Shamshul Bahar Yaakob
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Preferred name
Shamshul Bahar Yaakob
Official Name
Shamshul Bahar, Yaakob
Alternative Name
Yaakob, Shamsul Bahar
Yaakob, Shamshul B.
Bahar Yaakob, Shamshul
Yaakob, Sb
Yaakob, S. B.
Yaakob, Shamshul Bahar
Main Affiliation
Scopus Author ID
24825943100
Researcher ID
EDU-6433-2022

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  • Publication
    Investment planning problem in power system using Artificial Neural Network
    ( 2018-12)
    Shamshul Bahar Yaakob
    ;
    Siti Hajar Mohd Tahar
    ;
    Amran Ahmed
    This paper presents a model to solve Distribution Expansion Planning (DEP) problem. An effective method is proposed to determine an optimal solution for strategic investment planning in distribution system. The proposed method will be formulated by using meanvariance analysis (MVA) approach in the form of mixed-integer quadratic programming problem. Its target is to minimize the risk and maximize the expected return. The proposed method consists of two layers neural networks combining Hopfield network at the upper layer and Boltzmann machine in the lower layer resulting the fast computational time. The originality of the proposed model is it will delete the unit of the lower layer, which is not selected in upper layer in its execution. Then, the lower layer is restructured using the selected units. Due to this feature, the proposed model will improve times and the accuracy of obtained solution. The significance of output from this project is the improvement of computational time and the accurate solution will be obtained. This model might help the decision makers to choose the optimal solution with variety options provided from this proposed method. Therefore, the performance of strategic investment planning in solving DEP problem certainly enhance
      3  14
  • Publication
    Power system investment planning based on double layer boltzmann machine method
    ( 2018)
    Shamshul Bahar Yaakob
    Quadratic programming problems arise in many scientific and engineering applications for a long time. Meta-heuristic methods have been employed so as to obtain optimal solutions for the problems such as artificial neural network, genetic algorithm, simulated annealing, ant colony optimization and others. Great progress has been made in research on artificial neural networks in recent decades. Artificial neural networks have been applied to many fields such as pattern recognition, forecasting, data mining, multiple objective decision making and combinatorial optimization problems. Recently, power-supply failures have caused major social losses. Therefore, power supply systems need to be highly reliable. The objective of this research is to present a significant and effective method of determining a productive investment to protect a power supply system from damage. Previous studies have examined the utility and social impact of investment in distributed generation. In this research, an artificial neural network has been applied to solve the portfolio selection problem efficiently. The reliability and risks of each of the units are evaluated with a variance-covariance matrix, and the effects and expenses of replacement are analysed. The mean-variance analysis is formulated as a mathematical program with the following two objectives which are to minimize the risk and to maximize the expected return. Finally, a structural learning model of a mutual connection neural network (double layered Boltzmann machine) is used to solve problems defined by mixed-integer quadratic programming, and is employed in the mean-variance analysis. This method is applied to a power system network in the Tokyo Metropolitan area. As a result, it was shown that the structural learning can provide an alternative solution for decision makers to select the best solution from their respective point of view, as a numerical example shows. The simulation also showed that computational cost is significantly decreased compared with a conventional Boltzmann machine. The obtained results show that the selection, investment expense rate to units and reduced computation time can be prolonged to increase cost savings.
      3  12
  • Publication
    Corneal arcus classification for hyperlipidemia detection using gray level co-occurrence matrix features
    ( 2020-01-07)
    Ramlee R.A.
    ;
    Subramaniam S.K.
    ;
    Shamshul Bahar Yaakob
    ;
    Ahmad Shukri Fazil Rahman
    ;
    Saad N.M.
    The arcus cornea is an eye problem that is often encountered among older people, but the situation is risky to teenagers and young people, in which it gave a sign the presence of lipid abnormalities in their blood and the risk of disease problems such as diabetes and heart disease. This paper presents the classification of the arcus cornea, using the extraction of texture features of the gray level co-occurrence matrix, along with several models of the classifiers, namely as scale conjugate gradient, Bayesian regulation, and Levenberg-Marquardt. Percentage fractions for training, testing and validation for classifier are 70%, 15%, and 15% respectively. The comparison of the classifiers used by the past researchers for classification the eye abnormalities, also were analyzed and studied in this work. In this experiment, a total of 125 image eyes were used, consisting of two classes of the eye image, which is normal and abnormal. The best result demonstrated in this proposed framework using Bayesian regulation classifier is, a sensitivity of 96%, and a specificity of 100%. However, this classifier did not achieve perfectly classification or an accuracy of 100%. Nevertheless, it is able and evident that the system is effective by the output of 98.4% accuracy.
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