Solar radiation estimation in different climates with meteorological variables using Bayesian model averaging and new soft computing models

Zhang, Guodao; Band, Shahab S.; Jun, Changhyun; Bateni, Sayed M.; Chuang, Huan-Ming; Turabieh, Hamza; Mafarja, Majdi; Mosavi, Amir; Moslehpour, Massoud

doi:10.1016/j.egyr.2021.10.117

Cited by 30 publications

(13 citation statements)

References 54 publications

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“…Currently the sample size is limited to 150 people, which is not ideal for implementing the machine learning methods. However, in some cases, e.g., [45][46][47][48][49][50][51][52][53][54][55], the smaller sample cases and limited data had been also used for modeling the hydrological models. Application to medical and social sciences on the other hand needs more datapoints to fit a correct model.…”

Section: Resultsmentioning

confidence: 99%

Investigating the predictive contribution of attitude towards life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation

Rituraj¹

2023

Preprint

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The purpose of the present study was to investigate the predictive contribution of attitude to life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation. The current research is fundamental in terms of its purpose and descriptive of correlation type in terms of method. The statistical population consists of all cancer patients of Shahinshahr city. The sample size was 150 people based on Cochran's formula and the country's corona situation. The sampling method of the current research is available. This research used tools of attitude to life, belief system, self-resilience, psychological toughness, and emotion regulation, all of which had acceptable reliability and validity. The research data was done in two parts, descriptive and inferential, with the help of Excel, SPSS, and SmartPLS software. The results showed that the attitude towards life and the belief system with the role of mediating emotion regulation has a significant predictive ability on the self-resilience of cancer patients. Also, attitude towards life and belief systems with the role of mediating emotion regulation can predict the psychological toughness of cancer patients. In addition, emotional regulation has a significant relationship with self-resilience and psychological toughness. According to the research results, it can be concluded that the attitude towards life and the belief system mediating emotion regulation can predict cancer patients' resilience and psychological toughness. Also, the attitude toward life and the belief system directly impacts. In addition, attitude towards life has a direct positive relationship with psychological toughness.

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Section: Resultsmentioning

confidence: 99%

Investigating the predictive contribution of attitude towards life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation

Rituraj¹

2023

Preprint

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“…Future challenges and problems will result from the real-time integration of big data analytics in a smart grid together with effective protocols. A sus-tainable future is hampered by several factors, including scalability, achieving an effective and efficient approach, data protection, trustworthy algorithm performance under unfa-vorable circumstances, and operator expertise in artificial intelligence tools [72,73]. There are still several important scientific challenges to solve in order to get over these limitations and meet the demands of the upcoming smart grid [74].…”

Section: Discussionmentioning

confidence: 99%

https://www.academia.edu/93557562/Predicting_Power_and_Hydrogen_Generation_of_a_Renewable_Energy_Converter_Utilizing_Data_Driven_methods_A_Sustainable_Smart_Grid_Case_Study

Rituraj¹

2022

Preprint

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This study proposes a data-driven methodology for modeling power and hydrogen generation of a sustainable energy converter. The wave and hydrogen production at different wave heights and wind speeds are predicted. Furthermore, this research emphasizes and encourages the possibility of extracting hydrogen from ocean waves. By using the extracted data from FLOW-3D software simulation and the experimental data from the special test in the ocean, the comparison analysis of two data-driven learning methods is conducted. The results show that the amount of hydrogen production is propor-tional to the amount of generated electrical power. The reliability of the proposed renewable energy converter is further discussed as a sustainable smart grid application.

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“…In the case of logistic regression, according to Raftery [22], the prior distribution of the < = can be a multivariate normal distribution with mean < > +? @ and variance A B which is the inverse of the expected Fisher information matrix for one observation data.…”

Section: Bayesian Model Averaging (Bma)mentioning

confidence: 99%

“…The Bayesian Model Averaging (BMA) approach offers the best alternative solution to this problem [8], [9]. Bayesian model averaging has been applied in other fields such as economic and financial [10]- [13], hydrology [14]- [17], engineering [18], behavioral research [19], environmental [20], agriculture [21], climate and meteorological [22]- [24] and many others.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Model Averaging (BMA) Based on Logistic Regression for Gene Selection and Classification of Animal Tumor Disease on Microarray Data

Kuswanto¹,

Fitriana²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Tumor is one of the deadly diseases which is frequently to be found in animals. However, identifying whether an animal has a tumor still becomes a big challenge. Classification of tumor disease can be done through gene expression, which consists of hundreds of genes, but only a small number of samples is taken. This data structure is called microarray data having the characteristic of highdimensional data. The choice of a single model can be a problem for high-dimensional data because it ignores model uncertainty. This research proposed to use Bayesian Model Averaging (BMA) to model the uncertainty model by averaging the posterior distribution of all best models, weighted by their posterior model probabilities. Selecting relevant genes to diagnose animal tumors is very important; hence, variable selection needs to be carried out. The selection of predictor variables is carried out by using the iterative BMA algorithm.The BMA results showed that from 335 gene expressions, 12 genes were selected to be relevant genes for classifying whether the animals have a tumor or normal. Moreover, from 2 335 possible models formed, 12 of the best models are selected. The accuracy of BMA results is assessed using the Brier Score, resulting from a value indicating that the BMA model is good enough to classify animals, whether they have a tumor or not. This research has proven that BMA with logistic performance has very good predictability; hence, the method can be applied to classify other diseases.

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Solar radiation estimation in different climates with meteorological variables using Bayesian model averaging and new soft computing models

Cited by 30 publications

References 54 publications

Investigating the predictive contribution of attitude towards life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation

Investigating the predictive contribution of attitude towards life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation

https://www.academia.edu/93557562/Predicting_Power_and_Hydrogen_Generation_of_a_Renewable_Energy_Converter_Utilizing_Data_Driven_methods_A_Sustainable_Smart_Grid_Case_Study

Bayesian Model Averaging (BMA) Based on Logistic Regression for Gene Selection and Classification of Animal Tumor Disease on Microarray Data

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