Neural network analysis of mammary gland thermograms using the estimate of fractal dimension in field

2020 ◽  
Vol 15 (16) ◽  
pp. 28-34
Author(s):  
Y. E. Liakh ◽  
Keyword(s):  
Neural Network ◽  
Fractal Dimension ◽  
Mammary Gland ◽  
Linear Model ◽  
Pathological Process ◽  
Field Size ◽  
Field Temperature ◽  
Hidden Layer ◽  
Mlp Model ◽  
Multifactor Regression

Introduction. Thermography is one of the promising additional standard methods of mammary glands screening in a large group of population. This method is considered to be suitable for widespread use due to its non-invasiveness, lack of radiation exposure and thus safety for the health of patients, accessibility to patients and high detection effectiveness of pathological changes of the mam-mary gland. Methods of thermograms evaluation and analysis. To identify the risk of mammary gland pathology we analyzed thermograms using 68 features, among which three indicators of general characteristics: age of the patient, minimal temperature of theMG field, size of the MG temperature field; 32 features of the relative area of temperature rise; and 33 features of thermograms characteris-tics according to Hurst exponent of high dimensional fractals. To analyze distribution of MG field temperature and to identify signs of thermograms associated with the risk of pathology, methods of constructing one-factor and multifactor regression models were used, as well as method of operating characteristic curves (ROC). Quantitative analysis of the thermography results. On the basis of the selected factor signs, a linear model for predicting the risk of MG pathology was built — AUC = 0,85 (95% CI 0,82–0,87) and a nonlinear model (was used a multilayer perceptron — MLP, with one hidden layer with sigmoid activation functions) for predicting the risk of MG pathology AUC = 0,89 (95% CI 0,87–0,92). A non-linear neural network model on a reduced set of traits had better (p < 0,05) prognostic characteristics (AUC) than a linear model on all 68 features or a linear model on significant factor features. The prognostic characteristics of the MLP model allow to use it in order to predict the risk of a pathological process. Conclusions. To analyze mammary gland thermograms with assessment of the fractal dimension of the field temperature distribu-tion in norm and in pathology was constructed a neural network MLP model for predicting the risk of MG pathology. Sensitivity of the proposed model is 90,2% (95% CI 86,7%–93,0%), specificity — 85,1% (95% CI 80,6%–88,9). Key words: Thermograms; Mammary gland; Fractal dimension; MG temperature; One-wayanalysis of variance; MLP model.

scholarly journals Application of Artificial Neural Network in Simulation of Supercritical Extraction of Valerenic Acid from Valeriana officinalis L.

2012 ◽  
Vol 2012 ◽  
pp. 1-7
Author(s):  
Amir Rabiee Kenaree ◽  
Shohreh Fatemi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supercritical Extraction ◽  
The Third ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
Valerenic Acid ◽  
Hidden Layer ◽  
Dynamic Time ◽  
Mlp Model

Application of artificial neural network (ANN) has been studied for simulation of the extraction process by supercritical CO2. Supercritical extraction of valerenic acid from Valeriana officianalis L. has been studied and simulated according to the significant operational parameters such as pressure, temperature, and dynamic extraction time. ANN, using multilayer perceptron (MLP) model, is employed to predict the amount of extracted VA versus the studied variables. Three tests, validation, and training data sets in three various scenarios are selected to predict the amount of extracted VA at dynamic time of extraction, working pressure, and temperature values. Levenberg-Marquardt algorithm has been employed to train the MLP network. The model in first scenario has three neurons in one hidden layer, and the models associated with the second and the third scenarios have four neurons in one hidden layer. The determination coefficients are calculated as 0.971, 0.940, and 0.964 for the first, second, and the third scenarios, respectively, demonstrating the effectiveness of the MLP model in simulating this process using any of the scenarios, and accurate prediction of extraction yield has been revealed in different working conditions of pressure, temperature, and dynamic time of extraction.

scholarly journals Predicting Fatalities in Air Accidents using CHAID XGBoost Generalized Linear Model Neural Network and Ensemble Models of Machine Learning

2020 ◽  
Vol 9 (3S) ◽  
pp. 35-39
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Historical Data ◽  
Random Trees ◽  
Ensemble Model ◽  
Human Beings ◽  
Hidden Layer ◽  
Learning Principles

The study examines the historical data of about 4700 air crashes all over the world since the first recorded air crash of 1908. Given the immense impact on human beings as well as companies, the study aimed at utilizing Machine Learning principles for predicting fatalities. The train-test partition used was 75-25. Employing the IBM SPSS Modeler, the machine learning models used included CHAID model, Neural Network, Generalized Linear Model, XGBoost, Random Trees and the Ensemble model to predict fatalities in air crashes. The best results (90.6% accuracy) were achieved through Neural Network with one hidden layer. The results presented also include comparison of the predicted versus observed results for the test data.

Study on the Relation between Waxberry Color and its Nutrition Composition Based on BP Neural Network

2011 ◽  
Vol 304 ◽  
pp. 268-273
Author(s):  
Hong Xia Zhao ◽  
Zhi Xia Liu ◽  
Zhi Yang Luo ◽  
Guan Yun Xiao
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Linear Model ◽  
Bp Neural Network ◽  
Bp Network ◽  
The Neural Network ◽  
Farm Produce ◽  
Training Error ◽  
Hidden Layer ◽  
Function Transfer

The color of farm produce is a very important index of quality, its nutrition is correlative with itself color. At present, most of the analyses for pigment and nutrient composition still depend on chemical method; therefore the relation is studied between waxberry color and its nutrition composition based on BP neural network. The conversion relation is expressed by three-layer BP network, which hidden layer has 11 node numbers and its transfer function adopts tansig function; transfer function of output layer selects purelin function. The neural network and linear model of nutrition composition is compared respectively. The MSE value of linear model is 0.300892, and that training error of neural network is 0.0219585. From this result，we can find that the conversion relation between waxberry color and its nutrition composition is a complex non-linear relation, so neural network is adopted to complete this conversion.

Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden (Preprint)

2020 ◽  
Author(s):  
Dianbo Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Third World ◽  
Mortality Prediction ◽  
Neural Net ◽  
Medical Settings ◽  
Hidden Layer ◽  
Applications Of Machine Learning ◽  
Computational Resources ◽  
Developed Nations

BACKGROUND Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare. OBJECTIVE In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset. METHODS We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits. RESULTS We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction. CONCLUSIONS This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

Breast cancer diagnosis using multiple activation deep neural network

2021 ◽  
pp. 1063293X2110251
Author(s):  
K Vijayakumar ◽  
Vinod J Kadam ◽  
Sudhir Kumar Sharma
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis ◽  
Activation Function ◽  
Linear Functions ◽  
Cancer Data ◽  
Final Layer ◽  
Improved Performance ◽  
Hidden Layer

Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

scholarly journals Image reconstruction through a multimode fiber with a simple neural network architecture

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Changyan Zhu ◽  
Eng Aik Chan ◽  
You Wang ◽  
Weina Peng ◽  
Ruixiang Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Training Time ◽  
Modal Dispersion ◽  
Multimode Fibers ◽  
Speckle Patterns ◽  
Hidden Layer ◽  
Simple Neural Network

AbstractMultimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of training time and computing resources required. The trained networks can accurately reconstruct MMF images collected over a week after the cessation of the training set, with the dense network performing as well as the CNN over the entire period.

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