Brushlet transform for hyperspectral feature extraction in automated detection of nutsedge presence in soybean

2002 ◽  
Author(s):  
Yan Huang ◽  
L.M. Bruce ◽  
Jiang Li ◽  
C. Leon ◽  
D. Shaw
Keyword(s):  
Feature Extraction ◽  
Automated Detection

New feature extraction for automated detection of epileptic seizure using complex network framework

2021 ◽  
Vol 180 ◽  
pp. 108098
Author(s):  
Supriya Supriya ◽  
Siuly Siuly ◽  
Hua Wang ◽  
Yanchun Zhang
Keyword(s):  
Feature Extraction ◽  
Complex Network ◽  
Epileptic Seizure ◽  
Automated Detection ◽  
New Feature

scholarly journals Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5037
Author(s):  
Hisham ElMoaqet ◽  
Mohammad Eid ◽  
Martin Glos ◽  
Mutaz Ryalat ◽  
Thomas Penzel
Keyword(s):  
Feature Extraction ◽  
Sleep Apnea ◽  
Short Term Memory ◽  
Single Channel ◽  
Automated Detection ◽  
Detection Methods ◽  
Short Term ◽  
Term Memory ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory

Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals: Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method.

scholarly journals Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
N. Yuvaraj ◽  
K. Srihari ◽  
Gaurav Dhiman ◽  
K. Somasundaram ◽  
Ashutosh Sharma ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Evaluation System ◽  
Automated Detection ◽  
User Comments ◽  
Aggressive Action ◽  
Conventional Machine ◽  
Automated Tools ◽  
Artificial Neural Network Ann ◽  
Psychological Features ◽  
Modern Era

In the modern era, the cyberbullying (CB) is an intentional and aggressive action of an individual or a group against a victim via electronic media. The consequence of CB is increasing alarmingly, affecting the victim either physically or psychologically. This allows the use of automated detection tools, but research on such automated tools is limited due to poor datasets or elimination of wide features during the CB detection. In this paper, an integrated model is proposed that combines both the feature extraction engine and classification engine from the input raw text datasets from a social media engine. The feature extraction engine extracts the psychological features, user comments, and the context into consideration for CB detection. The classification engine using artificial neural network (ANN) classifies the results, and it is provided with an evaluation system that either rewards or penalizes the classified output. The evaluation is carried out using Deep Reinforcement Learning (DRL) that improves the performance of classification. The simulation is carried out to validate the efficacy of the ANN-DRL model against various metrics that include accuracy, precision, recall, and f-measure. The results of the simulation show that the ANN-DRL has higher classification results than conventional machine learning classifiers.

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