Dual-manifold regularized regression models for feature selection based on hesitant fuzzy correlation

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

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Unique personality profiles predict when and why sad music is enjoyed

Psychology of Music ◽

10.1177/0305735620932660 ◽

2020 ◽

pp. 030573562093266

Author(s):

Matthew E. Sachs ◽

Antonio Damasio ◽

Assal Habibi

Keyword(s):

Regression Models ◽

Positive Emotions ◽

Emotional Responses ◽

Reward System ◽

Situational Factors ◽

Regularized Regression ◽

Personality Profiles ◽

Sad Music ◽

The Relationship

The experience of sadness is largely unpleasant, but when expressed through music, it can be pleasurable. Previous research has shown that an attraction to sad music is correlated with personality traits like empathy, Absorption, and rumination. However, the intricacies of the relationship between personality, situational factors, and reasons for engaging with sad music have yet to be fully explored. To address this, participants ( N = 431) reported the situations in which they would listen to sad music and their motivations for doing so. Regularized regression models were employed to assess correlations between personality, situational, and motivational factors. Mediation models were used to determine if emotional responses mediated these associations. People who scored higher on Absorption, the Fantasy component of empathy, and rumination reported enjoying sad music. Absorption and Fantasy were associated with liking sad music because of its ability to regulate/enhance positive emotions. Rumination was associated with liking sad music in tense situations because it both strengthens positive and releases negative emotions. Our results further our understanding of reward responses to negative stimuli by highlighting the role of personality and situational factors. Such findings have implications for the development of interventions for mood disorders, in which music could be used as a tool to regulate emotions and re-engage the reward system.

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Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection

Sensors ◽

10.3390/s20164402 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4402

Author(s):

Pekka Siirtola ◽

Juha Röning

Keyword(s):

Feature Selection ◽

Regression Model ◽

Regression Models ◽

Personal Data ◽

The Other ◽

Training Data ◽

Classification Model ◽

Classification Models ◽

Stress Detection ◽

Personal Training

In this article, regression and classification models are compared for stress detection. Both personal and user-independent models are experimented. The article is based on publicly open dataset called AffectiveROAD, which contains data gathered using Empatica E4 sensor and unlike most of the other stress detection datasets, it contains continuous target variables. The used classification model is Random Forest and the regression model is Bagged tree based ensemble. Based on experiments, regression models outperform classification models, when classifying observations as stressed or not-stressed. The best user-independent results are obtained using a combination of blood volume pulse and skin temperature features, and using these the average balanced accuracy was 74.1% with classification model and 82.3% using regression model. In addition, regression models can be used to estimate the level of the stress. Moreover, the results based on models trained using personal data are not encouraging showing that biosignals have a lot of variation not only between the study subjects but also between the session gathered from the same person. On the other hand, it is shown that with subject-wise feature selection for user-independent model, it is possible to improve recognition models more than by using personal training data to build personal models. In fact, it is shown that with subject-wise feature selection, the average detection rate can be improved as much as 4%-units, and it is especially useful to reduce the variance in the recognition rates between the study subjects.

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FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images

2019 IEEE Visualization Conference (VIS) ◽

10.1109/visual.2019.8933619 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jieqiong Zhao ◽

Morteza Karimzadeh ◽

Ali Masjedi ◽

Taojun Wang ◽

Xiwen Zhang ◽

...

Keyword(s):

Feature Selection ◽

Regression Models ◽

Hyperspectral Images ◽

Interactive Feature

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Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis

Sensors ◽

10.3390/s20236742 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6742

Author(s):

Harsh S. Dhiman ◽

Dipankar Deb ◽

James Carroll ◽

Vlad Muresan ◽

Mihaela-Ligia Unguresan

Keyword(s):

Feature Selection ◽

Support Vector Regression ◽

Condition Monitoring ◽

Regression Models ◽

Point Of View ◽

Support Vector ◽

Twin Support Vector Regression ◽

Feature Selection Technique ◽

Wind Turbine Gearbox ◽

Statistical Point

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models.

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