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.

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 Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

New recommendation to predict export value using big data and machine learning technique

2021 ◽  
pp. 1-14
Author(s):  
Rani Nooraeni ◽  
Jimmy Nickelson ◽  
Eko Rahmadian ◽  
Nugroho Puspito Yudho
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Big Data ◽  
Ann Model ◽  
Machine Learning Technique ◽  
Current Period ◽  
Learning Technique ◽  
Monthly Period ◽  
Conventional Machine ◽  
Artificial Neural Network Ann

Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model.

Neural Networks for the Classification of Benign and Malignant Patters in Digital Mammograms

2006 ◽  
pp. 251-272 ◽  
Author(s):  
Brijesh Verma ◽  
Rinku Panchal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Digital Mammography ◽  
Extensive Review ◽  
Benchmark Database ◽  
Feature Extraction And Selection ◽  
Recent Developments ◽  
Artificial Neural Network Ann

This chapter presents neural network-based techniques for the classification of micro-calcification patterns in digital mammograms. Artificial neural network (ANN) applications in digital mammography are mainly focused on feature extraction, feature selection, and classification of micro-calcification patterns into ‘benign’ and ‘malignant’. An extensive review of neural network based techniques in digital mammography is presented. Recent developments such as auto-associators and evolutionary neural networks for feature extraction and selection are presented. Experimental results using ANN techniques on a benchmark database are described and analysed. Finally, a comparison of various neural network-based techniques is presented.

scholarly journals Cognitive Matching of Design Subjects in Product Form Evolutionary Design

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Shutao Zhang ◽  
Shijie Wang ◽  
Aimin Zhou ◽  
Shifeng Liu ◽  
Jianning Su
Keyword(s):  
Evaluation System ◽  
Intelligent Design ◽  
Evolution System ◽  
Image Evaluation ◽  
Matching Model ◽  
Cognitive Differences ◽  
Matching Process ◽  
Order Preference ◽  
Artificial Neural Network Ann ◽  
Product Forms

In actual product development, the cognitive differences between users and designers make it difficult for the designed products to be recognized by users. To reduce the cognitive differences between these two design subjects, this paper proposes a method of cognitive matching of the design subjects. First, we use the relevant methods of Kansei engineering to quantify the Kansei image cognition of the two design subjects and construct a cognitive matching model of the design subjects with information entropy and the technique for order preference by similarity to ideal solution (TOPSIS). Second, according to the Kansei image, the Kansei image prototype cluster is constructed, and the representative Kansei image prototype is obtained. Then, we combine an artificial neural network (ANN) with a cognitive matching model of the design subjects to construct a product Kansei image evaluation system; this is used to evaluate the evolved forms. Finally, a product Kansei image form evolution system is constructed based on the genetic algorithm (GA). To some extent, the system simulates the cognitive matching process between designers and users in product design, helps designers to more accurately understand the cognitive trends of the two design subjects, and provides a theoretical basis for the intelligent design of product forms through the cognitive balance of multiple design subjects. This paper takes a beverage bottle as an example to verify the feasibility of the model through a comparative study.

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