算法在处理不同特征点云时存在普适性不足、效率低下、难以应用于工程实际的问题,提出一种基于神经网络的岩体结构面智能识别方法,具体包括4个步骤。首先,对原始点云进行标准化预处理操作,并人工选取具有代表性的特征区域,以... 为解决现有算法在处理不同特征点云时存在普适性不足、效率低下、难以应用于工程实际的问题,提出一种基于神经网络的岩体结构面智能识别方法,具体包括4个步骤。首先,对原始点云进行标准化预处理操作,并人工选取具有代表性的特征区域,以构建高质量的训练样本集;其次,采用CFSFDP(clustering by fast search and find of density peaks)聚类算法为样本生成标签;再次,构建并训练多层感知机(multilayer perceptron,MLP)模型和多层卷积神经网络(multi-layer convolutional neural network,MCNN)模型,输入全尺度点云的点法向量进行结构面粗识别,并对2种模型进行比选分析;最后,使用HDBSCAN(hierarchical density-based spatial clustering of applications with noise)算法对分类结果进行细化与产状计算。结果表明:1)采用多层感知机模型处理简单结构面时具有较高的处理速度,而卷积神经网络模型在处理复杂、非均匀点云时展现出更高的分类精度。2)与聚类方法相比,该方法计算时间提升25%~50%,能够有效解决传统算法无法适用于不同复杂点云的问题,且具有很强的鲁棒性。 展开更多
关键词 三维点云 岩体结构面 多层感知机模型 卷积神经网络 智能识别
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用于短文本分类的BLSTM_MLPCNN模型 认领 引用 被引量：12
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作者 郑诚 洪彤彤 薛满意 《计算机科学》 CSCD 北大核心 2019年第6期206-211,共6页
文本表示和文本特征提取是自然语言处理的基础工作,直接影响文本分类的性能。文中提出了以字符级向量联合词向量作为输入的BLSTM_MLPCNN神经网络模型。该模型首先将卷积神经网络(CNN)作用于字符以获取字符级向量,并将字符级向量联合词... 文本表示和文本特征提取是自然语言处理的基础工作,直接影响文本分类的性能。文中提出了以字符级向量联合词向量作为输入的BLSTM_MLPCNN神经网络模型。该模型首先将卷积神经网络(CNN)作用于字符以获取字符级向量,并将字符级向量联合词向量作为预训练词嵌入向量,也即双向长短时记忆网(BLSTM)模型的输入;然后联合BLSTM模型的前向输出、词嵌入向量、后向输出构成文档特征图;最后利用多层感知器卷积神经网络(MLPCNN)进行特征提取。在相关数据集上的实验结果表明:相比于CNN,RNN以及CNN与RNN的组合模型,BLSTM_MLPCNN模型具有更优的分类性能。 展开更多
关键词 字符级向量 词向量 卷积神经网络(CNN) 双向长短时记忆神经网络(BLSTM) 多层感知器(MLP) 多层感知器卷积网络(MLPCNN)
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基于Network In Network网络结构的高光谱影像分类方法 认领 引用 被引量：7
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作者 高奎亮 张鹏强 +2 位作者 余旭初 谭熊 刘冰 《测绘科学技术学报》 北大核心 2019年第5期500-504,510,共5页
为进一步提高高光谱影像分类精度,通过引入Network In Network网络结构,构建了一种新的网络模型。该网络模型能够对局部感受野内的数据进行更加抽象的建模,从而能够对影像中的空谱联合特征进行更为抽象的表达。通过在Pavia University和... 为进一步提高高光谱影像分类精度,通过引入Network In Network网络结构,构建了一种新的网络模型。该网络模型能够对局部感受野内的数据进行更加抽象的建模,从而能够对影像中的空谱联合特征进行更为抽象的表达。通过在Pavia University和Indian Pines两个数据集上进行验证,实验结果表明,所构建的网络模型能够有效提高分类精度,在减少训练样本的条件下仍具有较好的分类性能。 展开更多
关键词 高光谱影像分类 卷积神经网络 网络中的网络 Mlpconv层 多层感知器
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基于多模态特征融合的可解释性脂质纳米颗粒转染效率预测模型 认领 引用
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作者 庞国君 林邦姜 +2 位作者 郑博文 陈剑 徐博辉 《计算机应用研究》 CSCD 北大核心 2026年第4期1054-1060,共7页
近年来,针对药物递送系统脂质纳米颗粒(LNPs)的设计研究大多仅聚焦于单一特征,且忽视了辅助脂质的作用。该研究旨在构建一种基于多模态特征融合的可解释性深度学习模型(MolGraphNet),用于辅助脂质转染效率的高精度预测与结构可解释分析... 近年来,针对药物递送系统脂质纳米颗粒(LNPs)的设计研究大多仅聚焦于单一特征,且忽视了辅助脂质的作用。该研究旨在构建一种基于多模态特征融合的可解释性深度学习模型(MolGraphNet),用于辅助脂质转染效率的高精度预测与结构可解释分析。MolGraphNet以神经网络卷积(NNConv)提取分子结构图特征,以多层感知机(MLP)提取物理化学相关的数值特征,并通过多模态特征融合器实现结构与性质信息的深度整合。模型还结合了分子结构编码技术与SHAP分析法实现对关键特征的可解释性分析与可视化,揭示辅助脂质结构与转染效率之间的内在关联。在公开数据集中六种细胞类型任务上的结果显示,MolGraphNet的预测误差为6%~11%,皮尔森和斯皮尔曼相关系数在0.75~0.92,显著优于基线模型。MolGraphNet不仅具有较高的预测精度和鲁棒性,还能通过SHAP分析等方法实现关键特征的可解释可视化,为辅助脂质的设计和LNPs配方的优化提供了有力的指导和参考。 展开更多
关键词 多模态特征融合 NNConv MLP 脂质纳米颗粒 SHAP 模型可解释性
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基于“传统模型+神经网络”的无创产前检测技术研究 认领 引用
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作者 喇秉军 普措才仁 +1 位作者 张莉 伍欢欢 《西北民族大学学报（自然科学版）》 2026年第2期60-70,共11页
针对解决无创产前检测(NIPT)男胎胎儿Y染色体信号比例Cy预测假阴性率高、女胎非整倍体跨指标关联难的问题,为进一步提升检测精度与临床适用性,构建“传统模型+神经网络”混合检测模型.通过广义可加模型(GAM)结合多层感知机(MLP)优化男... 针对解决无创产前检测(NIPT)男胎胎儿Y染色体信号比例Cy预测假阴性率高、女胎非整倍体跨指标关联难的问题,为进一步提升检测精度与临床适用性,构建“传统模型+神经网络”混合检测模型.通过广义可加模型(GAM)结合多层感知机(MLP)优化男胎胎儿Y染色体信号比例预测,以自编码器+强化学习确定BMI分组及时点;女胎则通过多变量逻辑回归量化Z值风险,用CNN-MLP串联模型提取跨染色体关联.男胎假阴性风险控制在3.5%以内,MLP调整后R2=0.142;女胎CNN-MLP模型AUC为0.953,异常样本羊水穿刺结果的一致率为94.5%,其中Z21SHAP贡献占比38.5%.该混合检测模型兼顾精度与可解释性,有效解决NIPT临床检测难点,具备良好应用前景. 展开更多
关键词 无创产前检测 广义可加模型 多层感知机(MLP) 卷积神经网络(CNN) 强化学习
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