Azah Kamilah Muda

Work place: Faculty of Information and Communication Technology (FTMK), Universiti Teknikal Malaysia Melaka, Malaysia



Research Interests: Data Structures and Algorithms, Image Processing, Image Manipulation, Image Compression, Computer Architecture and Organization, Pattern Recognition, Computer systems and computational processes


Azah Kamilah Muda is an Associate Professor and deputy dean of research and post graduate at Faculty of ICT, UTeM. She received her PhD in 2010 from Universiti Teknologi Malaysia, specializing in image processing. Her research interest includes pattern analysis and recognition, image processing, soft computing and computational intelligence. Her current research work is on pattern analysis of molecular computing for drug analysis, sentiment analysis in social network and root cause analysis in manufacturing process. She also serves as Editorial Boards of various international journal such as Applied Soft Computing, International Journal of Computing and Applications (JCA), International Association for Computer Scientists and Engineers (IACSE), International Journal of Advances in Soft Computing and Its Applications (IJASCA), International Journal of Network and Innovative Computing (JNIC) and International Journal of Computer Information Systems and Industrial Management Applications (IJCISM).

Author Articles
Adjustive Reciprocal Whale Optimization Algorithm for Wrapper Attribute Selection and Classification

By Heba F. Eid Azah Kamilah Muda

DOI:, Pub. Date: 8 Mar. 2019

One of the most difficult challenges in machine learning is the data attribute selection process. The main disadvantages of the classical optimization algorithms based attribute selection are local optima stagnation and slow convergence speed. This makes bio¬-inspired optimization algorithm a reliable alternative to alleviate these drawbacks. Whale optimization algorithm (WOA) is a recent bio-inspired algorithm, which is competitive to other swarm based algorithms. In this paper, a modified WOA algorithm is proposed to enhance the basic WOA performance. Furthermore, a wrapper attribute selection algorithm is proposed by integrating information gain as a preprocessing initialization phase. Experimental results based on twenty mathematical optimization functions demonstrate the stability and effectiveness of the modified WOA when compared to the basic WOA and the other three well-known algorithms. In addition, experimental results on nine UCI datasets show the ability of the novel wrapper attribute selection algorithm in selecting the most informative attributes for classification tasks.

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