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Knowledge Based System in Defining Human Gender

Based On Syllable Pattern Recognition

Fachrurrozi, Muhammad

Informatics Engineering, Computer

Science, Sriwijaya University

Jalan Joko Atas No 23

Palembang, Indonesia

+62­85213355478

fachrur@yahoo.com

ABSTRACT

Registration process for event or activity participant becomes important moment because the given information will become reference to give attribute and making a decision to the participant. Gender is one of important attribute to be given. Native people in an area or a place have characteristics in giving name to their children. Usually, name can represent his gender: man or women. By Knowledge based system and word pattern recognition his name, we can get relative conclusion or suggestion about his own gender.

Keywords

Gender, knowledge based, syllable pattern recognition.

1.

INTRODUCTION

By information technology advancing, almost event or activity implement the technology to get faster and more accurate result. The events or activities give a responsibility to the registrar to fill the registration form. In the registration process, the given information seldom occurs incorrectly. The incorrect information may occur from system or long time registration process. In many online registration (e.g. mail.yahoo.com) have already given another form in word pattern recognition based on given name to be used in defining unique ID suggestion.

Gender is one of human identity. This gender only have 2 (two) answer, man or woman. Thus, the incorrect information will be effect to next attribute which will be given to him. Every native people in an area or a place have name identity which are used to call or as a difference communication identity person to another person. In one area or place have characteristic how parent give their children name. For example in Palembang, person who has name “Yanti”, or “Tuti”, or “Santi” tend to a women gender. Thus, name with suffix ‘ti” tend to women gender with defined percentage.

Pattern recognition is already used by many researchers, in image or text form. In that way, they can get some conclusions to define another decision or to give some suggestions. In 2008, Xinyong do research about A Method for Evaluating the Sensitivity of Signal Features in Pattern Recognition Based on Neural Network. He used this algorithm to create a criterion function for evaluating the feature sensitivity [3]. And word pattern recognition also gives us some temporal conclusion, while he is a man or a woman by using neural network algorithm.

2.

HUMAN GENDER IDENTITY

Gender is defined by FAO as ‘the relations between men and women, both perceptual and material. Gender is not determined biologically, as a result of sexual characteristics of either women or men, but is constructed socially. It is a central organizing principle of societies, and often governs the processes of production and reproduction, consumption and distribution’ [1]. Despite this definition, gender is often misunderstood as being the promotion of women only. However, as we see from the FAO definition, gender issues focus on women and on the relationship between men and women, their roles, access to and control over resources, division of labour, interests and needs. Gender relations affect household security, family well-being, planning, production and many other aspects of life [1].

Gender roles are the ‘social definition’ of women and men. They vary among different societies and cultures, classes, ages and during different periods in history. Gender-specific roles and responsibilities are often conditioned by household structure, access to resources, specific impacts of the global economy, and other locally relevant factors such as ecological conditions [1]. Gender relations are the ways in which a culture or society defines rights, responsibilities, and the identities of men and women in relation to one another [1].

3.

CHARACTERISTIC OF A NAME

A name is an important thing for people because of many reasons. A name represents as an identity and as a subject difference beside as an object of people to communicate each other. In giving the name for their children, parent have some reasons such as cultural reason, social status, religion, their hometown, or taken from famous people. The name also could be considered to a certain gender. i.e., the person who lives in Palembang may have the name such as “Santi”, “Fitriyanti”, “Tuti”, are tend to classify to woman gender. Otherwise, the name such as “Firman”, “Lukman”, “Lukman”, are classified to man gender.

The name may consist more than one word and every word may contain more than one syllable. Certain syllable also could be considered as identity, i.e. the name with suffix “ti”, “ni”, “na” are tend to woman gender. Otherwise the name with suffix “to”, “di”, “man” are tend to man gender.

4.

KNOWLEDGE BASED SYSTEM

ability to improve their performance. Machine learning can be broadly classified into three categories: i) Supervised learning, ii) Unsupervised learning and iii) Reinforcement learning. Supervised learning requires a trainer, who supplies the input-output training instances. The learning system adapts its parameters by some algorithms to generate the desired output patterns from a given input pattern. In absence of trainers, the desired output for a given input instance is not known, and consequently the learner has to adapt its parameters autonomously. Such type of learning is termed ‘unsupervised learning’. The third type called the reinforcement learning bridges a gap between supervised and unsupervised categories. In reinforcement learning, the learner does not explicitly know the input-output instances, but it receives some form of feedback from its environment.

4.1

The Back-propagation Training

Algorithm

(a) A typical neuron used in Back propagation algorithm

(b) Schematic representation of the neuron shown in (a)

(c) Training of a weight, Wp,q,k at the output (kth) layer.

Figure 4.1: Attributes of neurons and weight adjustments by the back propagation learning algorithm[3]

The back-propagation training requires a neural net of feed-forward topology. Since it is a supervised training algorithm, both the input and the target patterns are given. For a given input pattern, the output vector is estimated through a forward pass on the network. After the forward pass is over, the error vector at the

output layer is estimated by taking the component-wise difference of the target pattern and the generated output vector. A function of errors of the output layered nodes is then propagated back through the network to each layer for adjustment of weights in that layer. The weight adaptation policy in back-propagation algorithm is derived following the principle of steepest descent approach of finding minima of a multi-valued function.

Typical neurons employed in back-propagation learning contain two modules (vide fig. 4.1(a)). The circle containing Σ wi xi denotes a weighted sum of the inputs xi for i= 1 to n. The rectangular box in fig. 4.1(a) represents the sigmoid type non-linearity. It may be added here that the sigmoid has been chosen here because of the continuity of the function over a wide range. The continuity of the nonlinear function is required in back-propagation, as we have to differentiate the function to realize the steepest descent criteria of learning. Fig. 4.1(b) is a symbolic representation of the neurons used in fig. 4.1(c).

In fig. 4.1(c), two layers of neurons have been shown. The left side layer is the penultimate (k –1)-th layer, whereas the single neuron in the next k-th layer represents one of the output layered neurons. We denote the top two neurons at the (k-1)-th and k-th layer by neuron p and q respectively. The connecting weight between them is denoted by wp,q,k. For computing Wp,q,k(n+1), from its value at iteration n, we use the formula presented in

where q lies in the layer k and neuron p in (k-1)th _{layer counted}

from the input layer;

k p,



= the error generated at neuron q, lying in layer k;

j p

Out

,



output of neuron p, position of layer j.

For generating error at neuron p, lying in layer j, we use the

For training a network by this algorithm, one has to execute the following 4 steps in order for all patterns one by one.

For each input-output pattern do begin

4.Repeat from step 1 until the error at the last layer is within a desired margin.

End For;

5.

KNOWLEDGE UPDATING

PROCESS

In this system, Machine learning is knowledge based system which is updated from external input, and the steps are:

1. The initial condition of machine has no information yet (empty data); 2. External input is person name which is used initial knowledge. Is the name classified to a man or woman gender?; 3.For each given gender suggestion, the system will accept the external input as an addition knowledge to the system.

Figure 5 Knowledge updating process

6.

SYLLABLE PATTERN

RECOGNITION PROCESS

In each specific area, every parent will give the name of the child based on the customs and cultures in the area. The name given by parents has a pattern based on a word. The processes of pattern recognition that a word will be done in this study are:

1. Words separation; 2.Word syllable separation; 3.Search words and syllables in the database; 4.Retrieval of gender information for each syllable found; 5.Giving weight value to each syllable based on the amount of data found; 6.Calculation of weighted average of each syllable; 7.Gender information advisory. 8.Gender information verification as a new external input (knowledge updating).

Figure 6 Syllable pattern recognition process

7.

CONCLUSION

The conclusions are:

Provision of gender information can be made based on the name of people. In general, the naming can be based on the geographic location. .Giving weight value of each syllable is based on the number of syllables discovery in the data previously saved. Weighted average gives advice based on gender information included names. Gender information can be used as one factor for the provision of attributes or subsequent decisions.

8.

REFERENCES

[1] Building on Gender, Agrobiodiversity and Local Knowledge”. FAO, 2004.

[2] Konar, Amit. Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain. 2000 [3] Xinyong, Qiao, Liu Wei, A Method for Evaluating the