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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 4: December 2014" : 5 Documents clear
An Optimized Takagi-Sugeno Fuzzy-Based Satellite Attitude Controller by Two State Actuator Sobutyeh Rezanezhad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (732.815 KB) | DOI: 10.11591/ijai.v3.i4.pp166-176

Abstract

In this paper, an algorithm was presented to control the satellite attitude in orbit in order to reduce the fuel consumption and increase longevity of satellite. Because of proper operation and simplicity, fuzzy controller was used to save fuel and analyze the uncertainty and nonlinearities of satellite control system. The presented control algorithm has a high level of reliability facing unwanted disturbances considering the satellite limitations. The controller was designed based on Takagi-Sugeno satellite dynamic model, a powerful tool for modeling nonlinear systems. Inherent chattering related to on-off controller produces limit cycles with low frequency amplitude. This increases the system error and maximizes the satellite fuel consumption. Particle Swarm Optimization (PSO) algorithm was used to minimize the system error. The satellite simulation results show the high performance of fuzzy on-off controller with the presented algorithm.
Elastic Bunch Graph Matching Based Face Recognition Under Varying Lighting, Pose, and Expression Conditions Farooq Ahmad Bhat; M. Arif Wani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 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.i4.pp177-182

Abstract

In this paper performance of elastic bunch graph matching (EBGM) for face recognition under variation in facial expression, variation in lighting condition and variation in poses are given. In this approach faces are represented by labelled graphs. Experimental results of EBGM on ORL, Yale B and FERET datasets are provided. Strong and weak features of EBGM algorithm are discussed.
Prediction of Daily Network Traffic based on Radial Basis Function Neural Network Haviluddin Haviluddin; Imam Tahyudin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.254 KB) | DOI: 10.11591/ijai.v3.i4.pp145-149

Abstract

This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21 – 24 June 2013 (192 samples series data) in ICT Unit Universitas Mulawarman, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of percentage error (MPE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic.
Quantile Regression Neural Networks Based Prediction of Drug Activities Mohammed E. El-Telbany
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2179.281 KB) | DOI: 10.11591/ijai.v3.i4.pp150-155

Abstract

QSAR (quantitative structure-activity relationship) modeling is one of the well developed areas in drug development through computational chemistry. Similar molecules with just a slight variation in their structure can have quit different biological activity. This kind of relationship between molecular structure and change in biological activity is center of focus for QSAR Modeling. Predictions of property and/or activity of interest have the potential to save time, money and minimize the use of expensive experimental designs, such as, for example, animal testing. Intelligent machine learning techniques are important tools for QSAR analysis, as a result, they are integrated into the drug production process. The effective learnable model can reduce the cost of drug design significantly. The quantile estimation via neural network structure technique introduced in this paper is used to predict activity of pyrimidines based on the structure–activity relationship of these compounds which assist for finding potential treatment agents for serious disease. In comparison with statistical quantile regression, the qrnn significantly reduce the prediction error.
Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic Gautam S. Prakash; Shanu Sharma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (689.016 KB) | DOI: 10.11591/ijai.v3.i4.pp156-165

Abstract

Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document  authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, Ist and IInd order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.

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