The final goal of all industrial machine learning (ML) projects is to develop ML products and
rapidly bring them into production. However, it is highly challenging to automate and …

Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

Recently, Machine Learning (ML) has become a widely accepted method for significant
progress that is rapidly evolving. Since it employs computational methods to teach machines …

Over the past few decades, the substantial growth in enterprise-data availability and the
advancements in Artificial Intelligence (AI) have allowed companies to solve real-world …

Abstract Design-Make-Test-Analyse (DMTA) is the discovery cycle through which molecules
are designed, synthesised, and assayed to produce data that in turn are analysed to inform …

Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements
in drug discovery, propelled by the recent progress in deep neural networks. This review …

Machine Learning (ML) has become a fast-growing, trending approach in solution
development in practice. Deep Learning (DL) which is a subset of ML, learns using deep …

In chemoinformatics and medicinal chemistry, machine learning has evolved into an
important approach. In recent years, increasing computational resources and new deep …

Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are
impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI …

Our previous research applied a novel classification-integrated moving average (CIMA)
method, an intelligence method that improves the performance of passive infrared (PIR) …