Yun-Huoy Choo

Ant colony optimization with look forward ant in solving assembly line balancing problem

This work presents an approach based on the ant colony optimization technique to address the assembly line balancing problem. An improved ant colony optimization with look forward ant is proposed to solve the simple assembly line balancing problem of type 1 (SALBP-1). The proposed algorithm introduces an approach to dynamically assign the value of priority rule or heuristic information during the task selection phase by allowing the ant to look forward its direct successors during the consideration in selecting a task to be assigned into a workstation. The proposed algorithm is tested and compared with literature data sets and the result from the proposed algorithm shows competitive performance against them.

Single channel sEMG muscle fatigue prediction: An implementation using least square support vector machine

Surface electromyogram (sEMG) signal is commonly used for muscle fatigue analysis in clinical rehabilitation studies. Prediction results based on sEMG signals are promising because muscle contradiction can be easily characterized using sEMG signals. However, the prediction results usually deteriorate significantly when noise exist during data acquisition. Noise happens due to many factors ranging from hardware, software to procedure flaws. This investigation is aimed to assess the performance of the Least Square SVM model in predicting muscle fatigue using single channel sEMG signal. The root mean square, median frequency, and mean frequency features were extracted from two sets of raw sEMG signals captured at the multifidus (for low back pain) and flexor carpi radialis (for forearm muscle fatigue) muscles. The proposed LS-SVM technique were used to build the prediction rule-base separately for both the datasets. The implementation, testing and verification were performed in Matlab environment. The k-nearest neighbour and artificial neural network were used as the benchmarking techniques in results comparison and analysis. LS-SVM technique is proven good against the benchmarking techniques on classification accuracy and area under ROC curve. The ANOVA and Tukey HSD post hoc test were used to further validate the significant of the comparison results on both accuracy and AUC measurements.

SOCIFS feature selection framework for handwritten authorship

The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. This paper is meant to propose a novel feature selection framework for Swarm Optimized and Computationally Inexpensive Floating Selection SOCIFS, by exploring existing feature selection frameworks, and compare the performance of proposed feature selection framework against various feature selection methods in Writer Identification in order to find the most significant features. The promising applicability of the proposed framework has been demonstrated in the result and worth to receive further exploration in identifying the handwritten authorship.

PSO and Computationally Inexpensive Sequential Forward Floating Selection in acquiring significant features for handwritten authorship

The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain. This paper is meant to explore the usage of feature selection in Writer Identification in order to find the most significant features. This paper proposes a hybrid feature selection method of Particle Swarm Optimization and Computationally Inexpensive Sequential Forward Floating Selection for Writer Identification. The promising applicability of the proposed method has been demonstrated and worth to receive further exploration in identifying the handwritten authorship.

Computational Intelligence in Digital Forensics: Forensic Investigation and Applications

Computational Intelligence techniques have been widely explored in various domains including forensics. Analysis in forensic encompasses the study of pattern analysis that answer the question of interest in security, medical, legal, genetic studies and etc. However, forensic analysis is usually performed through experiments in lab which is expensive both in cost and time. Therefore, this book seeks to explore the progress and advancement of computational intelligence technique in different focus areas of forensic studies. This aims to build stronger connection between computer scientists and forensic field experts. This book, Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, is the first volume in the Intelligent Systems Reference Library series. The book presents original research results and innovative applications of computational intelligence in digital forensics. This edited volume contains seventeen chapters and presents the latest state-of-the-art advancement of Computational Intelligence in Digital Forensics; in both theoretical and application papers related to novel discovery in intelligent forensics. The chapters are further organized into three sections: (1) Introduction, (2) Forensic Discovery and Investigation, which discusses the computational intelligence technologies employed in Digital Forensic, and (3) Intelligent Forensic Science Applications, which encompasses the applications of computational intelligence in Digital Forensic, such as human anthropology, human biometrics, human by products, drugs, and electronic devices.

A framework of rough reducts optimization based on PSO/ACO hybridized algorithms

Rough reducts has contributed significantly in numerous researches of feature selection analysis. It has been proven as a reliable reduction technique in identifying the importance of attributes set in an information system. The key factor for the success of reducts calculation in finding minimal reduct with minimal cardinality of attributes is an NP-Hard problem. This paper has proposed an improved PSO/ACO optimization framework to enhance rough reduct performance by reducing the computational complexities. The proposed framework consists of a three-stage optimization process, i.e. global optimization with PSO, local optimization with ACO and vaccination process on discernibility matrix.

3D Geometric Moment Invariants for ATS Drugs Identification: A More Precise Approximation

National development is constantly threatened by drug abuse. The chemical composition of the drugs heavily determines the results of identification process, which becomes more unreliable due to the introduction of new, sophisticated, and increasingly complex ATS analogues. The identification of the unique characteristics of molecular structure in ATS drug is very crucial. Therefore, this paper is meant for formulating a more precise 3D geometric moment invariants to represent the molecular structure. The performance of the proposed technique was analyzed using drug molecular structures obtained from United Nations Office of Drugs and Crime and also from various sources. The evaluation shows the technique is qualified to be further explored and adapted to be fully compatible with ATS drug identification domain.

Exact Computation of 3D Geometric Moment Invariants for ATS Drugs Identification

The war on drug abuse involves all nations worldwide. Normally, molecular components are unique, and thus the drugs can be identified based on it. However, this procedure started to be more unreliable with the introduction of new ATS molecular structures which are increasingly complex and sophisticated. Hence, unique characteristics of molecular structure of ATS drug must be accurately identified. Therefore, this paper is meant for formulating an exact 3D geometric moment invariants to represent the drug molecular structure. The performance of the proposed technique was analyzed using drug chemical structures obtained from United Nations Office of Drugs and Crime (UNODC) and also from various sources. The evaluation shows the technique is qualified to be further explored and adapted in the future works to be fully compatible with ATS drug identification domain.

An Empirical Study of Density and Distribution Functions for Ant Swarm Optimized Rough Reducts

Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. It is used in rough reducts calculation for identifying optimally significant attributes set. Coexistence, cooperation, and individual contribution to food searching by a particle (ant) as a swarm (colony) survival behavior, depict the common characteristics of both PSO and ACO algorithms. Ant colony approach in Ant Swarm algorithm generates local solutions which satisfy the Gaussian distribution for global optimization using PSO algorithm. The density and distribution functions are two common types of Gaussian distribution representation. However, the description and comparison of both functions are very limited. Hence, this paper compares the solution vector of ACO is represented by both density and distribution function to search for a better solution and to specify a probability functions for every particle (ant), and generate components of solution vector, which satisfy Gaussian distributions. To describe relative probability of different random variables, Probability Density Function (PDF) and the Cumulative Density Function (CDF) are capable to specify its own characterization of Gaussian distributions. The comparison is based on the experimental result to increase higher fitness value and gain better reducts.

Preparation of ATS Drugs 3D Molecular Structure for 3D Moment Invariants-Based Molecular Descriptors

The campaign against drug abuse is fought by all countries, most notably on ATS drugs. The technical limitations of the current test kits to detect new brand of ATS drugs present a challenge to law enforcement authorities and forensic laboratories. Meanwhile, new molecular microscopy imaging devices which enabled the characterization of the physical 3D molecular structure have been recently introduced, and it can be used to remedy the limitations of existing drug test kits. Thus, a new type of 3D molecular structure representation, or molecular descriptors, technique should be developed to cater the 3D molecular structure acquired physically using these molecular imaging devices. One of the applications of image processing methods to represent a 3D image is 3D moment invariants. However, since there are currently no repository or database available which provide the drugs imaging results obtained using these molecular imaging devices, this paper proposes to construct the simulated 3D drugs molecular structure to be used by these 3D moment invariants-based molecular descriptors techniques. The drugs molecular structures are obtained from pihkal.info for the ATS drugs, while non-ATS drugs are obtained randomly from ChemSpider database.