During the last few years, important advances have been made in big data exploration,
complex pattern recognition and prediction of complex variables. Machine learning (ML) …

Abstract Machine learning (ML) is an artificial intelligence technology that has been used in
atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles …

Chemical engineers rely on models for design, research, and daily decision-making, often
with potentially large financial and safety implications. Previous efforts a few decades ago to …

A wide range of dyes are being disposed in water bodies from several industrial runoff and
the quantity is rapidly increasing over the years. From an environmental safety point of view …

The presence of various forms of heavy metals (HMs)(eg, Cu, Cd, Pb, Zn, Cr, Ni, As, Co, Hg,
Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and …

Chemical Product Engineering (CPE) is marked by numerous challenges, such as the
complexity of the properties–structure–ingredients–process relationship of the different …

Modern studies in the field of environment science and engineering show that deterministic
models struggle to capture the relationship between the concentration of atmospheric …

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients,
heavy metals and persistent organic pollutants. For the modeling and optimization of …

Absorption, distribution, metabolism, and excretion (ADME), which collectively define the
concentration profile of a drug at the site of action, are of critical importance to the success of …

The development of machine learning-based ensemble models for the prediction of complex
processes with non-linear nature (such as adsorption) has been remarkably advanced over …