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 Quality of Care Provided by Family Caregivers of Older People With Dementia: Measurement and Interpretations

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 152-152
Author(s):  
Afeez Hazzan
Keyword(s):  
Quality Of Care ◽  
Older People ◽  
Family Caregivers ◽  
Family Caregiver ◽  
Primary Reason ◽  
People With Dementia ◽  
High Level ◽  
The Impact ◽  
The Relationship

Abstract Family caregivers of older people living with dementia are relatives, friends, or neighbors who provide assistance related to this condition, but who are unpaid for the services they provide. Although caregiving could be personally rewarding, many caregivers report a high level of strain. Compared to caregivers of older adults who do not have dementia, family caregivers of older people living with dementia report lower quality-of-life (QoL). In a published systematic review examining the relationship between family caregiver QoL and the quality of care provided, only one study was found to be somewhat relevant. The study suggested that the primary reason for an absence of research into the link between family caregiver QoL and quality of care was the absence of a questionnaire for measuring quality of care in dementia. Therefore, any attempt to investigate the impact of caregiver QoL on the care provided to older people with dementia must first address the lack of an instrument to measure quality of care. To address this issue, we interviewed approximately 20 family caregivers in order to elicit feedback on measurements and interpretation of the quality of care provided by family caregivers of older people living with dementia. Content analysis of the interview transcripts revealed that the quality of relationships with family, caregiver availability to provide or supervise care, and availability of paid or volunteer help are important for the quality of care provided. These results have important implications, particularly for the development of an instrument to measure quality of care in dementia.

scholarly journals Lateral Preference and Inter-limb Asymmetry in Completing Technical Tasks During Official Professional Futsal Matches: The Role of Playing Position and Opponent Quality

2021 ◽  
Vol 12 ◽  
Author(s):  
Luiz H. Palucci Vieira ◽  
Carlos A. Kalva-Filho ◽  
Felipe B. Santinelli ◽  
Filipe M. Clemente ◽  
Sergio A. Cunha ◽  
...  
Keyword(s):  
Accuracy Rate ◽  
World Cup ◽  
Lateral Preference ◽  
Match Play ◽  
Frequency Of Use ◽  
Limb Asymmetry ◽  
Similar Accuracy ◽  
High Level

This study had the purpose of analyzing dominant and non-dominant limb performances (frequency of use and accuracy) during match-play technical actions with ball possession (receiving, passing, and shooting a ball) in professional futsal and also check for the possible influence of playing position and the quality of opponent. We have analyzed data pertaining to eight matches of the FIFA Futsal World Cup Thailand 2012™ in which 76 male professional senior futsal players participated (44 right-footed and 32 left-footed). In total, we coded 5,856 actions (2,550 ball receptions, 3,076 passes, and 230 shoots). Our main findings were that (a) players used the dominant limb more frequently than the non-dominant limb for all actions considered [p < 0.001; effect size (ES) medium-to-large]; (b) accuracy was generally greater when using the dominant limb, regardless of the quality of opponent (p < 0.01; ES large); and (c) in shooting actions, pivots showed similar accuracy between dominant and non-dominant limbs (p = 0.51; ES small). The study suggested that when completing technical actions with the ball in futsal, high-level players depended to a greater extent on the use of their dominant lower limb during official matches. Excepting a similarity detected between limbs on shooting performance of pivots, players from all positional roles generally showed a higher accuracy rate in receiving, passing, and shooting a ball when using their dominant limb as compared to their non-dominant one during match-play and the limb usage and accuracy seemed to be independent of the quality of opponents.

scholarly journals Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests

Land ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 174
Author(s):  
Desheng Wang ◽  
A-Xing Zhu
Keyword(s):  
Random Forests ◽  
Cross Validation ◽  
Soil Mapping ◽  
High Quality ◽  
Integrated Method ◽  
Average Accuracy ◽  
Soil Information ◽  
Fold Cross Validation

Digital soil mapping (DSM) is currently the primary framework for predicting the spatial variation of soil information (soil type or soil properties). Random forests and similarity-based methods have been used widely in DSM. However, the accuracy of the similarity-based approach is limited, and the performance of random forests is affected by the quality of the feature set. The objective of this study was to present a method for soil mapping by integrating the similarity-based approach and the random forests method. The Heshan area (Heilongjiang province, China) was selected as the case study for mapping soil subgroups. The results of the regular validation samples showed that the overall accuracy of the integrated method (71.79%) is higher than that of a similarity-based approach (58.97%) and random forests (66.67%). The results of the 5-fold cross-validation showed that the overall accuracy of the integrated method, similarity-based approach, and random forests range from 55% to 72.73%, 43.48% to 69.57%, and 54.17% to 70.83%, with an average accuracy of 66.61%, 57.39%, and 59.62%, respectively. These results suggest that the proposed method can produce a high-quality covariate set and achieve a better performance than either the random forests or similarity-based approach alone.

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 Collaborative Classification Approach for Airline Tweets Using Sentiment Analysis

2021 ◽  
Vol 12 (3) ◽  
pp. 3597-3603
Author(s):  
M.Veera Kumari Et.al
Keyword(s):  
Sentiment Analysis ◽  
Cross Validation ◽  
Majority Voting ◽  
Quality Of Services ◽  
Classification Methods ◽  
K Nearest Neighbors ◽  
Classification Techniques ◽  
Improve Accuracy ◽  
Fold Cross Validation

In the world there are so many airline services which facilitate different airline facilities for their customers. Those airline services may satisfy or may not satisfy their customers. Customers cannot express their comments immediately, so airline services provide the twitter blog to give the feedback on their services. Twitter has been increased to develop the quality of services[4]. This paper develop the different classification techniques to improve accuracy for sentiment analysis. The tweets of services are classified into three polarities such as positive, negative and neutral. Classification methods are Random forest(RF), Logistic Regression(LR), K-Nearest Neighbors(KNN), Naïve Baye’s(NB), Decision Tree(DTC), Extreme Gradient Boost(XGB), merging of (two, three and four) classification techniques with majority Voting Classifier, AdaBoost measuring the accuracy achieved by the function using 20-fold and 30-fold cross validation was compassed in the validation phase. In this paper proposes a new ensemble Bagging approach for different classifiers[10]. The metrics of sentiment analysis precision, recall, f1-score, micro average, macro average and accuracy are discovered for all above mentioned classification techniques. In addition average predictions of classifiers and also accuracy of average predictions of classifiers was calculated for getting good quality of services. The result describes that bagging classifiers achieve better accuracy than non-bagging classifiers.

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.

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.

Inkjet Quality Ruler Experiments and Print Uniformity Predictor

2020 ◽  
Vol 2020 (9) ◽  
pp. 373-1-373-8
Author(s):  
Yi Yang ◽  
Utpal Sarkar ◽  
Isabel Borrell ◽  
Jan P. Allebach
Keyword(s):  
Cross Validation ◽  
Ground Truth ◽  
Test Sample ◽  
International Committee ◽  
Perceptual Quality ◽  
Psychophysical Experiment ◽  
Technology Standards ◽  
Inkjet Printers ◽  
Fold Cross Validation

Macro-uniformity is an important factor in the overall quality of prints from inkjet printers. The International Committee for Information Technology Standards (INCITS) defined the macrouniformity for prints, which includes several printing defects such as banding, streaks, mottle, etc. Although we can quantitatively analyze a certain kind of defect, it is difficult to assess the overall perceptual quality when multiple defects appear simultaneously in a print. We used the Macro-uniformity quality rulers designed by INCITS W1.1 as experimental references, to conduct a psychophysical experiment for pooling perceptual assessments of our print samples from subjects. Then, calculated features can describe the severity of defects in a test sample; and we trained a predictive model using these data. The predictor can automatically predict the macro-uniformity score as judged by humans. Our results show that the predictor can work accurately. The predicted scores are similar to the subjective visual scores (ground-truth). Also, we used 6-fold cross-validation to confirm the efficacy of our predictor.

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