Dewi Nasien

Statistical Learning Theory and Support Vector Machines

It has been more than 30 years that statistical learning theory (SLT) has been introduced in the field of machine learning. Its objective is to provide a framework for studying the problem of inference that is of gaining knowledge, making predictions, making decisions or constructing models from a set of data. Support Vector Machine, a method based on SLT, then emerged and becoming a widely accepted method for solving real-world problems. This paper overviews the pattern recognition techniques and describes the state of art in SVM in the field of pattern recognition.

Support Vector Machine (SVM) for English Handwritten Character Recognition

This paper proposes a recognition model for English handwritten (lowercase, uppercase and letter) character recognition that uses Freeman chain code (FCC) as the representation technique of an image character. Chain code representation gives the boundary of a character image in which the codes represent the direction of where is the location of the next pixel. An FCC method that uses 8-neighbourhood that starts from direction labelled as 1 to 8 is used. Randomized algorithm is used to generate the FCC. After that, features vector is built. The criteria of features to input the classification is the chain code that converted to 64 features. Support vector machine (SVM) is chosen for the classification step. NIST Databases are used as the data in the experiment. Our test results show that by applying the proposed model, we reached a relatively high accuracy for the problem of English handwritten recognition.

Development of Robot Simulation Software for Five Joints Mitsubishi RV-2AJ Robot Using MATLAB/Simulink and V-Realm Builder

The Robot Simulation Software (RSS) nowadays is paramount important to increase the accuracy and efficiency of industrial robot. This paper reports the development of the RSS where a Mitsubishi RV-2AJ robot has been taken as a case study. The project adopts the virtual reality interface design methodology and utilizes MATLAB/Simulink and V-Realm Builder as the tools. A robot model has been developed and a RSS software life cycle has been implemented.

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

The development of handwriting character recognition (HCR) is an interesting area in pattern recognition. HCR system consists of a number of stages which are preprocessing, feature extraction, classification and followed by the actual recognition. It is generally agreed that one of the main factors influencing performance in HCR is the selection of an appropriate set of features for representing input samples. This paper provides a review of these advances. In a HCR, the set of features plays as main issues, as procedure in choosing the relevant feature that yields minimum classification error. To overcome these issues and maximize classification performance, many techniques have been proposed for reducing the dimensionality of the feature space in which data have to be processed. These techniques, generally denoted as feature reduction, may be divided in two main categories, called feature extraction and feature selection. A large number of research papers and reports have already been published on this topic. In this paper we provide an overview of some of the methods and approach of feature extraction and selection. Throughout this paper, we apply the investigation and analyzation of feature extraction and selection approaches in order to obtain the current trend. Throughout this paper also, the review of metaheuristic harmony search algorithm (HSA) has provide.

Determination of Gender from Pelvic Bones and Patella in Forensic Anthropology: A Comparison of Classification Techniques

The determination of gender is an important part of forensic anthropology because as the first essential step for positive identification process. Besides empirical methods for gender determination such as Discriminant Function Analysis (DFA), Artificial Intelligence methods such as Artificial Neural Network (ANN) should be considered to obtain more accurate determination result. This paper proposes Back propagation Neural Network (BPNN) model of ANN methods. By using data and DFA result of pelvic bones and patella from previous work, this paper compares accuracy of result obtained from the BPNN models. A total sample data of 136 pelvic bones and 133 patellae have been collected. For pelvic bones, BPNN gave average accuracy as much as 98.5% for training and 98.3 for testing. While on left pelvic bones, average accuracy that is obtained are 98.49% for training and 86.6% for testing. For patella bones, all average accuracy (males and females) are obtained by BPNN is 94.09%. If compared with previous study that using DFA obtained accuracy as much as 92.9%. It is concluded that in gender determination, BPNN gives high accuracy of classification for both bones compared with DFA.

Back-Propagation Neural Network for Gender Determination in Forensic Anthropology

Determination of gender is the foremost and important step of forensic anthropology in determining a positive identification from unidentified skeletal remains. Gender determination is the classification of an individual into one of two groups, male or female. The classification technique most used by anthropologists or researchers is traditional gender determination with applied linear approach, such as Discriminant Function Analysis (DFA). This paper proposed non-linear approach specific Back-Propagation Neural Network (BPNN) to determine gender from sacrum bone. Sacrum bone is one part of the body that is usually regarded as the most reliable indicator of sex. The data used in the experiment were taken from previous research, a total of 91 sacrum bones consisting of 34 females and 57 males. Method of measurement used is metric method which is measured based on six variables; real height, anterior length, anterior superior breadth, mid-ventral breadth, anterior posterior diameter of the base, and max-transverse diameter of the base. The objective of this paper is to examine and compare the degree of accuracy between previous research (DFA) and BPNN. There are two architectures of BPNN built for this case, namely [6; 6; 2] and [6; 12; 2]. The best average accuracy obtained by BPNN is model [6; 12; 2] with accuracy 99.030 % for training and 97.379 % for testing on experiment lr = 0.5 and mc = 0.9, then obtained Mean Squared Error (MSE) training is 0.01 and MSE testing is 1.660. Previous research using DFA only obtained accuracy as high as 87 %. Hence, it can be concluded that BPNN provide classification accuracy higher than DFA for gender determination in forensic anthropology.

Backpropagation Neural Network for Sex Determination from Patella in Forensic Anthropology

Forensic anthropology is a discipline that concerned on postmortem identification from skeletal remains in sex determination. In sex determination, besides empirical techniques such as Discriminant Function Analysis (DFA), Artificial Intelligence techniques such as Artificial Neural Network (ANN) should be considered to get more accurate result. This paper proposes back propagation ANN model for sex determination. By using data and DFA result from previous work, this paper compares the result with the result of ANN model obtained from the experiment. A total sample data of 113 patellae has been generated based on statistics values of previous study. The data is divided into three groups of ages (young, middle, and old) and is measured using three parameters (width, height, and thickness). The ANN model produces average accuracy until 96.1% compared to 92.9% result from DFA technique. This concludes that ANN produces more accurate result in sex determination compared to DFA.