scholarly journals Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Mi Li ◽  
Lei Cao ◽  
Qian Zhai ◽  
Peng Li ◽  
Sa Liu ◽  
...  
Keyword(s):  
Eye Movement ◽  
Principal Component ◽  
Classification Performance ◽  
Physiological Signals ◽  
Movement Behavior ◽  
Emotional Stimuli ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
The One ◽  
Better Than

This paper presents a method of depression recognition based on direct measurement of affective disorder. Firstly, visual emotional stimuli are used to obtain eye movement behavior signals and physiological signals directly related to mood. Then, in order to eliminate noise and redundant information and obtain better classification features, statistical methods (FDR corrected t-test) and principal component analysis (PCA) are used to select features of eye movement behavior and physiological signals. Finally, based on feature extraction, we use kernel extreme learning machine (KELM) to recognize depression based on PCA features. The results show that, on the one hand, the classification performance based on the fusion features of eye movement behavior and physiological signals is better than using a single behavior feature and a single physiological feature; on the other hand, compared with previous methods, the proposed method for depression recognition achieves better classification results. This study is of great value for the establishment of an automatic depression diagnosis system for clinical use.

scholarly journals Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

scholarly journals Deep Kernel Extreme-Learning Machine for the Spectral–Spatial Classification of Hyperspectral Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 2036 ◽  
Author(s):  
Jiaojiao Li ◽  
Bobo Xi ◽  
Qian Du ◽  
Rui Song ◽  
Yunsong Li ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Hyperspectral Image ◽  
Low Cost ◽  
Principal Component ◽  
Learning Machines ◽  
Spatial Features ◽  
Classification Framework ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Deep Feature Extraction

Extreme-learning machines (ELM) have attracted significant attention in hyperspectral image classification due to their extremely fast and simple training structure. However, their shallow architecture may not be capable of further improving classification accuracy. Recently, deep-learning-based algorithms have focused on deep feature extraction. In this paper, a deep neural network-based kernel extreme-learning machine (KELM) is proposed. Furthermore, an excellent spatial guided filter with first-principal component (GFFPC) is also proposed for spatial feature enhancement. Consequently, a new classification framework derived from the deep KELM network and GFFPC is presented to generate deep spectral and spatial features. Experimental results demonstrate that the proposed framework outperforms some state-of-the-art algorithms with very low cost, which can be used for real-time processes.

scholarly journals An Optimal Feature Selection with Wavelet Kernel Extreme Learning Machine for Big Data Analysis of Product Reviews

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
R. Sathya , Et. al.
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Extreme Learning Machine ◽  
Selection Process ◽  
Classification Performance ◽  
Product Reviews ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

In recent times, generation of big data takes place in an exponential way from diverse textual data sources like review sites, media, blogs, etc. Sentiment analysis (SA) finds it useful to classify the opinions of the big data to different kinds ofsentiments. Therefore, SA on big data helps a business to take beneficial commercial understandings from text based content. Though several SA approaches have been presented, yet, there is a need to improve the performance of SA to interpret the customer’s feedback and increase the product quality.This paper introduces a novel social spider optimization based feature selection based wavelet kernel extreme learning machine (SSO-WKELM) model. The proposed model initially undergoes pre-processing to remove the unwanted word removal. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a social spider optimization (SSO) algorithm is utilized for feature selection process and thereby achieves improved classification performance. Subsequently, WKELM is employed as a classifier to classify the incidence of positive or negative user reviews. For experimental validation, a Product review dataset derived from Amazon along with synthetic data is used. The experimental results stated the superior classification performance of the SSO-WKELM model.   

scholarly journals Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 811
Author(s):  
Muhammad Attique Khan ◽  
Muhammad Sharif ◽  
Tallha Akram ◽  
Robertas Damaševičius ◽  
Rytis Maskeliūnas
Keyword(s):  
Skin Lesion ◽  
Skin Lesions ◽  
Classification Performance ◽  
Lesion Segmentation ◽  
Local Color ◽  
Heat Map ◽  
Kernel Extreme Learning Machine ◽  
Deep Learning Features ◽  
Learning Machine ◽  
Thresholding Function

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

scholarly journals Facial Expression Decoding based on fMRI Brain Signal

2019 ◽  
Vol 14 (4) ◽  
pp. 475-488
Author(s):  
Benchun Cao ◽  
Yanchun Liang ◽  
Shinichi Yoshida ◽  
Renchu Guan
Keyword(s):  
Support Vector Machine ◽  
Extreme Learning Machine ◽  
Facial Expressions ◽  
Classification Performance ◽  
Computer Interface ◽  
Support Vector ◽  
Brain Decoding ◽  
Learning Machine ◽  
The Brain ◽  
Better Than

The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions.

scholarly journals KELM-KPCA Method for COVID-19-induced Pneumonia Detection

2021 ◽  
Vol 17 ◽  
pp. 166-171
Author(s):  
Bacha Sawssen ◽  
Taouali Okba ◽  
Liouane Noureeddine
Keyword(s):  
Principal Component Analysis ◽  
Extreme Learning Machine ◽  
Principal Component ◽  
Component Analysis ◽  
Chest Radiographs ◽  
Kernel Principal Component Analysis ◽  
The World ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
One Year

The new corona virus 2019 (COVID-19) has become the most pressing issue facing mankind. Like a wildfire burning through the world, the COVID-19 disease has changed the global landscape in only one year. In this mini-review, a novel image classifier based on Kernel Extreme Learning Machine (KELM) and Kernel Principal Component Analysis (KPCA) is presented. The proposed algorithm called KELM-KPCA, aims to detect COVID-19 disease in chest radiographs, using a constrained dataset.

scholarly journals Exploring the Extreme Learning Machine for Classification of Brain MRIs

2019 ◽  
Vol 9 (2) ◽  
pp. 3654-3657
Keyword(s):  
Extreme Learning Machine ◽  
Principal Component ◽  
Classification Performance ◽  
Feature Reduction ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
Learning Machine ◽  
Hidden Layer ◽  
Method Accuracy

Magnetic Resonance Imaging (MRI) technique of brain is the most important aspect of diagnosis of brain diseases. The manual analysis of MR images and identifying the brain diseases is tedious and error prone task for the radiologists and physicians. In this paper 2-Dimensional Discrete Wavelet Transformation (2D DWT) is used for feature extraction and Principal Component Analysis (PCA) is used for feature reduction. The three types of brain diseases i.e. Alzheimer, Glioma and Multiple Sclerosis are considered for this work. The Two Hidden layer Extreme learning Machine (TELM) is used for classification of samples into normal or pathological. The performance of the TELM is compared with basic ELM and the simulation results indicate that TELM outperformed the basic ELM method. Accuracy, Recall, Sensitivity and F-score are considered as the classification performance measures in this paper

Subjective Time Preference and Willingness to Pay for an Energy-Saving Durable Good

2001 ◽  
Vol 32 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Gerrit Antonides ◽  
Sophia R. Wunderink
Keyword(s):  
Willingness To Pay ◽  
Energy Saving ◽  
Subjective Time ◽  
Durable Good ◽  
Discount Function ◽  
Hyperbolic Discount ◽  
The One ◽  
Two Parameter ◽  
Difference Effect ◽  
Better Than

Summary: Different shapes of individual subjective discount functions were compared using real measures of willingness to accept future monetary outcomes in an experiment. The two-parameter hyperbolic discount function described the data better than three alternative one-parameter discount functions. However, the hyperbolic discount functions did not explain the common difference effect better than the classical discount function. Discount functions were also estimated from survey data of Dutch households who reported their willingness to postpone positive and negative amounts. Future positive amounts were discounted more than future negative amounts and smaller amounts were discounted more than larger amounts. Furthermore, younger people discounted more than older people. Finally, discount functions were used in explaining consumers' willingness to pay for an energy-saving durable good. In this case, the two-parameter discount model could not be estimated and the one-parameter models did not differ significantly in explaining the data.

Exploring the Multidimensional Facets of Authoritarianism: Authoritarian Aggression and Social Dominance Orientation

2008 ◽  
Vol 67 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Stefano Passini
Keyword(s):  
Social Dominance ◽  
Factor Model ◽  
Three Dimensional ◽  
Social Dominance Orientation ◽  
Model Fit ◽  
Regression Analyses ◽  
One Dimensional ◽  
Confirmatory Factor ◽  
The One ◽  
Better Than

The relation between authoritarianism and social dominance orientation was analyzed, with authoritarianism measured using a three-dimensional scale. The implicit multidimensional structure (authoritarian submission, conventionalism, authoritarian aggression) of Altemeyer’s (1981, 1988) conceptualization of authoritarianism is inconsistent with its one-dimensional methodological operationalization. The dimensionality of authoritarianism was investigated using confirmatory factor analysis in a sample of 713 university students. As hypothesized, the three-factor model fit the data significantly better than the one-factor model. Regression analyses revealed that only authoritarian aggression was related to social dominance orientation. That is, only intolerance of deviance was related to high social dominance, whereas submissiveness was not.

Spearman's Law of Diminishing Returns

2006 ◽  
Vol 27 (4) ◽  
pp. 199-207 ◽  
Author(s):  
Peter Hartmann
Keyword(s):  
Principal Axis ◽  
Age Groups ◽  
Principal Component ◽  
Longitudinal Survey ◽  
Male And Female ◽  
Two Samples ◽  
Diminishing Returns ◽  
The One ◽  
Armed Services ◽  
Principal Axis Factor

Spearman's Law of Diminishing Returns (SLODR) with regard to age was tested in two different databases from the National Longitudinal Survey of Youth. The first database consisted of 6,980 boys and girls aged 12–16 from the 1997 cohort ( NLSY 1997 ). The subjects were tested with a computer-administered adaptive format (CAT) of the Armed Services Vocational Aptitude Battery (ASVAB) consisting of 12 subtests. The second database consisted of 11,448 male and female subjects aged 15–24 from the 1979 cohort ( NLSY 1979 ). These subjects were tested with the older 10-subtest version of the ASVAB. The hypothesis was tested by dividing the sample into Young and Old age groups while keeping IQ fairly constant by a method similar to the one developed and employed by Deary et al. (1996) . The different age groups were subsequently factor-analyzed separately. The eigenvalue of the first principal component (PC1) and the first principal axis factor (PAF1), and the average intercorrelation of the subtests were used as estimates of the g saturation and compared across groups. There were no significant differences in the g saturation across age groups for any of the two samples, thereby pointing to no support for this aspect of Spearman's “Law of Diminishing Returns.”

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