Automatic epileptic seizure recognition using reliefF feature selection and long short term memory classifier

2020 ◽  
Author(s):  
Hirald Dwaraka Praveena ◽  
C. Subhas ◽  
K. Rama Naidu
Keyword(s):  
Feature Selection ◽  
Epileptic Seizure ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Ensemble-Based Feature Selection With Long Short-Term Memory for Classification of Network Intrusion

2021 ◽  
pp. 228-245
Author(s):  
Preethi D. ◽  
Neelu Khare
Keyword(s):  
Feature Selection ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Short Term ◽  
Chi Square ◽  
Term Memory ◽  
Network Intrusion ◽  
Proposed Model ◽  
Long Short Term Memory

This chapter presents an ensemble-based feature selection with long short-term memory (LSTM) model. A deep recurrent learning model is proposed for classifying network intrusion. This model uses ensemble-based feature selection (EFS) for selecting the appropriate features from the dataset and long short-term memory for the classification of network intrusions. The EFS combines five feature selection techniques, namely information gain, gain ratio, chi-square, correlation-based feature selection, and symmetric uncertainty-based feature selection. The experiments were conducted using the standard benchmark NSL-KDD dataset and implemented using tensor flow and python. The proposed model is evaluated using the classification performance metrics and also compared with all the 41 features without any feature selection as well as with each individual feature selection technique and classified using LSTM. The performance study showed that the proposed model performs better, with 99.8% accuracy, with a higher detection and lower false alarm rates.

Epileptic seizure detection using 1 D-convolutional long short-term memory neural networks

2021 ◽  
Vol 177 ◽  
pp. 107941
Author(s):  
Waqar Hussain ◽  
Muhammad Tariq Sadiq ◽  
Siuly Siuly ◽  
Ateeq Ur Rehman
Keyword(s):  
Neural Networks ◽  
Epileptic Seizure ◽  
Short Term Memory ◽  
Seizure Detection ◽  
Short Term ◽  
Term Memory ◽  
Epileptic Seizure Detection ◽  
Long Short Term Memory

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Satish Kumar ◽  
Tushar Kolekar ◽  
Ketan Kotecha ◽  
Shruti Patil ◽  
Arunkumar Bongale
Keyword(s):  
Feature Selection ◽  
Tool Wear ◽  
Hybrid Model ◽  
Short Term Memory ◽  
Short Term ◽  
Wear Prediction ◽  
Term Memory ◽  
Content Type ◽  
Tool Wear Prediction ◽  
Long Short Term Memory

Purpose Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.Design/methodology/approachThis paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.FindingsThe R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.Originality/value The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

An intelligent monitoring system of grinding wheel wear based on two-stage feature selection and Long Short-Term Memory network

2019 ◽  
Vol 233 (13) ◽  
pp. 2436-2446 ◽  
Author(s):  
Weicheng Guo ◽  
Beizhi Li ◽  
Qinzhi Zhou
Keyword(s):  
Feature Selection ◽  
Short Term Memory ◽  
Grinding Wheel ◽  
Feature Subset ◽  
Short Term ◽  
Wheel Wear ◽  
Two Stage ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Optimal Feature Subset

Grinding wheel condition is considered as the key factor affecting grinding performance, and therefore, accurate monitoring of wheel wear is necessary to prevent the deterioration of part quality. An intelligent wheel wear monitoring system is introduced in this article to realize processing of grinding signal, extraction of signal features, selection of optimal feature subset, and prediction of wheel wear. Physical information generated during the grinding of C-250 maraging steel is collected by a dynamometer, accelerometer, and acoustic emission sensor, and a large quantity of features in time domain and frequency domain are extracted from the processed grinding signals. To reduce feature redundancy and increase relevancy of feature to wheel wear, a two-stage feature selection approach combining filter and wrapper framework is proposed. The filter preselects individual features by minimum Redundancy Maximum Relevance method, while the wrapper evaluates different feature subsets by the model performance. A deep learning network structure named Long Short-Term Memory network is adopted to develop the wheel wear monitoring model and is compared with a conventional machine learning algorithm, Random Forest. The results have shown that the two-stage feature selection method is able to provide the globally optimal feature subset for the model. Long Short-Term Memory model achieves an R2 of 0.994 and a root-mean-square error of 0.240 with four features, while Random Forest model obtains an R2 of 0.980 and a root-mean-square error of 0.463 with seven features, which indicates that Long Short-Term Memory model is capable of predicting wheel wear more accurately even with less features.

scholarly journals Development of a Novel Soft Sensor with Long Short-Term Memory Network and Normalized Mutual Information Feature Selection

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Dongfeng Li ◽  
Zhirui Li ◽  
Kai Sun
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Variable Selection ◽  
Short Term Memory ◽  
Dynamic Modelling ◽  
Soft Sensor ◽  
Short Term ◽  
Term Memory ◽  
Normalized Mutual Information ◽  
Long Short Term Memory

In this paper, a novel soft sensor is developed by combining long short-term memory (LSTM) network with normalized mutual information feature selection (NMIFS). In the proposed algorithm, LSTM is designed to handle time series with high nonlinearity and dynamics of industrial processes. NMIFS is conducted to perform the input variable selection for LSTM to simplify the excessive complexity of the model. The developed soft sensor combines the excellent dynamic modelling of LSTM and precise variable selection of NMIFS. Simulations on two actual production datasets are used to demonstrate the performance of the proposed algorithm. The developed soft sensor could precisely predict the objective variables and has better performance than other methods.

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