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  • Open Access


    Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

    Nandhi Kesavan*, Latha

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 327-337, 2023, DOI:10.32604/iasc.2023.030910

    Abstract Climate change is the most serious causes and has a direct impact on biodiversity. According to the world’s biodiversity conservation organization, reptile species are most affected since their biological and ecological qualities are directly linked to climate. Due to a lack of time frame in existing works, conservation adoption affects the performance of existing works. The proposed research presents a knowledge-driven Decision Support System (DSS) including the assisted translocation to adapt to future climate change to conserving from its extinction. The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable… More >

  • Open Access


    Function of Palm Fiber in Stabilization of Alluvial Clayey Soil in Yangtze River Estuary

    Jili Qu*, Hao Zhu*

    Journal of Renewable Materials, Vol.9, No.4, pp. 767-787, 2021, DOI:10.32604/jrm.2021.013816

    Abstract Palm fiber is one of the favorable materials used in stabilization of soft soil in geotechnical engineering projects in recent years due to its nature of sustainability, no harm to the environment, biodegradability, availability and cost-effectiveness in the context of widespread appeal from the world for returning to nature and protecting the earth our homestead. This paper is aimed at exploring the mechanical performance of Shanghai clayey soil reinforced with palm fiber. The unconfined compressive tests are carried out on samples treated with palm fibers of different lengths and contents, and the unconfined compressive strength (UCS), ductility rate (DR), secant… More >

  • Open Access


    A Z-Number Valued Regression Model and Its Application

    Lala M. Zeinalovaa, O. H. Huseynovb, P. Sharghic

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 187-192, 2018, DOI:10.1080/10798587.2017.1327551

    Abstract Regression analysis is widely used for modeling of real-world processes in various fields. It should be noted that information relevant to real-world processes is characterized by imprecision and partial reliability. This involves combination of fuzzy and probabilistic uncertainties. Prof.. L. Zadeh introduced the concept of a Z-number as a formal construct for dealing with such information. The present stateof-the-art of regression analysis under Z-number valued information is very scarce. In this paper we consider a Z-number valued multiple regression analysis and its application to a real-world decisionmaking problem. The obtained results show applicability of the proposed approach. More >

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