2022
DOI: 10.1080/15226514.2022.2059056
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An investigation on environmental pollution due to essential heavy metals: a prediction model through multilayer perceptrons

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Cited by 8 publications
(2 citation statements)
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“…Artificial neural networks (ANNs), which do not need to provide similar strict assumptions, as they can overcome this problem, have become very popular, especially in recent years. ANNs are frequently and effectively used in a wide variety of scientific disciplines, such as environmental pollution [8][9][10], biomechanics [11], climatology [12], finance and economy [13], and medical application [14]. In addition, with the aim of predicting the amount of basic heavy metals such as Fe, Mn, and Ni, various machine learning (ML) methods, including ANNs and deep neural networks, have been used in the literature [15][16][17][18][19].…”
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confidence: 99%
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“…Artificial neural networks (ANNs), which do not need to provide similar strict assumptions, as they can overcome this problem, have become very popular, especially in recent years. ANNs are frequently and effectively used in a wide variety of scientific disciplines, such as environmental pollution [8][9][10], biomechanics [11], climatology [12], finance and economy [13], and medical application [14]. In addition, with the aim of predicting the amount of basic heavy metals such as Fe, Mn, and Ni, various machine learning (ML) methods, including ANNs and deep neural networks, have been used in the literature [15][16][17][18][19].…”
mentioning
confidence: 99%
“…The determination of these elements, which have different effects, in plants and soils is of great importance for biomonitoring and environmental pollution studies. Although successful results have been obtained by taking advantage of ANNs and various ML algorithms in these studies, it is also necessary to mention fundamental and crucial shortcomings [10,[41][42][43]. First, all these studies, in the problem they are interested in, assume the relationship between inputs and outputs only in a nonlinear structure.…”
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confidence: 99%