Discovering Emotion-Inducing Music Features Using EEG Signals

2013 ◽  
Vol 17 (3) ◽  
pp. 362-370 ◽  
Author(s):  
Rafael Cabredo ◽  
◽  
Roberto Legaspi ◽  
Paul Salvador Inventado ◽  
Masayuki Numao ◽  
...  
Keyword(s):  
Cross Validation ◽  
Absolute Error ◽  
Window Length ◽  
Analysis Method ◽  
Eeg Signals ◽  
Eeg Data ◽  
Windowing Technique ◽  
High Level ◽  
Fold Cross Validation ◽  
Emotion Models

Music induces different kinds of emotions in listeners. Previous research on music and emotions discovered that different music features can be used for classifying how certain music can induce emotions in an individual. We propose a method for collecting electroencephalograph (EEG) data from subjects listening to emotion-inducing music. The EEG data is used to continuously label high-level music features with continuous-valued emotion annotations using the emotion spectrum analysis method. The music features are extracted fromMIDI files using a windowing technique. We highlight the results of two emotion models for stress and relaxation which were constructed using C4.5. Evaluations of the models using 10-fold cross validation give promising results with an average relative absolute error of 6.54% using a window length of 38.4 seconds.

Detection of Relevance between Long-Term Different Professional Training and Brain Development Using EEG

2011 ◽  
Vol 179-180 ◽  
pp. 886-890 ◽  
Author(s):  
Li Li Fan ◽  
Wei Wang
Keyword(s):  
Brain Development ◽  
Professional Training ◽  
Cross Validation ◽  
Eeg Signals ◽  
Bp Network ◽  
Development Patterns ◽  
Frequency Features ◽  
Fold Cross Validation ◽  
Mental Tasks

To analyse the effects of long-term different professional training on brain development. First, EEG singals of 12 students are collected under thinking different mental tasks, after long-term different professional training. Second, to use DFT to extract frequency features and then use BP network and 10-fold cross validation to classify. The classification accuracy can amount to 90 percent around. The result indicates that EEG signals can change specially under special mental tasks, after long-term different professional training. It can reflect the different brain development patterns after different professional training directly.

scholarly journals Penerapan Algoritma C4.5 Untuk Prediksi Churn Rate Pengguna Jasa Telekomunikasi

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
yohana Tri Utami ◽  
Dewi Asiah Shofiana ◽  
Yunda Heningtyas
Keyword(s):  
Cross Validation ◽  
Primary Reason ◽  
Accuracy Rate ◽  
Customer Churn ◽  
Performance Quality ◽  
Classification Technique ◽  
C4.5 Algorithm ◽  
High Level ◽  
Fold Cross Validation

Telecommunication industries are experiencing substantial problems related to the migration of customers due to a large number of competing companies, dynamic circumstances, as well as the presence of many innovative and attractive offerings. The situation has resulted in a high level of customer migration, affecting a decrement toward the company revenue. Regarding that condition, the customer churn is one well-know approach that can help in increasing the company's revenue and reputation. As to predict the reason behind the migration of customer, this study proposed a data mining classification technique by applying the C4.5 algorithm. Patterns generated by the model were implemented using 10-fold cross-validation, resulting in a model with an accuracy rate of 87%, precision 87.5%, and a recall of 97%. Based on the good performance quality of the model, it can be stated that the C4.5 algorithm succeeded to discover several causes from the migration of telecommunication users, in which price holds the top place as the primary reason

scholarly journals Predictive modeling of religiosity, prosociality, and moralizing in 295,000 individuals from European and non-European populations

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pierre O. Jacquet ◽  
Farid Pazhoohi ◽  
Charles Findling ◽  
Hugo Mell ◽  
Coralie Chevallier ◽  
...  
Keyword(s):  
Sexual Behaviors ◽  
Large Scale ◽  
Cross Validation ◽  
Psychological Explanation ◽  
Collective Actions ◽  
Sample Data ◽  
Out Of Sample ◽  
Prosocial Attitudes ◽  
High Level ◽  
Fold Cross Validation

AbstractWhy do moral religions exist? An influential psychological explanation is that religious beliefs in supernatural punishment is cultural group adaptation enhancing prosocial attitudes and thereby large-scale cooperation. An alternative explanation is that religiosity is an individual strategy that results from high level of mistrust and the need for individuals to control others’ behaviors through moralizing. Existing evidence is mixed but most works are limited by sample size and generalizability issues. The present study overcomes these limitations by applying k-fold cross-validation on multivariate modeling of data from >295,000 individuals in 108 countries of the World Values Surveys and the European Value Study. First, this methodology reveals no evidence that European and non-European religious people invest more in collective actions and are more trustful of unrelated conspecifics. Instead, the individuals’ level of religiosity is found to be weakly but positively associated with social mistrust and negatively associated with the production of behaviors, which benefit unrelated members of the large-scale community. Second, our models show that individual variation in religiosity is well explained by the interaction of increased levels of social mistrust and increased needs to moralize other people’s sexual behaviors. Finally, stratified k-fold cross-validation demonstrates that the structures of these association patterns are robust to sampling variability and reliable enough to generalize to out-of-sample data.

scholarly journals DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment

2020 ◽  
Vol 21 (16) ◽  
pp. 5710
Author(s):  
Xiao Wang ◽  
Yinping Jin ◽  
Qiuwen Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Protein Localization ◽  
Mitochondrial Protein ◽  
Intermembrane Space ◽  
The Matrix ◽  
Localization Predictor ◽  
High Level ◽  
Fold Cross Validation

Mitochondrial proteins are physiologically active in different compartments, and their abnormal location will trigger the pathogenesis of human mitochondrial pathologies. Correctly identifying submitochondrial locations can provide information for disease pathogenesis and drug design. A mitochondrion has four submitochondrial compartments, the matrix, the outer membrane, the inner membrane, and the intermembrane space, but various existing studies ignored the intermembrane space. The majority of researchers used traditional machine learning methods for predicting mitochondrial protein localization. Those predictors required expert-level knowledge of biology to be encoded as features rather than allowing the underlying predictor to extract features through a data-driven procedure. Besides, few researchers have considered the imbalance in datasets. In this paper, we propose a novel end-to-end predictor employing deep neural networks, DeepPred-SubMito, for protein submitochondrial location prediction. First, we utilize random over-sampling to decrease the influence caused by unbalanced datasets. Next, we train a multi-channel bilayer convolutional neural network for multiple subsequences to learn high-level features. Third, the prediction result is outputted through the fully connected layer. The performance of the predictor is measured by 10-fold cross-validation and 5-fold cross-validation on the SM424-18 dataset and the SubMitoPred dataset, respectively. Experimental results show that the predictor outperforms state-of-the-art predictors. In addition, the prediction of results in the M983 dataset also confirmed its effectiveness in predicting submitochondrial locations.

scholarly journals Machine learning meets pKa

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 113 ◽  
Author(s):  
Marcel Baltruschat ◽  
Paul Czodrowski
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
External Validation ◽  
Absolute Error ◽  
Source Model ◽  
Squared Error ◽  
Fold Cross Validation ◽  
Better Than

We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

scholarly journals Deep learning based DNA:RNA triplex forming potential prediction

2020 ◽  
Author(s):  
Yu ZHANG ◽  
Yahui Long ◽  
Chee Keong Kwoh
Keyword(s):  
Cross Validation ◽  
Roc Curves ◽  
Triplex Dna ◽  
Triplex Formation ◽  
Machine Learning Model ◽  
Non Coding Rnas ◽  
High Level ◽  
Cis And Trans ◽  
Integrated Program ◽  
Fold Cross Validation

Abstract Background: Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: i) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and ii) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data. Results: In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the 5-fold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarise the cis and trans targeting of triplexes lncRNAs. Conclusions: The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

scholarly journals Predictive multivariate modelling of religiosity, prosociality and moralizing in 295,000 individuals from European and non-European populations

2020 ◽  
Author(s):  
Pierre O. Jacquet ◽  
Farid Pazhoohi ◽  
Charles Findling ◽  
Hugo Mell ◽  
Coralie Chevallier ◽  
...  
Keyword(s):  
Large Scale ◽  
Alternative Explanation ◽  
Cross Validation ◽  
Positive Association ◽  
Collective Actions ◽  
Individual Strategy ◽  
Multivariate Modelling ◽  
Prosocial Attitudes ◽  
High Level ◽  
Fold Cross Validation

Why do moral religions exist? An influential explanation is that religious beliefs in supernatural punishment is cultural group adaptation enhancing prosocial attitudes and thereby large-scale cooperation. An alternative explanation is that religiosity is an individual strategy that results from high level of mistrust and the need for individuals to control others’ behaviours through moralizing. Existing evidence is mixed but most works are limited by sample size and generalizability issues. The present study overcomes these limitations by applying k-fold cross-validation on multivariate modelling of data from more than 295,000 individuals in 108 countries of the World Values Surveys and the European Value Study. This methodology demonstrates that in European as well as in non-European samples, religious people invest less in collective actions and are more mistrustful of others. By contrast, we find a strong and positive association between higher level of mistrust, higher level of moralizing and higher level of religiosity.

Adolescent age estimation using voice features

2020 ◽  
Vol 65 (4) ◽  
pp. 429-434
Author(s):  
Marcin D. Bugdol ◽  
Monika N. Bugdol ◽  
Maria J. Bieńkowska ◽  
Anna Lipowicz ◽  
Agata M. Wijata ◽  
...  
Keyword(s):  
Age Estimation ◽  
Cross Validation ◽  
Rapid Development ◽  
Absolute Error ◽  
Regression Method ◽  
Chronological Age ◽  
Height Measurement ◽  
Voice Signal ◽  
Selection Operator ◽  
Fold Cross Validation

AbstractIn this paper, a method for evaluating the chronological age of adolescents on the basis of their voice signal is presented. For every examined child, the vowels a, e, i, o and u were recorded in extended phonation. Sixty voice parameters were extracted from each recording. Voice recordings were supplemented with height measurement in order to check if it could improve the accuracy of the proposed solution. Predictor selection was performed using the LASSO (least absolute shrinkage and selection operator) algorithm. For age estimation, the random forest (RF) for regression method was employed and it was tested using a 10-fold cross-validation. The lowest absolute error (0.37 year ± 0.28) was obtained for boys only when all selected features were included into prediction. In all cases, the achieved accuracy was higher for boys than for girls, which results from the fact that the change of voice with age is larger for men than for women. The achieved results suggest that the presented approach can be employed for accurate age estimation during rapid development in children.

scholarly journals Classification of Mental Tasks from EEG Signals Using Spectral Analysis, PCA and SVM

2018 ◽  
Vol 18 (1) ◽  
pp. 81-92 ◽  
Author(s):  
Nikolay N. Neshov ◽  
Agata H. Manolova ◽  
Ivo R. Draganov ◽  
Krasimir T. Tonschev ◽  
Ognian L. Boumbarov
Keyword(s):  
Cross Validation ◽  
Recognition Rate ◽  
Main Idea ◽  
Principal Component ◽  
Gaussian Kernel ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Eeg Data ◽  
Leave One Out ◽  
Mental Tasks

Abstract Signals provided by the ElectroEncephaloGraphy (EEG) are widely used in Brain-Computer Interface (BCI) applications. They can be further analyzed and used for thinking activity recognition. In this paper we proposed an algorithm that is able to recognize five mental tasks using 6 channel EEG data. The main idea is to separate the raw EEG signals into several frames and compute their spectrums. Next, a second-order derivative of Gaussian is applied to extract features and an optimum Gaussian kernel parameters grid search is performed with the help of cross-validation. The extracted features are further reduced by Principal Component Analysis. The processed data is utilized to train SVM classifier which is used for mental tasks recognition afterwards. The performance of the algorithm is estimated on publically available dataset. In terms of 5 folds cross-validation we obtained an average of 82.7% recognition rate (accuracy). Additional experiments were conducted using leave-one-out cross-validation where 67.2% correct classification was reported. Comparison to several state-of-the art methods reveals the advantages of the proposed algorithm.

scholarly journals Khmer Treebank Construction via Interactive Tree Visualization

2019 ◽  
Vol 3 (3) ◽  
pp. 67
Author(s):  
Bonpagna Kann ◽  
Thodsaporn Chay-intr ◽  
Hour Kaing ◽  
Thanaruk Theeramunkong
Keyword(s):  
Language Processing ◽  
Cross Validation ◽  
The Other ◽  
Pos Tagging ◽  
Grammar Rules ◽  
High Level ◽  
Leave One Out ◽  
Self Consistency ◽  
Fold Cross Validation ◽  
Tree Visualization

Despite the fact that there are a number of researches working on Khmer Language in the field of Natural Language Processing along with some resources regarding words segmentation and POS Tagging, we still lack of high-level resources regarding syntax, Treebanks and grammars, for example. This paper illustrates the semi-automatic framework of constructing Khmer Treebank and the extraction of the Khmer grammar rules from a set of sentences taken from the Khmer grammar books. Initially, these sentences will be manually annotated and processed to generate a number of grammar rules with their probabilities once the Treebank is obtained. In our experiments, the annotated trees and the extracted grammar rules are analyzed in both quantitative and qualitative way. Finally, the results will be evaluated in three evaluation processes including Self-Consistency, 5-Fold Cross-Validation, Leave-One-Out Cross-Validation along with the three validation methods such as Precision, Recall, F1-Measure. According to the result of the three validations, Self-Consistency has shown the best result with more than 92%, followed by the Leave-One-Out Cross-Validation and 5-Fold Cross Validation with the average of 88% and 75% respectively. On the other hand, the crossing bracket data shows that Leave-One-Out Cross Validation holds the highest average with 96% while the other two are 85% and 89%, respectively.

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