Comprehensive review of load forecasting with emphasis on intelligent computing approaches

2023

Preprint

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.

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

2023

Preprint

“…Increasing cost efficiency is essential for establishing total competitiveness [53][54][55]. The decrease in operation and maintenance costs is a crucial issue in this regard in addition to sustainable economies of scale brought on by larger wind turbine designs and manufacturing advancements [56][57][58][59][60]. For both mechanical and electrical components, sophisticated early defect diagnosis techniques could be a key factor in low-ering failure costs and maintenance expenses [61].…”

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

2022

Preprint

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.

“…In general, in order to predict this important, other artificial intelligence methods can also be used such as those proposed in e.g., [51][52][53][54][55]. Among them the hybrid machine learning and deep learning which are proven efficient in other applications, e.g., [56][57][58] will be used for breast cancer prediction to explore their suitability and performance.…”

Section: Data Preprocessingmentioning

confidence: 99%

Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast's Tumor

2023

Preprint

Breast cancer is the second leading cause of death after lung cancer. The only possible way to save patients' lives is early diagnosis of the disease; Because if this disease is diagnosed in the early stages and with a high level of accuracy, the chance of survival increases. Different fuzzy-based soft computing techniques have been proposed. In this research, the proposed fuzzy hybrid algorithm - particle swarm has been used to detect the type of breast tumors based on the analysis of features in mammography images. The proposed method in this study, the fuzzy hybrid algorithm - the proposed particle swarm algorithm, has a remarkable performance of 94.58% in breast cancer diagnosis. The results obtained from this study can be used for timely diagnosis and providing effective treatments for breast cancer