Ahmed N. AL-Masri
American University in the Emirates, UAE
Dr. Ahmed AL-Masri received his Ph.D degree in the field of Artificial Intelligence application from University Putra Malaysia. He has more than 6 years’ experience in teaching, programming and research. He involved in many project used artificial neural network system such as forecasting, online monitoring, smart grid, security assessment in electrical power system and dynamic system stability. He is acting as a reviewer for various International and National Journals, and Member of the Institute of Electrical and Electronic Engineers (IEEE). His professional expertise is in the design, analysis of artificial intelligence system, security assessment, parallel processing, virtualization, cloud computing and system automation.
Big Data analytics is one of the great trials in Learning Machine algorithms, as most of real life applications include a massive information or big data knowledge base. On the other hand, the artificial intelligent system with data knowledge base should be able to compute the result in very accurate and fast manner. This paper focused on the challenges and solutions of using Learning Machine with Big Data. Data processing is a mandatory step to transform Big Data which is unstructured into meaningful and optimized data set in any LM module. However, it is necessary to deploy an optimized data set to support the distributed processing and real-time application. This work also reviewed the current used technologies on the Big Data analysis and LM computation. The revision emphasizes on the viability of using different solution for a certain applications could increase the performance of LM. The new development especially in cloud computing and data transaction speed gives more advantages to the practical use of artificial intelligence applications.