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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 2, No 3: September 2013" : 5 Documents clear
Unknown Word Detection via Syntax Analyzer Soe Lai Phyue
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (72.889 KB)

Abstract

A knowledge resource is the central repository of data for all Natural Language Processing (NLP) applications and development of NLP applications mostly depend on coverage of knowledge resources. The multipurpose Myanmar Language Lexico-conceptual Knowledge Resource (ML2KR) and Myanmar function tagged corpus were developed as initial resources by using semiautomatic approach. ML2KR consists of Myanmar WordNet, Myanmar English bilingual computational lexicon and morphological processor. Myanmar language is morphologically rich and agglutinative language. Therefore, it is usually required to segment Myanmar texts prior to further processing. Segmentation has two main problems, word ambiguity that more than one meaning and unknown word occurrence that a word does not have in the lexicon. In this paper, we address on the unknown word occurrence issue. To detect the new unrestricted character patterns of words, character based parsing syntax analyzer is built by using Context Free Grammar (CFG). Firstly, unknown words are considered as a Name by Name Entity Recognition with forward and backward rule based approach. If the name does not agree with syntax analyzer, all possible unknown words are verified to update the lexicon and Myanmar WordNet.DOI: http://dx.doi.org/10.11591/ij-ai.v2i3.1802
Design of Multi-Criteria Spatial Decision Support System (MC-SDSS) for Animal Production Hesham Ahmed Hassan; Hazem Mokhtar El-Bakry; Hamada Gaber Abd Allah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (719.872 KB)

Abstract

This paper presents a Multi-criteria spatial decision support system (MC-SDSS) as a tool for decision making and planning. MC-SDSS can be used to assess different criteria with different weights. We believe that such tool can be utilized to help policy/decision makers to improve animal production in Egypt. MC-SDSS facilitates the integration of the exploration and evaluation phases of the decision-making process in a transparent and interactive system that allows policy/decision makers to carry out the analyses without advanced geographical information system (GIS) or multiple criteria decision analysis (MCDA) training. We use weighted overlay method to support data spatial analysis, and then visualize and analyze different factors such as "Diseases", "Climate", "Veterinary care" and "Economical factors" which affect the animal production in Egypt. Policy/Decision makers can change their weights and parameters with this tool for their different study areas. Moreover they can use final suitability maps from this tool.DOI: http://dx.doi.org/10.11591/ij-ai.v2i3.1948
Designing Observer Based Variable Structure Controller for Large Scale Nonlinear Systems Reza Ghasemi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (222.463 KB)

Abstract

Designing observer based decentralized fuzzy adaptive controller is discussed for a class of large scale non-canonical nonlinear systems with unknown functions of the subsystems in this paper. The On-line adaptation of the controller and the observer parameters, boundedness of the output and the observer errors, robustness against external disturbance are the advantages of the proposed method. The simulation results show the promising performance of the proposed method.DOI: http://dx.doi.org/10.11591/ij-ai.v2i3.2025
Text Wrapping Approach to natural Language Information retrieval using significant Indicator Toyin Enikuomehin; J S Sadiku
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (226.656 KB)

Abstract

This paper continues the advancement of models proposed for Information Retrieval by understanding that, the Information Retrieval task continues to draw attention as the information repositories increase. Knowing that Natural Language presentation of user’s information need help to reduce the complexity of the search process, we propose the use of a well defined Significant Indicator, which uses the relevance index of terms derived from the position of the text, to perform retrieval. This is achieved by initiating a text wrapping process such that document representation in space could algebraically be measured and assigned appropriate function as similarity ratio for Query and Document. Benchmark tools for Information Retrieval were followed and experiment performed using TREC classified data implemented with TRECEVAL shows better performance against some baseline models. The paper suggests further research in the direction of the Significant Indicator as a method for large search space reductionDOI: http://dx.doi.org/10.11591/ij-ai.v2i3.2202
Black Holes Algorithm: A Swarm Algorithm inspired of Black Holes for Optimization Problems Mostafa Nemati; Reza Salimi; Navid Bazrkar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (350.577 KB)

Abstract

In this paper a swarms algorithms, for optimization problem is proposed. This algorithm is inspired of black holes. A black hole is a region of space-time whose gravitational field is so strong that nothing which enters it, not even light, can escape. Every black hole has mass, and charge.  In this Algorithm we suppose each solution of problem as a black hole and use of gravity force for global search and electrical force for local search. The proposed method is verified using several benchmark problems commonly used in the area of optimization. The experimental results on different benchmarks indicate that the performance of the proposed algorithm is better than    PSO (Particle Swarms Optimization), AFS (Artifitial Fish Swarm Algorithm) and RBH-PSO (random black hole particle swarm optimization Algorithm).DOI: http://dx.doi.org/10.11591/ij-ai.v2i3.3226

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