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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 6 Documents
 1 
Search results for , issue "Vol 2, No 1: March 2013" : 6 Documents clear
Enhancing Knowledge Hyper Surface Method for Casting Diagnosing Nazri Mohd Nawi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (832.727 KB)

Abstract

The diagnosis of defective castings has always been a centre of attention in the manufacturing industry. This is mainly because the cause and effect relationship in a casting process is complex and non-linear. Furthermore, a large number of parameters are needed to be coordinated with each other in an optimal way to minimise the occurrence of defective castings. An intelligent diagnosis system is needed to diagnose effectively the causal representation and also justify its diagnosis. A previous method, known as the Knowledge Hyper-surface method which used Lagrange Interpolation polynomials has gained more popularity in learning cause and effect analysis in casting processes. The current method show that the belief value of the occurrence of cause with respect to the change in the belief value in the occurrence of effect can be modeled by linear, quadratic or cubic relationships and the method retained the advantages of neural networks and overcomes their limitations in learning the input-output mapping function in the presence of noisy, limited and sparse data. However, the methodology was unable to model exponential increase/decrease in belief values in cause and effect relationships. This paper proposed an enhancement to the current Knowledge Hyper-surface method by introducing midpoints in the existing shape formulation which further constrains the shape of the Knowledge hyper-surfaces to model an exponential rise in belief values but without exposing the dataset to the limitations of ‘over fitting’. The ability of the proposed method to capture the exponential change in the belief variation of the cause when the belief in the effect is at its minimum is compared to the current method on real casting data.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.584
Segmentation and Recognition of Arabic Printed Script Fakir Mohamed Fakir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (387.43 KB)

Abstract

In this work we present a method for the recognition of Arabic printed script. The major problem of the automatic reading of cursive writing is a segmentation of script to isolate characters. The recognition process consists of four phases: Preprocessing, segmentation, feature extraction and the recognition.In the preprocessing, the image is scanned and smoothed. The correction of skew lines is done by using Hough transform . In the second phase, the text is segmented into lines, words or parts of words and each word into characters based on the principle of projection of the histogram. Features such as:  density, profile, Hu moments and histogram are used to classifier the characters based on the Neural network.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1236 
Legal Documents Clustering and Summarization using Hierarchical Latent Dirichlet Allocation Ravi kumar Venkatesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (320.79 KB)

Abstract

In a common law system and in a country like India, decisions made by judges are significant sources of application and understanding of law. Online access to the Indian Legal Judgments in the digital form creates an opportunities and challenges to the both legal community and information technology researchers. This necessitates organizing, analyzing, retrieving relevant judgment and presenting it in a useful manner to the legal community for quick understanding and for taking necessary decision pertaining to a present case. In this paper we propose an approach to cluster legal judgments based on the topics obtained from hierarchical Latent Dirichlet Allocation (hLDA) using similarity measure between topics and documents and to find the summarization of each document using the same topics. The developed topic based clustering model is capable of grouping the legal judgments into different clusters and to generate summarization in effective manner compare to our previous [1] approach.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1186
Plant Leaf Disease Image Retrieval Using Color Moments Jayamala Kumar Patil; Raj Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (315.65 KB)

Abstract

This paper  presents a Content Based Image Retrieval (CBIR)method for plant leaf image retrieval, intended for identification of leaf diseases. We  used color features to extract the contents of leaf images. The color features are extracted using first three color moments.  For similarity measurement median value of moment feature vectors is used. The method is studied for three different color spaces i.e. RGB, HSV & YCbCr. The experimental result shows that HSV color space provides better results for plant leaf disease retrieval.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1319
Support Vector Machines for Object Based Building Extraction in Suburban Area using Very High Resolution Satellite Images, a Case Study: Tetuan, Morocco Omar Benarchid; Naoufal Raissouni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (397.694 KB)

Abstract

Many fields of artificial intelligence have been developed such as computational intelligence and machine learning involving neural networks, fuzzy systems, genetic algorithms, intelligent agents and Support Vector Machines (SVM). SVM is a machine learning methodology with great results in image classification. In this paper, we present the potential of SVMs to automatically extract buildings in suburban area using Very High Resolution Satellite (VHRS) images. To achieve this goal, we use object based approach: Segmentation before classification in order to create meaningful image objects using color features. In the first step, we form objects with the aid of mean shift clustering algorithm. Then, SVM classifier was used to extract buildings. The proposed method has been applied on a suburban area in Tetuan city (Morocco) and 83.76% of existing buildings have been extracted by only using color features. This result can be improved by adding other features (e.g., spectral, texture, morphology and context).DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1781
Indexing Of Three Dimensions Objects Using GIST, Zernike & PCA Descriptors Driss Naji; Fakir Mohamed Fakir; O. Bencharef; B. Bouikhalene; A. Razouk
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (316.153 KB)

Abstract

In this paper, we present a new approach to object to recognition based on the combination of Zernike moments, descriptors Gist and PCA pair wise applied to color images. The recognition of objects are based on two approaches of classification the first use neural networks (NN) for learning stage and gratitude as well to the Support Vector Machines (SVM). The experimental results showed that the recognition by SVM is better than NN. We illustrate the proposed method on color images, including objects from the database COIL-100.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.825

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