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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 1, No 2: June 2012" : 6 Documents clear
Solving University Scheduling Problem with a Memetic Algorithm Mortaza Abbaszadeh; Saeed Saeedvand; Hamid Asbagi Mayani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2713.506 KB)

Abstract

Scheduling problem is one of the Non-deterministic Polynomial (NP) problems. This means that using a normal algorithm to solve NP problems is so time-consuming a process (it may take months or even years with available equipment), and thus such an algorithm is regarded as an impracticable way of dealing with NP problems. The method of Memetic Algorithm presented in this paper is different from other available algorithms. In this algorithm the problem of a university class Scheduling is solved through applying a new chromosome structure, modifying the normal genetic methods and adding a local search, which is claimed to considerably improve the solution. We included the teacher, class and course information with their maximal constraints in the proposed algorithm, and it produced an optimized scheduling table for a weekly program of the university after creating the initial population of chromosomes and running genetic operators. The results of the study show a high efficiency for the proposed algorithm compared with other algorithms considering maximum Constraints.DOI: http://dx.doi.org/10.11591/ij-ai.v1i2.512
A Projection Algorithm to Detect Cancer Using Microarray Nazario D. Ramirez-Beltran; Joan Manuel Castro; Harry Rodriguez
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (373.276 KB)

Abstract

The projection algorithm to classify tissues with a large number of genes and a small number of microarrays is proposed.The algorithm is based on the angle formed by two vectors in the n-dimensional space, and takes advantages of the geometrical projection principle.The properties of known tissues can be used to train the algorithm and distinguish between the cancer and normal gene expressions.The gene’s percentiles from an independent data set can be used to create a third vector, which is projected into the previously trained vectors to classify the third vector in one of the two populations, cancer or normal population.The proposed algorithm was implemented to detect cervical cancer in a microarray data set, which contains 8 normal and 25 cancerous tissues, which were randomly selected one thousand of times using a combinatory strategy.The algorithm was compared with three existing algorithms that have been used to solve the microarray classification problem: Fisher discriminate function, logistic regression, and artificial neural networks.Results show that the proposed algorithm outperformed the selected algorithmsDOI: http://dx.doi.org/10.11591/ij-ai.v1i2.469
Fuzzy Controller Design of Lighting Control System By Using VI Package Ragavan Saravanan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (445.605 KB)

Abstract

This paper describes how we design a lighting control system including hardware and software. Hardware includes Dimmer with relays Bulb light sensing circuit, control circuit, and 8255 expanding I/O circuit, PC, and bulb.   Sensing circuit uses photo-resistance component to sense the environmental light and then transmit the signal of the lightness to the computer through an 8-bit A/D converter 0804.  The control circuit applies reed relay in digital control way to adjust the variable resistor value of the traditional dimmer.  Software incorporates LABVIEW graph- ical programming language and MATLAB Fuzzy Logic Toolbox to design the light fuzzy controller.  The rule-base of the fuzzy logic controller either for the single input single output (SISO) system or the double inputs single output (DISO) system is developed and compared based on the op- eration of the bulb and the light sensor.  The control system can dim the bulb automatically according to the environmental light.   It can be applied to many fields such as control of streetlights and lighting control of car’s headlights and it is possible to save energy by dimming the bulb.  Experimental results show that the fuzzy controller with the DISO system can make bulb response faster than with the SISO system under sudden change of environmental light.DOI: http://dx.doi.org/10.11591/ij-ai.v1i2.441
RDVBT: Resource Distance Vector Binary Tree Algorithm for Resource Discovery in Grid SeyedElyar Hashemseresht; Ali Asghar Pourhaji Kazem
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (477.909 KB)

Abstract

Nowadays, with the increasing variety of computer systems, resource discovery in the Grid environment has been very important due to their applications; thus, offering optimal and dynamic algorithms for discovering resources in which users need a short period is an important task in grid environments.One of the methods used in resource discovery in grid is to use routing tables RDV (resource distance vector) in which the resources are based on certain criteria clustering and the clusters form a graph. In this way, some information about the resources is stored in RDV tables. Due to the environmental cycle in the graph, there are some problems; for example there are multiple paths to resources, most of which are repeated. Also, in large environments, due to the existence of many neighbors, updating the graph is time-consuming. In this paper, the structure of RDV was presented as a binary tree and these two methods (RDV graph-algorithm and RDVBT) were compared. Simulation results showed that, as a result of converting the structure to a binary tree, much better results were obtained for routing time, table updating time and number of successful requests; also the number of unsuccessful requests was reduced.DOI: http://dx.doi.org/10.11591/ij-ai.v1i2.442 
Hybrid Genetic Algorithms for Solving Winner Determination Problem in Combinatorial Double Auction in Grid Farhad Gorbanzadeh; Ali Asghar Pourhaji Kazem
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (298.655 KB)

Abstract

Nowadays, since grid has been turned to commercialization, using economic methods such as auction methods are appropriate for resource allocation because of their decentralized nature. Combinatorial double auction has emerged as a major model in the economy and is a good approach for resource allocation in which participants of grid, give their requests once to the combination of resources instead of giving them to different resources multiple times. One problem with the combinatorial double auction is the efficient allocation of resources to derive the maximum benefit. This problem is known as winner determination problem (WDP) and is an NP-hard problem. So far, many methods have been proposed to solve this problem and genetic algorithm is one of the best ones. In this paper, two types of hybrid genetic algorithms were presented to improve the efficiency of genetic algorithm for solving the winner determination problem. The results showed that the proposed algorithms had good efficiency and led to better answers. DOI: http://dx.doi.org/10.11591/ij-ai.v1i2.443
Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System Adnan Tawafan; Marizan Bin Sulaiman; Zulkifilie Bin Ibrahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (782.725 KB)

Abstract

High impedance fault (HIF) is abnormal event on electric power distribution feeder which does not draw enough fault current to be detected by conventional protective devices. The algorithm for HIF detection based on the amplitude ratio of second and odd harmonics to fundamental is presented. This paper proposes an intelligent algorithm using an adaptive neural- Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. It is integrating the learning capabilities of neural network to the fuzzy logic system robustness in the sense that fuzzy logic concepts are embedded in the network structure. It also provides a natural framework for combining both numerical information in the form of input/output pairs and linguistic information in the form of IF–THEN rules in a uniform fashion. Fast Fourier Transformation (FFT) is used to extract the features of the fault signal and other power system events. The effect of capacitor banks switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. The results show that the proposed algorithm can distinguish successfully HIFs from other events in distribution power systemDOI: http://dx.doi.org/10.11591/ij-ai.v1i2.425

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