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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 3, No 3: September 2014" : 5 Documents clear
Fingerprint Classification Using Fuzzy-neural Network and Other Methods Idriss Tazight; Mohamed Fakir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.66 KB) | DOI: 10.11591/ijai.v3.i3.pp129-135

Abstract

The fingerprints are unique to each individual; they can be used as a means to distinguish one individual from another.Therefore they are used to identify a person. Fingerprint Classification is done to associate a given fingerprint to one of the existing classes, such as left loop, right loop, arch, tented arch and whorl. Classifying fingerprint images is a very complex pattern recognition problem, due to properties of intra-class diversitiesand inter-class similarities. Its objective is to reduce the responsetime and reducing the search space in an automatic identificationsystem fingerprint (AIS), in classifying fingerprints. In these papers we present a system of fingerprint classificationbased on singular characteristics for extracting feature vectorsand neural networks and fuzzy neural networks, SVM and Knearest neighbour for classifying.
Enhanced Camera Calibration for Machine Vision using OpenCV Shubham Rohan Asthana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (503.322 KB) | DOI: 10.11591/ijai.v3.i3.pp136-144

Abstract

In several machine vision applications, a fundamental step is to precisely determine the relation between the image of the object and its physical dimension by performing a calibration process. The aim is to devise an enhanced mechanism for camera calibration in order to improve the already existing methods in OpenCV. A good calibration is important when we need to reconstruct a world model or interact with the world as in case of robot, hand-eye coordination. In order to meet the rising demands for higher accuracy various calibration techniques have been developed but they are unable in obtaining precise results. In this paper we propose an enhanced camera calibration procedure using a special grid pattern of concentric circles with special markers. The overall objective is to minimize the re-projection for good camera calibration.
Hybrid Model of Automated Anaphora Resolution Kalyani Pradiprao Kamune; Avinash Agrawal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.026 KB) | DOI: 10.11591/ijai.v3.i3.pp105-111

Abstract

Anaphora resolution has proven to be a very difficult problem of natural language processing, and it is useful in discourse analysis, language understanding and processing, information exaction, machine translation and many more. This paper represents a system that instead of using a monolithic architecture for resolving anaphora, use the hybrid model which combines the constraint-based and preferences-based architectures, each uses a different source of knowledge, and proves effective on theoretical and computational basis. An algorithm identifies both inter-sentential and intra-sentential antecedents of “Third person pronoun anaphors”, “Pleonastic it”, and “Lexical noun phrase anaphora”. The algorithm use Charniak parser (parser05Aug16) as an associated tool, and it relays on the output generated by it. Salience measures derived from parse tree, in order to find out accurate antecedents from the list of potential antecedents. We have tested the system extensively on 'Reuters Newspaper corpus'.
Effect of Feature Selection on Small and Large Document Summarization Dipti Yashodhan Sakhare; Rajkumar Rajkumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (667.356 KB) | DOI: 10.11591/ijai.v3.i3.pp112-120

Abstract

As the amount of textual Information increases, we experience a need for Automatic Text Summarizers. In Automatic summarization a text document or a larger corpus of multiple documents are reduced to a short set of words or paragraph that conveys the main meaning of the text Summarization can be classified into two approaches: extraction and abstraction. This paper focuses on extraction approach.The goal of text summarization based on extraction approach is sentences selection. The first step in summarization by extraction is the identification of important features. In our approach short stories and biographies are used as test documents. Each document is prepared by pre-processing process: sentence segmentation, tokenization, stop word removal, case folding, lemmatization, and stemming. Then, using important features, sentence filtering, data compression and finally calculating score for each sentence is done. In this paper we proposed various features of Summary Extraction and also analyzed features that are to be applied depending upon the size of the Document. The experimentation is performed with the DUC 2002 dataset. The comparative results of the proposed approach and that of MS-Word are also presented here. The concept based features are given more weightage. From these results we propose that use of the concept based features helps in improving the quality of the summary in case of large documents.
Optical Character Recognition of Off-Line Typed and Handwritten English Text Using Morphological and Template Matching Techniques Olakanmi Olufemi Oladayo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (708.548 KB) | DOI: 10.11591/ijai.v3.i3.pp121-128

Abstract

The existence of several documents in historical archives which need to be edited and stored in a computer has been one of the drives of Optical Character Reader (OCR) research. Earlier scanner has been used to achieve this tedious task however scanner only produces picture images of the documents.This makes the documents unreadable and un-editable through other word processing applications.This paper proposed an OCR system which converts off line typed and handwritten texts into their editable textual representations.The morphological correlation technique improves the mapping and recognition efficiency of the OCR system.

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