Analysis Of Neural Network Models Research Articles

Data-driven approaches for predicting performance degradation of solid oxide fuel cells system considering prolonged operation and shutdown accumulation effect

Solid oxide fuel cell system is widely acknowledged as the leading alternative energy generation system in the field. Due to their high efficiency, low emissions, low noise, and various other advantages, solid oxide fuel cell systems are being considered for use in automobiles as a replacement for traditional internal combustion engines. However, prolonged operation and abnormal shutdowns can lead to performance degradation, which affects the efficiency, stability, and lifespan of stack. In the context of prolonged operation, considering the fluctuations in stack performance parameters and balance of plant, several regression models based on voltage parameters are established to accurately predict changes in stack performance. The results reveal that the genetic algorithm optimized backpropagation neural network model is highly sensitive in predicting the system performance degradation. Further analysis reveals that abnormal shutdowns can cause system performance fluctuations. As a result, the number and duration of shutdowns are incorporated into genetic algorithm optimized backpropagation neural network model for analysis. This model exhibits the best performance degradation evolution prediction compared to the long short-term memory and particle swarm optimization backpropagation neural network methods. This finding is important for monitoring and controlling the operational status of stack.

Correction: Designing of Enhanced Deep Neural Network Model for Analysis and Identification of Kidney Stone, Cyst, and Tumour

Correction: Designing of Enhanced Deep Neural Network Model for Analysis and Identification of Kidney Stone, Cyst, and Tumour

Designing of Enhanced Deep Neural Network Model for Analysis and Identification of Kidney Stone, Cyst, and Tumour

Designing of Enhanced Deep Neural Network Model for Analysis and Identification of Kidney Stone, Cyst, and Tumour

Psychological effect of online games on adults through deep neural network analysis

The online gaming industry is now expanding, and under the guise of this, a problem that cannot be ignored is online gaming addiction among adults. Adults with severe addiction problems often spend money and sabotage their lives, careers, and relationships. Society must pay attention to and study the adult online gaming addiction problem and find corresponding solutions. Advances in deep neural networks have opened up new horizons for studying adult online gaming addiction. Through deep neural network analysis, the reasons why adults are addicted to online games can be better explained. Under the above background, in order to solve the problem of adult game addiction, this study investigated the current situation of adult online game addiction, and established a deep neural network model for analysis to find out the deep reasons and feasible solutions. According to the calculation results of the model, solutions are proposed from the perspectives of the game itself, game players, players’ families and society. The solution emphasizes the self-regulation of the game itself, the player’s self-control and external help.

A Multimodal Convolutional Neural Network Model for the Analysis of Music Genre on Children's Emotions Influence Intelligence.

This paper designs a multimodal convolutional neural network model for the intelligent analysis of the influence of music genres on children's emotions by constructing a multimodal convolutional neural network model and profoundly analyzing the impact of music genres on children's feelings. Considering the diversity of music genre features in the audio power spectrogram, the Mel filtering method is used in the feature extraction stage to ensure the effective retention of the genre feature attributes of the audio signal by dimensional reduction of the Mel filtered signal, deepening the differences of the extracted features between different genres, and to reduce the input size and expand the model training scale in the model input stage, the audio power spectrogram obtained by feature extraction is cut the MSCN-LSTM consists of two modules: multiscale convolutional kernel convolutional neural network and long and short term memory network. The MSCNN network is used to extract the EEG signal features, the LSTM network is used to remove the temporal characteristics of the eye-movement signal, and the feature fusion is done by feature-level fusion. The multimodal signal has a higher emotion classification accuracy than the unimodal signal, and the average accuracy of emotion quadruple classification based on a 6-channel EEG signal, and children's multimodal signal reaches 97.94%. After pretraining with the MSD (Million Song Dataset) dataset in this paper, the model effect was further improved significantly. The accuracy of the Dense Inception network improved to 91.0% and 89.91% on the GTZAN dataset and ISMIR2004 dataset, respectively, proving that the Dense Inception network's effectiveness and advancedness of the Dense Inception network were demonstrated.

Weight interpretation of artificial neural network model for analysis of rice (Oryza sativa L.) with near-infrared spectroscopy.

Prediction models for major nutrients of rice were built using near-infrared (NIR) spectral data based on the artificial neural network (ANN). Scientific interpretation of the weight values was proposed and performed to understand the wavenumbers contributing to the prediction of nutrients. NIR spectra were acquired from 110 rice samples. Carbohydrate and moisture contents were predicted with values for the determination coefficient, relative root mean square error, range error ratio, and residual prediction deviation of 0.98, 0.11 %, 44, and 7.3, and 0.97, 0.80 %, 27, and 5.8, respectively. The results agreed well with ones reported in the previous studies and acquired by the conventional partial least squares (PLS)-variable importance in projection method. This study demonstrates that the combination of NIR and ANN is a powerful and accurate tool to monitor nutrients of rice and scientific interpretation of weights can be performed to overcome black box nature of the ANN.

AAPFE: Aligned Assembly Pre-Training Function Embedding for Malware Analysis

The use of natural language processing to analyze binary data is a popular research topic in malware analysis. Embedding binary code into a vector is an important basis for building a binary analysis neural network model. Current solutions focus on embedding instructions or basic block sequences into vectors with recurrent neural network models or utilizing a graph algorithm on control flow graphs or annotated control flow graphs to generate binary representation vectors. In malware analysis, most of these studies only focus on the single structural information of the binary and rely on one corpus. It is difficult for vectors to effectively represent the semantics and functionality of binary code. Therefore, this study proposes aligned assembly pre-training function embedding, a function embedding scheme based on a pre-training aligned assembly. The scheme creatively applies data augmentation and a triplet network structure to the embedding model training. Each sub-network extracts instruction sequence information using the self-attention mechanism and basic block graph structure information with the graph convolution network model. An embedding model is pre-trained with the produced aligned assembly triplet function dataset and is subsequently evaluated against a series of comparative experiments and application evaluations. The results show that the model is superior to the state-of-the-art methods in terms of precision, precision ranking at top N (p@N), and the area under the curve, verifying the effectiveness of the aligned assembly pre-training and multi-level information extraction methods.

Optimized Sequence Dataset Generation for a Two-Stage Pipelined Microcontroller Instruction-Level Electromagnetic Information Leakage Analysis

With the prevalence of the Internet of Things (IoT) and microcontrollers (MCUs), the security issues of IoT and MCUs have become increasingly important. Side-channel analysis (SCA) is a major threat to such problems. One of the non-invasive SCAs is through electromagnetic information leakage (EM-leak) analysis. The author has developed a machine-instruction-level EM-leak analysis by neural network (NN) model. The NN model analysis need a large dataset for training and validation. And the dataset should be complete and sufficient. The dataset sufficiency can be achieved by acquiring more data from the already proposed EM-leak measurement platform. However, the completeness issue becomes a challenge due to the pipelined architecture in the target MCU. In this paper, the completeness issue of a NN model dataset for the EM-leak analysis is addressed. The issue is to be solved in several steps. First, it is simplified to a two-stage pipelined MCU individual EM-leak analysis sequence problem (2-EMAseq). The problem is then proved that at least one optimal solution exists. And an algorithm is proposed based on the proof to find an optimal solution (a complete EM-leak NN dataset). Experiments show that the proposed algorithm can find an optimal solution. The contributions of this paper include: it successfully reduces a real problem to a 2-EMAseq description, proves that the 2-EMAseq problem has at least one optimal solution, uses this proof to develop an algorithm, and uses this algorithm to find a complete dataset is the target two-stage pipelined MCU dataset for NN model training/validation. Currently, the algorithm can only generate the optimal EM-dataset for two-stage pipelined MCUs. However, there are many MCUs with 4~8 pipeline stages. Whether there is an optimal solution when the stages are more than two is still an open question. And it needs to find proofs or derive heuristics in the future for such MCUs.

Psychological health analysis based on fuzzy assisted neural network model for sports person

Stress is indeed a life aspect that influences everyone, even though athletes seem to suffer from it one step ahead of others because of the extent they are expected to balance between coursework, workouts, and competitions, along with everyday life and family stress. Therefore, an efficient psychological health analysis for sportspersons is crucial in sports training. This paper introduces a Fuzzy-assisted Neural Network model for Psychological Health Analysis (FNN-PHA) to assess mental stress by monitoring the Electro Cardio Gram signal (ECG), Electroencephalogram (EEG), and Pulse rate. This paper integrates the fuzzy assisted Petri nets, fuzzy assisted k-complex detector, and fuzzy assisted transient time analyzer to ensure the psychological health analysis neural network model’s adaptive performance. The strength of the proposed fuzzy model demonstrates interpretability against the accuracy of different criteria. The simulation analysis shows that the FNN-PHA model enhances the prediction ratio of 98.7%, emotional stability of 96.7%, personal growth of 95.7%, physical fitness level of 97.8%, and depression ratio of 12.5% when compared to other existing models.

Matters of Neural Network Repository Designing for Analyzing and Predicting of Spatial Processes

The article is devoted to solving the scientific problem of accumulating and systematizing models and machine learning algorithms by developing a repository of deep neural network models for analyzing and predicting of spatial processes in order to support the process of making managerial decisions in the field of ensuring conditions for sustainable development of regions. The issues of architecture development and software implementation of a repository of deep neural network models for spatial data analysis are considered, based on a new ontological model, which makes it possible to systematize models in terms of their application for solving design problems. An ontological model of a deep neural network repository for spatial data analysis is decomposed into the domain of deep machine learning models, problems being solved and data. Special attention is paid to the problems of storing data in the repository and the development of a subsystem for visualizing neural networks using a graph model. The authors have shown that for organizing a repository of deep neural network models, it is advisable to use a scientifically grounded set of database management systems integrated into a multi-model storage, characterizing the domains of using relational and NoSQL storages.

Recursive neural network model for analysis and forecast of PM10 and PM2.5

Atmospheric particulate matter (PM) is one of the pollutant that may have a significant impact on human health. Data collected during three years in an urban area of the Adriatic coast are analysed using three models: a multiple linear regression model, a neural network model with and without recursive architecture. Measured meteorological parameters and PM10 concentration are used as input to forecast the daily averaged concentration of PM10 from one to three days ahead. All simulations show that the neural network with recursive architecture has better performances compared to both the multiple linear regression model and the neural network model without the recursive architecture. Results of PM forecasts are compared with the air quality limits for health protection to test the performance as operational tool. The inclusion of carbon monoxide (CO) concentration as further input parameter in the model, has been evaluated in terms of forecast improvements. Finally, all models are used to forecast the PM2.5 concentration, using as input the meteorological data, the PM10 and CO concentration, to simulate the situation when PM2.5 is not observed. The comparison between observed and forecasted PM2.5 shows that the neural network is able to forecast the PM2.5 concentrations even if PM2.5 is not included among the input parameters.

Assessing the Effect of Quantitative and Qualitative Predictors on Gastric Cancer Individuals Survival Using Hierarchical Artificial Neural Network Models

BackgroundThere are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qualitative dichotomous variables, and results of using discrete and continuous quantitative, ordinal, or multinomial categorical predictive variables in these models are not well understood in comparison to conventional models.ObjectivesThis study was designed and conducted to examine the application of these models in order to determine the survival of gastric cancer patients, in comparison to the Cox proportional hazards model.Patients and MethodsWe studied the postoperative survival of 330 gastric cancer patients who suffered surgery at a surgical unit of the Iran Cancer Institute over a five-year period. Covariates of age, gender, history of substance abuse, cancer site, type of pathology, presence of metastasis, stage, and number of complementary treatments were entered in the models, and survival probabilities were calculated at 6, 12, 18, 24, 36, 48, and 60 months using the Cox proportional hazards and neural network models. We estimated coefficients of the Cox model and the weights in the neural network (with 3, 5, and 7 nodes in the hidden layer) in the training group, and used them to derive predictions in the study group. Predictions with these two methods were compared with those of the Kaplan-Meier product limit estimator as the gold standard. Comparisons were performed with the Friedman and Kruskal-Wallis tests.ResultsSurvival probabilities at different times were determined using the Cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the Kaplan-Meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between Cox and Kaplan-Meier (P < 0.05) and no significant difference between Cox and the neural network, and the neural network and the standard (Kaplan-Meier), as well as better accuracy for the neural network (with 3 nodes in the hidden layer). Probabilities of survival were calculated using three neural network models with 3, 5, and 7 nodes in the hidden layer, and it has been observed that none of the predictions was significantly different from results with the Kaplan-Meier method and they appeared more comparable towards the last months (fifth year). However, we observed better accuracy using the neural network with 5 nodes in the hidden layer. Using the Cox proportional hazards and a neural network with 3 nodes in the hidden layer, we found enhanced accuracy with the neural network model.ConclusionsNeural networks can provide more accurate predictions for survival probabilities compared to the Cox proportional hazards mode, especially now that advances in computer sciences have eliminated limitations associated with complex computations. It is not recommended in order to adding too many hidden layer nodes because sample size related effects can reduce the accuracy. We recommend increasing the number of nodes to a point that increased accuracy continues (decrease in mean standard error), however increasing nodes should cease when a change in this trend is observed.

An artificial neural network model for analysis and synthesis of pianists’ performance styles

A model for synthesis and analysis of professional pianists’ performance styles by means of artificial neural networks (ANNs) is presented. The style of pianists was synthesized using Bruno Repp’s measurements [J. Acoust. Soc. Am. 92, 2546–2568 (1992)] of Schumann’s Trumerei as performed by 24 famous pianists. It will be shown that, by using significant score parameters, ANN can learn and reproduce the style of a pianist at a local level. The behavior of the trained ANN and the performance characteristics of the pianists were analyzed by running the trained ANN in a special way. All ANN input neurones were locked to fixed values, except one or two neurones whose input values were allowed to change in time. For example, if one of these neurones is related to the duration of the current note, the output of the ANN gives a curve showing expressive deviations produced by the ANN, when the duration of the note is varying. In this way it is possible to extract behavior rules of performing action of the ANN, and compare them with the performance style of pianists as well as of rule-based performance systems. The model is developed in the Java language and is available on the Internet.

A neural network model for analysis of prognostic factors in congestive heart failure

A neural network model for analysis of prognostic factors in congestive heart failure

A neural network model for analysis and optimization of processes

Artificial neural networks based on a feedforward architecture and trained bv the backpropagation technique were applied to analysis and improvement of a separation process. Various neural network topologies have been tested and compared. The Powell method was used to train the network by minimising the sum of squares of residuals as well as GDR algorithm. The performance of the network have been analysed. The obtained results show the applicability the neural networks structure with and without hidden layer. These results ilustrate the feasibility of using a neural network as a data analyser and as an optimisation tool.

A neural network model for analysis and optimization of processes

A neural network model for analysis and optimization of processes