Objective and Motivation

Machine learning is a concept which aims to learn from given data to make accurate predictions and intelligent decisions from this data. This concept is being used decades ago in many applications such as medical applications, biometric authentication, image retrieval, and others. Since the emerging of the Big Data, obtained from different field such as meteorology, genomics, finance, healthcare, social media and web data, the importance of the machine learning became very crucial for the decision makers. Optimization is one of the statistical learning approaches. It could be used in various applications to find the minimal cost, maximal profit, minimal error, optimal design, and others. It has a vital role in designing smart/intelligent systems, e.g. recommender systems, search engines, image retrieval/recognition, computer vision and others. The advance of these systems requires the development of new optimization techniques to enhance the learning algorithms of the applied machine learning techniques. In general, optimization provide offers a valuable framework for reasoning about, formulating, analyzing, and solving different problems in machine learning.
The aim of this session is to attract researchers and practitioners from academia and industry, and provide a discussion environment in order to share their experiences of the state-of-the-art in optimization relevant to machine learning

Scope and Interests

Machine Learning and optimization are currently very important techniques for many computing applications. Examples of these applications include data-mining, data analytics and features extraction of Big Data, information retrieval, speech recognition, computer vision. They can also be used to improve the performance of security, and privacy techniques. The session welcomes Submissions must be original and should not have been published previously. Topics of interest include but are not limited to:

• Novel machine learning techniques
• Novel optimization algorithms and its application in machine learning application
• Crowd-sourcing and machine learning
• Big data analytics based on machine learning techniques
• Theoretical of machine learning and optimization algorithms
• Nature inspired machine learning algorithms
• Evolution-based machine learning approaches
• Biological inspired Optimization
• Large Scale Optimization
• New feature extraction techniques


Paper Submission Important Dates

The authors are invited to submit their contributions formatted according to the paper template posted on the http://www.iccmit.net/. Submitted papers will be refereed by at least two reviewers for quality, correctness, originality, and relevance. All accepted papers will be published in the Journal of Procedia Computer Science by ELSEVIER which is indexed by SCOUPS: the largest abstract and citation database of peer-reviewed literature and which will be available online on Sciencedirect.com. Furthermore, selected papers from the ICCMIT 2015 Conference Proceeding will be submitted in a number of international Journals, e.g. International journal of computer sciences and systems (IJCSS), ISSN 1803-8328, and Journal of communication and Information Technology (RTIC), ISSN 2237-5112.


Session Organizers:

Chair: Prof. Aboul Ella Hassanien
Faculty of Computers and Information Science
Cairo University, Cairo, Egypt
Beni-Suef University, Beni-suef, Egypt
Scientific Research Group in Egypt (www.egyptscience.net)
E-mail: aboitcairo@gmail.com

Co-chair: Dr. Tarek Gaber
Faculty of Computers & Informatics
Suez Canal University, Old Campus, Ismailia, Egypt.
Member at SRGE Research Group.
IT4Innovations, VŠB-Technical University of Ostrava,
17. listopadu 15/2172, 708 33 Ostrava - Poruba,
Czech Republic.
Email: Tarek.gaber@vsb.cz

Co-chair Dr. Mohamed Mostafa Fouad
Arab Academy for Science, Technology, and Maritime Transport, Cairo, Egypt
Member of SRGE Research Group.
IT4Innovations, VŠB-Technical University of Ostrava,
17. listopadu 15/2172, 708 33 Ostrava - Poruba,
Czech Republic.
E-mail: mohamed.mostafa@vsb.cz