Chin Kim On

Malay named entity recognition based on rule-based approach

A Named-Entity Recognition (NER) is part of the process in Text Mining and it is a very useful process for information extraction. This NER tool can be used to assist user in identifying and detecting entities such as person, location or organization. However, different languages may have different morphologies and thus require different NER processes. For instance, an English NER process cannot be applied in processing Malay articles due to the different morphology used in different languages. This paper proposes a Rule-Based Named-Entity Recognition algorithm for Malay articles. The proposed Malay NER is designed based on a Malay part-of-speech (POS) tagging features and contextual features that had been implemented to handle Malay articles. Based on the POS results, proper names will be identified or detected as the possible candidates for annotation. Besides that, there are some symbols and conjunctions that will also be considered in the process of identifying named-entity for Malay articles. Several manually constructed dictionaries will be used to handle three named-entities; Person, Location and Organizations. The experimental results show a reasonable output of 89.47% for the F-Measure value. The proposed Malay NER algorithm can be further improved by having more complete dictionaries and refined rules to be used in order to identify the correct Malay entities system.

Mel-frequency cepstral coefficient analysis in speech recognition

Speech recognition is a major topic in speech signal processing. Speech recognition is considered as one of the most popular and reliable biometric technologies used in automatic personal identification systems. Speech recognition systems are used for variety of applications such as multimedia browsing tool, access centre, security and finance. It allows people work in active environment to use computer. For a reliable and high accuracy of speech recognition, simple and efficient representation methods are required. In this paper, the zero crossing extraction and the energy level detection are applied to the recorded speech signal for voiced/unvoiced area detection. The detected voiced signals are applied for segmentation. Further, the MFCC method is applied to all of the segmented windows. The extracted MFCC data are further used as inputs for neural network training.

A review of stock market prediction with Artificial neural network (ANN)

Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). The aim of this paper is to provide a review of the applications of ANN in stock market prediction in order to determine what can be done in the future.

Infinite Mario Bross AI using Genetic Algorithm

Evolutionary Algorithm (EA) is commonly used to generate optimal Artificial Intelligence (AI) controller. It is a technique used to enhance the performance of generated controller. EA enables the system to evolve, to adapt and learn to give a better output. The implementation of EA into 2D game is not something new. Researchers used gaming platforms to test their own ideology or proposed algorithms. In this paper, a finite state machine which suitable to be used for Infinite Mario Bros game is proposed. The Genetic Algorithm (GA) is used along with the proposed finite state machine to evolve an AI agent that is capable to pass some levels of the game. The experimentation results showed that the finite state machine evolved with GA is able to create a competitive game bot that can pass through at least 3 levels of different game maps. The generated AI controller can guarantee to accomplish the tasks for some levels.

Evolving Neural Controllers Using GA for Warcraft 3-Real Time Strategy Game

This paper presents the research results found for the utilization of a Genetic Algorithm (GA) technique in evolving a set of Artificial Neural Networks (ANNs) weights which functions as controller in deciding what type of unit that should be spawned for winning against the opponent in a RTS game called War craft 3 (custom map). The elitism concept is applied during the optimization processes in order to avoid losing good solutions. The experimentation results show clearly a group of mixed randomized opponent can be defeated by the generated AI army. Hence, it is proof that GA is capable to act as a tuning technique in generating the required controllers in RTS game. Furthermore, the neural controllers generated are able to decide the best group of army used in defeating the opponent.

Multi-objective artificial evolution of RF-localization behavior and neural structures in mobile robots

This paper investigates the utilization of a multi- objective approach for evolving artificial neural networks (ANNs) that act as a controller for radio frequency (RF)- localization behavior of a virtual Khepera robot simulated in a 3D, physics-based environment. The non-elitist and elitist Pareto-frontier Differential Evolution (PDE) algorithm are used to generate the Pareto optimal sets of ANNs that optimize the conflicting objectives of maximizing the virtual Khepera robots behavior for homing towards a RF signal source and minimizing the number of hidden neurons used in its feedforward ANNs controller. A new fitness function which involved maximizing average wheels speed and detection of the RF signal source is also proposed. The experimentation results showed that the virtual Khepera robot was able to move towards to the target with using only a small number of hidden neurons. Furthermore, the testing results also showed that the success rate for the robot to achieve the signal source was higher when the elitist PDE-EMO algorithm was used. The path analysis of the Pareto controllers elucidated many different behaviors in terms of providing a successful homing behavior for the robot to attain the RF signal source.

A comparison on the performance of crossover techniques in video game

This paper describes the performance of four crossover operators used in evolving the required controllers in a video game. The crossover operators used in this research are the two-point crossover, the uniform crossover, the N-point crossover, and the single-point crossover. The performance of these crossover methods were tested using Infinite Mario Bros game. This video game was chosen due to the dynamicity and complexity of the game. This paper also presents a newly designed nondeterministic based Finite State Machine (FSM) method. The Mario character uses the proposed FSM as its strategy in the game. The proposed FSM is then optimized using a modified Genetic Algorithm (GA). The results showed that the required controllers were generated successfully using the proposed method. The results also showed that the N-point crossover performed well compared to the uniform crossover, the two-point crossover and the single-point crossover methods.

The Evolution of Gamebots for 3D First Person Shooter (FPS)

The implementation of Artificial Intelligence (AI)in 3-Dimensional (3D) First Person Shooter (FPS) game is quite general nowadays. Most of the conventional AI bots created are mostly from hard coded AI bots. Hence, it has limited the dynamicity of the AI bots and therefore it brings to a fixed strategy for gaming. The main focus of this paper is to discuss the methodologies used in generating the AI bots that is competitive in the FPS gaming. In this paper, a decision making structure is proposed. It has been combined with the Evolutionary Programming in generating the required AI controllers. Hence, there are two methodology discussions involved: (1) the proposed decision making structure and (2)the Evolutionary Programming used. The experiments show highly promising testing results after the generated AI bots have been tested and compared with the conventional ruled based AI bots. It proves that the generated AI bots using the combination of Evolutionary Programming and decision making structure performed better than those AI bots generated using conventional ruled based strategy which is hard coded and time consuming to develop.

Analysing market sentiment in financial news using lexical approach

Business and financial news bring us the latest information about the stock market. Studies have shown that business and financial news have a strong correlation with future stock performance. Therefore, extracting sentiments and opinions from business and financial news is useful as it may assist in the stock price predictions. In this paper, we present a sentiment analyser for financial news articles using lexicon-based approach. We use polarity lexicon to identify the positive or negative polarity of each term in the corpus. We conducted two sets of experiment using non-stemming tokens and stemming tokens by considering individual word found in the newspaper. The preliminary results are presented and discussed in this paper.

Automatic spell checker for Malay blog

Spell checker is a system that is used to detect and correct misspelled word. Misspelled word is a word that exists in the existing lexicon that is not correctly spelled or in shortened form. These misspelled words often result in ineffective results of the Information Retrieval (IR) application such as document retrieval. This is because IR application should be able to recognize all words in a particular language in order to be more robust. The current spell checker for the Malay language uses a dictionary that contains pair of commonly misspelled word and its correctly spelled word in detecting and correcting misspelled word. However, this type of spell checker can only correct misspelled words that exist in the existing dictionary; otherwise it requires user interaction to correct it manually. This approach works well if the spell checker is a standalone system but it is not really an effective system when the spell checker is part of another IR application such as document retrieval for weblog. This is because there will be always new misspelled words created along with the increasing number of weblog pages. Thus, the number of misspelled words will also grow extremely. In this paper, we propose a new spell checker that detects and automatically corrects misspelled words in Malay without any interaction from the user. The proposed approach automatically replaces the misspelled word if it exists in the reSpellWord dictionary. Otherwise, it will go through the process of Selangor Slang Identification or Repetitive word Identification or Opposite Word Identification whichever is suitable. If the word cannot be identified as a misspelled word, a few alternative words will be suggested and they are ranked using the Levenshtein Distance in order to choose the most likelihood word for the misspelled word. The correctly-spelled word that has the highest ranking will be chosen as a replacement for the misspelled word. This misspelled word and its correctly-spelled word are then added automatically into the dictionary in order to update the dictionary. The proposed approach is evaluated by using texts that are selected randomly from the popular Malay blog. Based on the experimental results obtained, the proposed approach is found to be effective in detecting and correcting the Malay misspelled word automatically.