Hasil untuk "machine learning"

Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Laila Tajeldin, Hein Venter

This study implements the proposed integration of machine learning (ML) techniques into the ISO/IEC 27043:2015 international standard processes using a hypothetical case scenario for a smart building. ISO/IEC 27043:2015 does not currently incorporate ML techniques. Incorporating these techniques into ISO/IEC 27043:2015 can improve the efficiency of the processes and reduce time and human effort by automating some manual tasks of the readiness processes. This research presents a case study for the smart building dataset, applying ML techniques to implement the ML readiness model in the ISO/IEC 27043:2015 standard. It compares the results of implementing ML techniques. These results indicate how the smart environment data can be proactively analysed and classified. These techniques will enable investigators to access the information to investigate such environments.

Kundan Meshram

Paul E. Utgoff

Eric Carter, Matthew Hurst

Gerhard Widmer, Miroslav Kubat

David A. McAllester

Michael J. Kearns, Robert E. Schapire, Linda M. Sellie

Thomas R. Amoth, Paul Cull, Prasad Tadepalli

William W. Cohen

Balay Ninisha

Ansh Kataria

Text classification is a difficult technique. Many techniques have been developed to decrease the dimension of feature vectors for use in text classification due to their enormous size. This work provides a detailed discussion of unique parameters utilising an optic clustering strategy, as well as a review of some of the most essential text categorization algorithms. In this case, the words are clustered according to their level of similarity. Each cluster's membership function is based on the mean along with the standard deviation of its data. Finally, characteristics are chosen from each grouping. Each cluster's extracted feature is the weighted sum of its words. There's also no need to guess or use trial-and-error approaches to determine the optimal number of clusters.

Changhe Yang

Yuejiuhui Gao

Claudine Tinsman, Calum Inverarity, Gefion Thuermer et al.

Ben Hiett, Peter Boyd, Charles Fletcher et al.

Yoshihiro Yamanishi, Hisashi Kashima

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.