Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (4)
  • Open Access

    ARTICLE

    The Influence of Internet Use on Women’s Depression and Its Countermeasures—Empirical Analysis Based on Data from CFPS

    Dengke Xu1, Linlin Shen1, Fangzhong Xu2,*

    International Journal of Mental Health Promotion, Vol.26, No.3, pp. 229-238, 2024, DOI:10.32604/ijmhp.2024.046023 - 08 April 2024

    Abstract Based on China Family Panel Studies (CFPS) 2018 data, the multiple linear regression model is used to analyze the effects of Internet use on women’s depression, and to test the robustness of the regression results. At the same time, the effects of Internet use on mental health of women with different residence, age, marital status and physical health status are analyzed. Then, we can obtain that Internet use has a significant promoting effect on women’s mental health, while the degree of Internet use has a significant inhibitory effect on women’s mental health. In addition, the… More >

  • Open Access

    ARTICLE

    Prediction of Residential Building’s Solar Installation Energy Demand in Morocco Using Multiple Linear Regression Analysis

    Nada Yamoul1,*, Latifa Dlimi1, Baraka Achraf Chakir2

    Energy Engineering, Vol.119, No.5, pp. 2135-2148, 2022, DOI:10.32604/ee.2022.020005 - 21 July 2022

    Abstract The building sector is one of the main energy-consuming sectors in Morocco. In fact, it accounts for 33% of the final consumption of energy and records a high increase in the annual consumption of energy caused by further planned large-scale projects. Indeed, the energy consumption of the building sector is experiencing a significant acceleration justified by the rapid need for the development of housing stock, wich is estimated at an average increase of 1,5% per year; furthermore, tant is an estimated increase of about 6,4%. In this sense, building constitutes an important potential source for… More >

  • Open Access

    ARTICLE

    Enrichment of Crop Yield Prophecy Using Machine Learning Algorithms

    R. Kingsy Grace*, K. Induja, M. Lincy

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 279-296, 2022, DOI:10.32604/iasc.2022.019947 - 03 September 2021

    Abstract Strong associations exist between the crop productivity and the seasonal, biological, economical causes in natural ecosystems. The linkages like climatic conditions, health of a soil, growth of crop, irrigation, fertilizers, temperature, rainwater, pesticides desired to be preserved in comprehensively managed crop lands which impacts the crop potency. Crop yield prognosis plays a vibrant part in agricultural planning, administration and environs sustainability. Advancements in the field of Machine Learning have perceived novel expectations to improve the prediction performance in Agriculture. Highly gratifying prediction of crop yield helps the majority of agronomists for their rapid decision-making in… More >

  • Open Access

    ARTICLE

    Research on Prediction Methods of Energy Consumption Data

    Ning Chen1, Naernaer Xialihaer2,3, Weiliang Kong3, Jiping Ren2,3,*

    Journal of New Media, Vol.2, No.3, pp. 99-109, 2020, DOI:10.32604/jnm.2020.09889 - 04 September 2020

    Abstract This paper analyzes the energy consumption situation in Beijing, based on the comparison of common energy consumption prediction methods. Here we use multiple linear regression analysis, grey prediction, BP neural net-work prediction, grey BP neural network prediction combined method, LSTM long-term and short-term memory network model prediction method. Firstly, before constructing the model, the whole model is explained theoretically. The advantages and disadvantages of each model are analyzed before the modeling, and the corresponding advantages and disadvantages of these models are pointed out. Finally, these models are used to construct the Beijing energy forecasting model, More >

Displaying 1-10 on page 1 of 4. Per Page