Khamis A. Al-Karawi
Diyala University

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Overlapped music segmentation using a new effective feature and random forests Duraid Y. Mohammed; Khamis A. Al-Karawi; Philip Duncan; Francis F. Li
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v8.i2.pp181-189

Abstract

In the field of audio classification, audio signals may be broadly divided into three classes: speech, music and events. Most studies, however, neglect that real audio soundtracks can have any combination of these classes simultaneously. This can result in information loss, thus compromising the knowledge discovery. In this study, a novel feature, “Entrocy”, is proposed for the detection of music in both pure form and overlapping with the other audio classes. Entrocy is defined as the variation of the information (or entropy) in an audio segment over time. Segments, which contain music, were found to have lower Entrocy since there are fewer abrupt changes over time. We have also compared Entrocy with existing music detection features and the entrocy showing a good performance.
Pushing towards ehealth for iraqi hypertensives: an integrated class association rules into SECI model Ahmed Aljuboori; Lubab Ahmed Tawfeeq; Khamis A. Al-Karawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp522-533

Abstract

This paper highlights the barriers that have led to a delay in the implementation of E-Health services in Iraq. A new framework is proposed to improve the E-Health sector using a SECI model which describes how explicit and tacit knowledge is generated, transferred, and recreated in organizations through main stages (socialization, externalization, combination and internalization). Class association rules (CARs) is integrated to mine the SECI model by extracting related rules which correspond to the medical advice. The proposed framework (SECICAR) can be done through a web portal to assemble healthcare professionals, patients in one environment. SECICAR will be applied to the hypertension community to show that disease if left untreated, frequently leads to serious illnesses such as heart disease. The SECICAR aims to facilitate the dissemination of tacit knowledge, which is explicit to hypertensives, in the form of strategies, guidelines and best practices. The validation of the SECICAR results displays satisfactory accuracy and reliability. Heuristic evaluation was used to test the web portal, the participants stating that there were no major issues regarding its usability.