Application of DRSA-ANN Classifier in Computational Stylistics

2011 ◽  
pp. 695-704 ◽  
Author(s):  
Urszula Stańczyk
Keyword(s):  
Computational Stylistics ◽  
Ann Classifier

scholarly journals Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images

Biomedicines ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 12
Author(s):  
Chung-Yao Chien ◽  
Szu-Wei Hsu ◽  
Tsung-Lin Lee ◽  
Pi-Shan Sung ◽  
Chou-Ching Lin
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Dopamine Transporter ◽  
Support Vector ◽  
Dat Spect ◽  
Ann Classifier ◽  
Spect Images

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

scholarly journals A Novel Method to Detect Inner Emotion States of Human using Artificial Neural Networks

2020 ◽  
Vol 8 (6) ◽  
pp. 5820-5825
Keyword(s):  
Physiological Signals ◽  
Classification Rate ◽  
Discrete Emotion ◽  
Novel Approach ◽  
Proposed Model ◽  
Depression Detection ◽  
Novel Method ◽  
Artificial Neural ◽  
Ann Classifier ◽  
Performance Rate

Human computer interaction is a fast growing area of research where in the physiological signals are used to identify human emotion states. Identifying emotion states can be done using various approaches. One such approach which gained interest of research is through physiological signals using EEG. In the present work, a novel approach is proposed to elicit emotion states using 3-D Video-audio stimuli. Around 66 subjects were involved during data acquisition using 32 channel Enobio device. FIR filter is used to preprocess the acquired raw EEG signals. The desired frequency bands like alpha, delta, beta and theta are extracted using 8-level DWT. The statistical features, Hurst exponential, entropy, power, energy, differential entropy of each bands are computed. Artificial Neural network is implemented using Sequential Keras model and applied on the extracted features to classify in to four classes (HVLA, HVHA, LVHA and LVLA) and eight discrete emotion states like clam, relax, happy, joy, sad, fear, tensed and bored. The performance of ANN classifier found to perform better for 4- classes than 8-classes with a classification rate of 90.835% and 74.0446% respectively. The proposed model achieved better performance rate in detecting discrete emotion states. This model can be used to build applications on health like stress / depression detection and on entertainment to build emotional DJ.

A New Islanding Detection Method Based on Wavelet-transform and ANN for Micro-grid Including Inverter Assisted Distributed Generator

2019 ◽  
Vol 20 (5) ◽  
Author(s):  
Zhengyuan Guan ◽  
Yuan Liao
Keyword(s):  
Wavelet Transform ◽  
Harmonic Distortion ◽  
Distribution Networks ◽  
Islanding Detection ◽  
Detection Zone ◽  
Distributed Generator ◽  
Voltage Unbalance ◽  
Typical Distribution ◽  
Artificial Neural Network Ann ◽  
Ann Classifier

Abstract This paper presents a new composite approach based on wavelet-transform and ANN for islanding detection of distributed generation (DG). The proposed method first uses wavelet-transform to detect the occurrence of events, and then uses artificial neural network (ANN) to classify islanding and non-islanding events. Total harmonic distortion and voltage unbalance are extracted as feature inputs for ANN classifier. The performance of the proposed method is tested by simulations for two typical distribution networks based on MATLAB/Simulink. The results show that the developed method can effectively detect islanding with low misclassification. The method has the advantages of small non-detection zone and robustness against noises.

Performance Analysis of Machine Learning Based Classifiers for the Diagnosis of Melanoma Cancer and Comparison

2018 ◽  
Vol 15 (2) ◽  
pp. 558-575
Author(s):  
A. Anto Spiritus Kingsly ◽  
B. Sankaragomathi
Keyword(s):  
Detection System ◽  
Noise Removal ◽  
Morphological Processing ◽  
Svm Classifier ◽  
Bag Of Visual Words ◽  
Data Set ◽  
Visual Words ◽  
Melanoma Cancer ◽  
Processing Step ◽  
Ann Classifier

Melanoma cancer is the most injurious form of cancer which affects the human. Skin cancer has quickly increased in western part of the country among the world. In this paper, diagnosing melanoma in premature stages a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy image of skin is taken, and it is subjected to the pre-processing step for noise removal and post-processing step for image enhancement. Then the processed image undergoes image segmentation using Otsu method and Morphological processing. Second, features are extracted using feature extraction technique-ABCD parameter, GLCM, and FOS. Various feature combinations are given as the input to the KNN, SVM, ANN and Bag of Visual Words classifiers. KNN classifier is used to classify the data set into two classes, SVM classifier is used to classify the data set into three classes, ANN classifier is used to classify the data set based on the number of layers and Bag of Visual Words are used to classify the data set into two classes. Performance is analyzed based on the accuracy of the learning classifier output.

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