Special Issue "Machine Learning for Data Analytics"

Submission Deadline: 31 January 2021 (closed)
Guest Editors
Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Mohammad Ayoub Khan, University of Bisha, Saudi Arabia.
Dr. Surbhi Bhatia, King Faisal University, Saudi Arabia.
Dr. Kapal Dev, CONNECT Centre, Trinity College Dublin, Ireland.


Data Science is gaining tremendous popularity in cyber world. Currently It is very active topic and has extensive scope, both in term of theory and applications. It has an enormous effect on improving business productivity and performance. Data science can be defined as an interdisciplinary field involving techniques to collect, store, analyze, manage and publish data.

Machine Learning is one of the core components of its foundation, which addressed the different important challenges of data science by using different innovative machine learning algorithms and methodologies. This special issue focuses on the latest developments in Machine Learning foundations of data science, as well as on the integration between data science and machine learning. We welcome new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science.

• Data science and analytics
• Data mining and big data analysis
• Intelligent systems
• Machine and deep learning

Published Papers
  • A Comprehensive Review on Medical Diagnosis Using Machine Learning
  • Abstract The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine… More
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