Artificial Neural Network Military Impulse Noise Classifier

Impulse Noise ◽

Training Data ◽

Noise Sources ◽

Wind Noise ◽

Validation Data ◽

Data Set ◽

Different Types ◽

Civilian noise complaints and damage claims have created the need for stations to monitor the production of military impulse noise. However, these stations suffer from numerous false positive detections (due to wind noise) of impulse events and often miss many events of interest. There is also interest in identifying specific noise sources, such different types of ordinance or different types of aircraft. To improve the accuracy of military impulse noise monitoring and make and initial effort to specifically classify noise source, an algorithm based upon an artificial neural network with inputs of conventional and custom acoustic metrics was proposed. To train and evaluate the noise classifier approximately 1,000 waveforms were field collected (110 military aircraft noise, 330 military impulse noise, and 560 non-impulse noise). The final noise classifier used kurtosis and crest factor and the custom metrics spectral slope and weighted square error as inputs. The classifier was able to achieve 99.7% accuracy on the training data set and 99.4% accuracy on the validation data set.

Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques

Journal of Hydroinformatics ◽

10.2166/hydro.2014.106 ◽

2014 ◽

Vol 17 (1) ◽

pp. 56-74 ◽

Cited By ~ 11

Author(s):

Gurjeet Singh ◽

Rabindra K. Panda ◽

Marc Lamers

Keyword(s):

Neural Network ◽

Agricultural Watershed ◽

Training Data ◽

Optimum Number ◽

Training Process ◽

Data Set ◽

Reported Study ◽

Artificial Neural ◽

Artificial Neural Network Ann

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

Mathematical Problems in Engineering ◽

10.1155/2013/346949 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

R. Manjula Devi ◽

S. Kuppuswami ◽

R. C. Suganthe

Keyword(s):

Neural Network ◽

Training Model ◽

Training Data ◽

Experimental Result ◽

Training Algorithms ◽

Data Set ◽

Training Time ◽

Input Sample ◽

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed. This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample. This LAST algorithm can be incorporated into any supervised training algorithms. Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.

Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)

Diyala Journal of Engineering Sciences ◽

10.24237/djes.2014.07409 ◽

2014 ◽

Vol 7 (4) ◽

pp. 132-143

Author(s):

ABBAS M. ABD ◽

SAAD SH. SAMMEN

Keyword(s):

Neural Network ◽

Correlation Coefficient ◽

Mean Squared Error ◽

Water Reservoir ◽

Prediction Method ◽

Training Data ◽

Ann Model ◽

Data Set ◽

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used.A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

Advances in Bioinformatics ◽

10.1155/2014/708279 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 42

Author(s):

Karthik Kalyan ◽

Binal Jakhia ◽

Ramachandra Dattatraya Lele ◽

Mukund Joshi ◽

Abhay Chowdhary

Keyword(s):

Neural Network ◽

Texture Features ◽

Speckle Noise ◽

Training Data ◽

Ultrasound Images ◽

Data Set ◽

Imaging Artifacts ◽

Artificial Neural ◽

Ultrasonic Images

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.

Regression and Artificial Neural Network Modeling for the Prediction of Gray Leaf Spot of Maize

Phytopathology ◽

10.1094/phyto-95-0388 ◽

2005 ◽

Vol 95 (4) ◽

pp. 388-396 ◽

Cited By ~ 28

Author(s):

P. A. Paul ◽

G. P. Munkvold

Keyword(s):

Neural Network ◽

Leaf Spot ◽

Gray Leaf Spot ◽

Predictor Variables ◽

Neural Network Modeling ◽

Growth Stages ◽

Validation Data ◽

Data Set ◽

Regression and artificial neural network (ANN) modeling approaches were combined to develop models to predict the severity of gray leaf spot of maize, caused by Cercospora zeae-maydis. In all, 329 cases consisting of environmental, cultural, and location-specific variables were collected for field plots in Iowa between 1998 and 2002. Disease severity on the ear leaf at the dough to dent plant growth stage was used as the response variable. Correlation and regression analyses were performed to select potentially useful predictor variables. Predictors from the best 9 of 80 regression models were used to develop ANN models. A random sample of 60% of the cases was used to train the networks, and 20% each for testing and validation. Model performance was evaluated based on coefficient of determination (R2) and mean square error (MSE) for the validation data set. The best models had R2 ranging from 0.70 to 0.75 and MSE ranging from 174.7 to 202.8. The most useful predictor variables were hours of daily temperatures between 22 and 30°C (85.50 to 230.50 h) and hours of nightly relative humidity ≥90% (122 to 330 h) for the period between growth stages V4 and V12, mean nightly temperature (65.26 to 76.56°C) for the period between growth stages V12 and R2, longitude (90.08 to 95.14°W), maize residue on the soil surface (0 to 100%), planting date (in day of the year; 112 to 182), and gray leaf spot resistance rating (2 to 7; based on a 1-to-9 scale, where 1 = most susceptible to 9 = most resistant).

Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination With Process Signals in Resistance Spot Welding of Advanced High-Strength Steels

10.20944/preprints202110.0411.v1 ◽

2021 ◽

Author(s):

Bassel El-Sari ◽

Max Biegler ◽

Michael Rethmeier

Keyword(s):

Neural Network ◽

Process Parameters ◽

Resistance Spot Welding ◽

Spot Welding ◽

Predictive Accuracy ◽

Training Data ◽

Multi Layer Perceptron ◽

Data Set ◽

Resistance spot welding is an established joining process in the production of safety-relevant components in the automotive industry. Therefore, a consecutive process monitoring is essential to meet the high-quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals to ensure the individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set and the prediction of untrained data is challenging. The aim of this paper is to investigate the extrapolation capability of the multi-layer perceptron model. That means, that the predictive performance of the model will be tested with data that clearly differs from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the trained datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of the process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.

INFLUENCE OF CHARACTERISTICS OF TRAINING DATA SET ON THE ACCURACY OF ROCK CLASSIFICATION ESTIMATION FOR MOUNTAIN TUNNELS BY ARTIFICIAL NEURAL NETWORK

Journal of Japan Society of Civil Engineers Ser F1 (Tunnel Engineering) ◽

10.2208/jscejte.75.130 ◽

2019 ◽

Vol 75 (1) ◽

pp. 130-142

Author(s):

Yotaro TSUCHIYA ◽

Takafumi KITAOKA ◽

Nobusuke HASEGAWA ◽

Thirapong PIPATPONGSA ◽

Hiroyasu OHTSU

Keyword(s):

Neural Network ◽

Training Data ◽

Data Set ◽

Rock Classification ◽

Mountain Tunnels ◽

Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels

Metals ◽

10.3390/met11111874 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1874

Author(s):

Bassel El-Sari ◽

Max Biegler ◽

Michael Rethmeier

Keyword(s):

Neural Network ◽

Process Parameters ◽

Resistance Spot Welding ◽

Spot Welding ◽

Predictive Accuracy ◽

Training Data ◽

Multi Layer Perceptron ◽

Data Set ◽

Resistance spot welding is an established joining process for the production of safety-relevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.

Prediction of seroma after total mastectomy using an artificial neural network algorithm

Breast Disease ◽

10.3233/bd-201051 ◽

2021 ◽

pp. 1-6

Author(s):

Amarit Tansawet ◽

Panupong Nakchuai ◽

Suphakarn Techapongsatorn ◽

Pakkapol Sukhvibul ◽

Sermkiat Lolak

Keyword(s):

Neural Network ◽

Axillary Lymph ◽

Total Mastectomy ◽

Validation Data ◽

Data Set ◽

Predictive Values ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Seroma is a common complication after mastectomy. To the best of our knowledge, no prediction models have been developed for this. Henceforth, medical records of total mastectomy patients were retrospectively reviewed. Data consisting of 120 subjects were divided into a training-validation data set (96 subjects) and a testing data set (24 subjects). Data was learned by using a 9-layer artificial neural network (ANN), and the model was validated using 10-fold cross-validation. The model performance was assessed by a confusion matrix in the validating data set. The receiver operating characteristic curve was constructed, and the area under the curve (AUC) was also calculated. Pathology type, presence of hypertension, presence of diabetes, receiving of neoadjuvant chemotherapy, body mass index, and axillary lymph node (LN) management (i.e., sentinel LN biopsy and axillary LN dissection) were selected as predictive factors in a model developed from the neural network algorithm. The model yielded an AUC of 0.760, which corresponded with a level of acceptable discrimination. Sensitivity, specificity, accuracy, and positive and negative predictive values were 100%, 52.9%, 66.7%, 46.7%, and 100%, respectively. Our model, which was developed from the ANN algorithm can predict seroma after total mastectomy with high sensitivity. Nevertheless, external validation is still needed to confirm the performance of this model.

Oversampling Method on Classifying Hypertension Using Naive Bayes, Decision Tree, and Artificial Neural Network

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i4.2015 ◽

2020 ◽

Vol 4 (4) ◽

pp. 635-641

Author(s):

Nurul Chamidah ◽

Mayanda Mega Santoni ◽

Nurhafifah Matondang

Keyword(s):

Neural Network ◽

Decision Tree ◽

Missing Values ◽

Naive Bayes ◽

Classification Performance ◽

Naïve Bayes ◽

Training Data ◽

Validation Data ◽

Oversampling is a technique to balance the number of data records for each class by generating data with a small number of records in a class, so that the amount is balanced with data with a class with a large number of records. Oversampling in this study is applied to hypertension dataset where hypertensive class has a small number of records when compared to the number of records for non-hypertensive classes. This study aims to evaluate the effect of oversampling on the classification of hypertension dataset consisting of hypertensive and non-hypertensive classes by utilizing the Naïve Bayes, Decision Tree, and Artificial Neural Network (ANN) as well as finding the best model of the three algorithms. Evaluation of the use of oversampling on hypertension dataset is done by processing the data by imputing missing values, oversampling, and transforming data into the same range, then using the Naïve Bayes, Decision Tree, and ANN to build classification models. By dividing 80% of data as training data to build models and 20% as validation data for testing models, we had an increase in classification performance in the form of accuracy, precision, and recall of the oversampled data when compared without oversampling. The best performance in this study resulted in the highest accuracy using ANN with 0.91, precision 0.86 and recall 0.99.