Machine learning (ML) has revolutionised various industries, from manufacturing to governance, and is now making its way into healthcare - a sector traditionally resistant to technological disruptions. ML has achieved human-level performance in various domains of clinical medicine, spanning from oncology...
The second wave is theML/AIcycle, which started in earnest with Generative AI. As we are in the early innings of this cycle, and most companies are very young, we have been liberal in including young startups (a good number of which are seed stage still) in the landscape. Note: thos...
This chapter introduces the application of machine learning to Image Quality Assessment (IQA) in the case of computer-generated images. The classical learning machines, like SVMs, are quickly remained and RVMs are presented to deal with this particular IQA case (noise features learning). A ...
to ensure fast knowledge transfer [48]. Integrating such multiscale datasets represents a new frontier for biomedical research. Broadly, the goal of machine learning (ML) integrative approaches is to generate a
[34,35], and optimized geometries of 1:1 H-bonded complexes [36]. A recent approach by the group of Varnek involves training a support vector machine learning (ML) model on ISIDA fragment descriptors, which take into account both donor and acceptor sites [37,38]. HBA/HBD strengths ...
Machine learning (ML) enables a system to scrutinize data and deduce knowledge. It goes beyond simply learning or extracting knowledge, to utilizing and improving knowledge over time and with experience. In essence, the goal of ML is to identify and exploit hidden patterns in “training” data....
[19,20]. Another application of machine learning that is paving its way is in big data privacy. A large amount of data correlation poses a threat to their privacy. The researchers can ensure data privacy guarantees by utilizing machine learning concepts. By using one such concept of ML is ...
Regression is used commonly to test hypotheses involving causal relationships, with the choice of model being based on its significance and goodness of fit. Classification Learning Classification supervised learning is a form of pattern recognition designed to predict a single, nonnumerical output, or ...
Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communiti
Machine Learning (ML), as a branch of AI, concerns the application of optimised algorithms that allow computers to learn how to handle data more efficiently than traditional approaches and to identify specific trends and patterns (Dey, Citation2016; Qiu, Wu, Ding, Xu, & Feng, Citation2016)....