KE Sharquie, AA Khorsheed, AA Al-Nuaimy, Saudi Medical Journal, 2007 - Cited by 91

Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

KE Sharquie, AA Noaimi, MS Al-Zoubaidi, Journal of Cosmetics, Dermatological Sciences and Applications, 2015 - Cited by 8

KE Sharquie, WS Al-Dori, IK Sharquie, AA Al–Nuaimy, Hospital, 2004 - Cited by 20

KE Sharquie, AA Noaimi, S Al-Hashimy, IGF Al-Tereihi, The Iraqi Postgraduate Medical Journal, 2013 - Cited by 5

Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.

HR Al-Hamamy, KE Sharquie, AA Noaimi, WN Hussein, Our Dermatology Online, 2014 - Cited by 6

    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A