Last year the answer was obvious, at least in Silicon Valley. The back-bending labour of office workers in the 21st century ...
Abstract: Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be ...
Abstract: Machine learning methods are becoming more and more popular in the field of computer-aided drug design. The specific data characteristic, including sparse, binary representation as well as ...
Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...
Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored ...
In supervised learning, a set of input variables, such as blood metabolite or gene expression levels, are used to predict a quantitative response variable like hormone level or a qualitative one such ...
In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among the most widely used machine learning methods for the identification of new active compounds. In ...
1 Faculty of Computing, Botho University-Maseru Campus, Maseru, Lesotho. 2 Department of Physics and Electronics, National University of Lesotho, Roma, Lesotho. Classical machine learning, which is at ...