This special session is organised and supported by Prince Sultan University, Riyadh, Saudi Arabia
Data science is a complex blend of several disciplines including technology, algorithm development, systems and workflows used to obtain insights from data. The enormous emerging progresses and applications of Data Science have been witnessed in a broad range of interdisciplinary and multidisciplinary fields that has high impact on applied and theoretical research areas like machine learning, artificial intelligence, big data analytics, image processing, pattern recognition, search engines, medical informatics, digital economy, robotics, finance, digital humanities, social sciences, text mining, security, IoT, smart education environment, crowd management, forensic, speech recognition and business intelligence and health care. Data Science is a rich family of learning methods, including classification, dimensionality reduction, anomaly detection, Recommender Systems, pattern recognition, sampling, regression, and discriminative analysis.
The promising progress has been observed in Data Science due to the emerging techniques of social computing, big data and deep learning. By now, the great number of research output has been obtained, showing the continued booming and productive research of data science, machine learning, artificial intelligence and their applications.
This special issue aims to bring together the researchers to highlight the significant multidisciplinary scientific problems and major societal issues related to Data Science, Machine Learning & AI, particularly, to address the above-mentioned domains and their recent trends, advances and possible challenges.
Topics of interest include, but are not limited to:
- Data Science Trends & Challenges
- Applications of Data Science
- Machine Learning for Intelligent Systems
- Business Analytics
- Data Visualization
- Image Informatics
- Personalized Medicine
- Networks and Marketing
- Digital Humanities
- Information Security
- Data/Web mining
- Machine Learning & Deep Learning
- Big Data, cloud computing and data-intensive systems
- Innovative data-intensive applications such as health, energy, transport, food, and water, etc.
Prospective authors are invited to submit full-length papers (not exceeding 6 pages) conform to the IEEE format . All papers will be handled and processed electronically via the EDAS online submission system.
Submission implies the willingness of at least one of the authors to register and present their papers.