3. Producción
Browse
Search Results
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Relationship between inclusive education and its development with the philosophy of religion in Peru(2022-10-26)The essential purpose of this research study is to describe the relationship between inclusive education and its development with the philosophy of religion in Peru. Based on the primary data analysis for collecting the data, this research study used informative questions related to the independent and dependent variables. These data were gathered from people who know about inclusive education and development. Some questions were fulfilled by students of schools and colleges. The research study used smart PLS software and generated different results to investigate. Inclusive education and its development is the primary independent variable, including the physical environment, sensory attitude, expectations, and opportunities; these are all sub-parts of an independent variable. The philosophy of religion is the main dependent variable. The composite reliability, discriminant validity, R square, F square, total effects, significant analysis, and the fitness analysis model also describe the Algorithm model of smart PLS related to the inclusive education and development and philosophy of religion. The overall result found a positive and direct relationship between inclusive education and its development with the philosophy of religion in Peru. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An efficient outlier detection with deep learning-based financial crisis prediction model in big data environment(Hindawi Limited, 2022-01-01)As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country's economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches.