Papers by Mohd Arfian Ismail
Review on Intrusion Detection System Based on The Goal of The Detection System, 2018
An extensive review of the intrusion detection system (IDS) is presented in this paper. Previous ... more An extensive review of the intrusion detection system (IDS) is presented in this paper. Previous studies review the IDS based on the approaches (algorithms) used or based on the types of the intrusion itself. The presented paper reviews the IDS based on the goal of the IDS (accuracy and time), which become the main objective of this paper. Firstly, the IDS were classified into two types based on the goal they intend to achieve. These two types of IDS were later reviewed in detail, followed by a comparison of some of the studies that have earlier been carried out on IDS. The comparison is done based on the results shown in the studies compared. The comparison shows that the studies focusing on the detection time reduce the accuracy of the detection compared to other studies.
Newton Competitive Genetic Algorithm Method for Optimization the Production of Biochemical Systems, 2018
In this work, the optimization of biochemical systems production is performed by using a hybrid m... more In this work, the optimization of biochemical systems production is performed by using a hybrid method of Newton competitive
genetic algorithm is presented. The proposed method works by representing the biochemical systems as a generalized mass
action model, where it leads to the process of solving a complex non-linear equations system. The optimization process
becomes hard and difficult when it involves multi-objective problem. This is where two objectives, namely the maximize the
biochemical systems production and minimize the total amount of chemical concentrations involves. To deal with the problem,
this work proposed a hybrid method of the Newton method, genetic algorithm, and competitive co-evolutionary algorithm. The
proposed method was experimentally applied on the benchmark biochemical systems and the experimental results showed that
the proposed method achieved better results compared to the existing works.
This paper present a hybrid method of Newton method, Differential Evolution Algorithm (DE) and Co... more This paper present a hybrid method of Newton method, Differential Evolution Algorithm (DE) and Cooperative Coevolution Algorithm (CCA). The proposed method is used to solve the optimisation problem in optimise the production of biochemical systems. The problems are maximising the biochemical systems production and simultaneously minimising the total amount of chemical reaction concentration involves. Besides that, the size of biochemical systems also contributed to the problem in optimising the biochemical systems production. In the proposed method, the Newton method is used in dealing biochemical system, DE for optimisation process while CCA is used to increase the performance of DE. In order to evaluate the performance of the proposed method, the proposed method is tested on two benchmark biochemical systems. Then, the result that obtained by the proposed method is compare with other works and the finding shows that the proposed method performs well compare to the other works.
Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method, 2017
In this paper, an improved method of multi-objective optimization for biochemical system producti... more In this paper, an improved method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contains many components. In addition, the multi-objective problem also needs to be considered. Due to that, this study proposed and improved a method that comprises with Newton method, differential evolution algorithm (DE) and competitive co-evolutionary algorithm (ComCA). The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems, and the experimental results show that the proposed method performs well compared to other works.
A Hybrid of Optimization Method for Multi-Objective Constraint Optimization of Biochemical System Production, 2015
In this paper, an advance method for multi-objective constraint optimization method of biochemica... more In this paper, an advance method for multi-objective constraint optimization method of biochemical system production was proposed and discussed in detail. The proposed method combines Newton method, Strength Pareto Evolutionary Algorithm (SPEA) and Cooperative Co-evolutionary Algorithm (CCA). The main objective of the proposed method was to improve the desired production and at the same time to reduce the total of component concentrations involved in producing the best result. The proposed method starts with Newton method by treating the biochemical system as a non-linear equations system. Then, Genetic Algorithm (GA) in SPEA and CCA were used to represent the variables in non-linear equations system into multiple sub-chromosomes. The used of GA was to improve the desired production while CCA to reduce the total of component concentrations involved. The effectiveness of the proposed method was evaluatedusing two benchmark biochemical systems and the experimental results showed that the proposed method was able to generate the highest results compare to other existing works.
An Improved Method of Newton Method, Genetic Algorithm and Cooperative Coevolutionary Algorithm for Optimization of Metabolic Pathway Production, 2015
In this study, an improved method for optimization of metabolic pathway was presented. The propos... more In this study, an improved method for optimization of metabolic pathway was presented. The proposed method combines Newton method, Genetic Algorithm (GA) and Cooperative Coevolutionary Algorithm (CCA). The aim of the proposed method was to improve the metabolic pathway production and at the same time reduce the total chemical reaction concentration involved. The proposed method started with Newton method that treated the metabolic pathway as a nonlinear equations system. Then, GA and CCA were used to represent the variables in nonlinear equations system as candidate solutions in the optimization process. GA was used to improve the production, while CCA minimized the total chemical reactions concentration involved. The proposed method was tested on Escherichia coli pathway, and several comparisons with previous works were made. The result showed that the proposed method perform well compared to previous works
The fuzzy cooperative genetic algorithm (FCoGA): The optimisation of a fuzzy model through incorporation of a cooperative coevolutionary method, 2011
Genetic Algorithms (GA) have been widely used to represent parameters in a fuzzy system. However,... more Genetic Algorithms (GA) have been widely used to represent parameters in a fuzzy system. However, when a fuzzy
system is applied to a complex problem, GA tends to lose their effectiveness because of the representation complexity of the
solution. In this paper, an improved method of fuzzy modelling called as Fuzzy Cooperative Genetic Algorithm (FCoGA) is
introduced. Cooperative Coevolution (CC) is applied to the GA by subdividing the chromosome into three sub-chromosomes
known as species, and thus reducing the representation complexity of the solution. Furthermore, two-level evaluations in the
FCoGA, at the species level and cooperative chromosome level, are introduced to improve the performance. To measure the
performance of FCoGA, two benchmark datasets namely Wisconsin Breast Cancer Diagnosis (WBCD) and Pima Indian
Diabetes (PID) datasets have been used. The experimental results show that FCoGA slightly improves the accuracy rate and
maintains comparable effectiveness with other existing study solutions.
A Hybrid of Newton Method and Genetic Algorithm for Constrained Optimization method of the Production of Metabolic Pathway, 2014
In this work, constrained optimization method of the production in metabolic pathway is presented... more In this work, constrained optimization method of the production in metabolic pathway is presented. The optimization of the production in metabolic pathway is a difficult task due as there are many components in the metabolic pathway. In addition, the condition of the constraint in improving the production of metabolic pathway should also be considered. In order to overcome this situation, this study presents an improved method in constrained optimization of the production in metabolic pathway. The proposed method consists of the Newton method and Genetic Algorithm (GA). The proposed method works with the Newton method by treating metabolic pathway as a non-linear equation system. Then, GA was applied to represent the variables in the non-linear equations system as a chromosome and fine-tune the chromosome to improve the variables. The proposed method was applied on several metabolic pathways to assess its performance. Several comparisons were conducted to evaluate the performance of the proposed method, and it was shown that the proposed method works well compared to the other methods.
A Newton Cooperative Genetic Algorithm Method for In Silico Optimization of Metabolic Pathway Production, 2015
This paper presents an in silico optimization method of metabolic pathway production. The metabol... more This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.
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Papers by Mohd Arfian Ismail
genetic algorithm is presented. The proposed method works by representing the biochemical systems as a generalized mass
action model, where it leads to the process of solving a complex non-linear equations system. The optimization process
becomes hard and difficult when it involves multi-objective problem. This is where two objectives, namely the maximize the
biochemical systems production and minimize the total amount of chemical concentrations involves. To deal with the problem,
this work proposed a hybrid method of the Newton method, genetic algorithm, and competitive co-evolutionary algorithm. The
proposed method was experimentally applied on the benchmark biochemical systems and the experimental results showed that
the proposed method achieved better results compared to the existing works.
system is applied to a complex problem, GA tends to lose their effectiveness because of the representation complexity of the
solution. In this paper, an improved method of fuzzy modelling called as Fuzzy Cooperative Genetic Algorithm (FCoGA) is
introduced. Cooperative Coevolution (CC) is applied to the GA by subdividing the chromosome into three sub-chromosomes
known as species, and thus reducing the representation complexity of the solution. Furthermore, two-level evaluations in the
FCoGA, at the species level and cooperative chromosome level, are introduced to improve the performance. To measure the
performance of FCoGA, two benchmark datasets namely Wisconsin Breast Cancer Diagnosis (WBCD) and Pima Indian
Diabetes (PID) datasets have been used. The experimental results show that FCoGA slightly improves the accuracy rate and
maintains comparable effectiveness with other existing study solutions.
genetic algorithm is presented. The proposed method works by representing the biochemical systems as a generalized mass
action model, where it leads to the process of solving a complex non-linear equations system. The optimization process
becomes hard and difficult when it involves multi-objective problem. This is where two objectives, namely the maximize the
biochemical systems production and minimize the total amount of chemical concentrations involves. To deal with the problem,
this work proposed a hybrid method of the Newton method, genetic algorithm, and competitive co-evolutionary algorithm. The
proposed method was experimentally applied on the benchmark biochemical systems and the experimental results showed that
the proposed method achieved better results compared to the existing works.
system is applied to a complex problem, GA tends to lose their effectiveness because of the representation complexity of the
solution. In this paper, an improved method of fuzzy modelling called as Fuzzy Cooperative Genetic Algorithm (FCoGA) is
introduced. Cooperative Coevolution (CC) is applied to the GA by subdividing the chromosome into three sub-chromosomes
known as species, and thus reducing the representation complexity of the solution. Furthermore, two-level evaluations in the
FCoGA, at the species level and cooperative chromosome level, are introduced to improve the performance. To measure the
performance of FCoGA, two benchmark datasets namely Wisconsin Breast Cancer Diagnosis (WBCD) and Pima Indian
Diabetes (PID) datasets have been used. The experimental results show that FCoGA slightly improves the accuracy rate and
maintains comparable effectiveness with other existing study solutions.