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.

Summary

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.


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

Published Papers
  • A Novel Method Based on UNET for Bearing Fault Diagnosis
  • Abstract Reliability of rotating machines is highly dependent on the smooth rolling of bearings. Thus, it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach. In the recent past, Deep Learning (DL) has become applicable in condition monitoring of rotating machines owing to its performance. This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images. The proposed method is the UNET model that is a recent development in DL models. The model is applied to the 2D… More
  •   Views:410       Downloads:293        Download PDF

  • Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN
  • Abstract Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings. In… More
  •   Views:409       Downloads:327        Download PDF

  • Computer Vision-Control-Based CNN-PID for Mobile Robot
  • Abstract With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed. In this paper, we propose an advanced control for a serving robot. A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions. The robot should be able to follow the trajectory with speed… More
  •   Views:610       Downloads:580        Download PDF

  • 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
  •   Views:814       Downloads:616        Download PDF