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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Nor Ashikin Mohamad Kamal
Universiti Teknologi MARA
Computer Science & IT Engineering

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Feature selection for human membrane protein type classification using filter methods Glenda Anak Kaya; Nor Ashikin Mohamad Kamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (432.355 KB) | DOI: 10.11591/ijai.v8.i4.pp375-381

Abstract

As the number of protein sequences in the database is increasing, effective and efficient techniques are needed to make these data meaningful. These protein sequences contain redundant and irrelevant features that cause lower classification accuracy and increase the running time of the computational algorithm. In this paper, we select the best features using Minimum Redundancy Maximum Relevance (mRMR) and Correlationbased feature selection (CFS) methods. Two datasets of human membrane protein are used, S1 and S2. After the features have been selected by mRMR and CFS, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers are used to classify these membrane proteins. The performance of these techniques is measured using accuracy, specificity and sensitivity. and F-measure. The proposed algorithm managed to achieve 76% accuracy for S1 and 73% accuracy for S2. Finally, our proposed methods present competitive results when compared with the previous works on membrane protein classification.
Prediction Outcome for Massive Multiplayer Online Games Using Data Mining Shazwani Samsurim; Nor Ashikin Mohamad Kamal; Marina Ismail; Norizan Mat Diah
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp248-255

Abstract

Massive Multiplayer Online (MMO) game is one of the famous game genres among teenagers nowadays. MMO games allow gamers to interact and play with up to thousand players. Rainbow Six Siege (RSS) belongs to MMO type of game. However, due to many operators that are available in this game, the player needs to choose the right operator to counter the enemy operator. Therefore, based on the characteristic of the selected operator, this paper attempted to predict the outcomes of the game.  In our prediction model, characteristics for these operators were extracted from 120 live stream replays. Three classification algorithms were utilized to predict the outcome of the game. Among these algorithms, IBK had obtained outstanding performance in the dataset. The accuracy of the model is 93.75%, applying 5-fold cross-validation test. The success rate reveals that our proposed model is suitable to predict the outcome of the game.
Co-Authors Glenda Anak Kaya Marina Ismail Norizan Mat Diah Shazwani Samsurim