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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN : 20894872     EISSN : 22528938     DOI : -
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.
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Articles 224 Documents
Predictive modelling and optimization of nitrogen oxides emission in coal power plant using Artificial Neural Network and Simulated Annealing P, Ilamathi; Selladurai, V.; Balamurugan, K.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this research paper, predictive modelling of NOx emission of a 210 MW capacity pulverized coal-fired boiler and combustion parameter optimization to reduce NOx emission in flue gas is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature and nozzle tilt are studied.  The data collected from parametric field experiments are used to build a feed-forward back-propagation artificial neural net (ANN). The coal combustion parameters are used as inputs and NOx emission as outputs of the model. The ANN model is developed for full load condition and its predicted values are verified with the actual values. The algebraic equation containing weights and biases of the trained net is used as fitness function in simulated annealing (SA) to find the optimum level of input operating conditions for low NOx emission. The result proves that the proposed approach could be used for generating feasible operating conditions.
Estimating Processed Cheese Shelf Life with Artificial Neural Networks Goyal, Sumit; Kumar Goyal, Gyanendra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

Cascade multilayer artificial neural network (ANN) models were developed for estimating the shelf life of processed cheese stored at 7-8oC.Mean square error , root mean square error,coefficient of determination and nash - sutcliffo coefficient were applied in order to compare the prediction ability of the developed models.The developed model with a combination of 5à16à16à1 showed excellent agreement between the actual and the predicted data , thus confirming that multilayer cascade models are good in estimating the shelf life of processed cheese.
Prediction of Future Stock Close Price using Proposed Hybrid ANN Model of Functional Link Fuzzy Logic Neural Model (FLFNM) Kumar. J, Kumaran; A, Kailas
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper, the prediction of future stock close price of SENSEX & NSE stock exchange is found using the proposed Hybrid ANN model of Functional Link Fuzzy Logic Neural Model. The historic raw data’s of SENSEX & NSE stock exchange has been pre-processed to the range of (0 to 1). After pre-processing the inputs and forwarded to functional expansion function to perform neural operation. The activation function of neuron has fuzzy sets in order to show the future close price range of SENSEX & NSE stock exchange. The model is trained with the pre-processed historic data’s of stock exchange and the prediction rate (Performance & Error rate) of the Proposed Hybrid ANN model of Functional Link Fuzzy Logic Neural Model is calculated at the testing phase using the performance metrics (MAPE & RMSE).
Dynamic Particle Swarm Optimization for Multimodal Function Omranpour, H.; Ebadzadeh, M.; Shiry, S.; Barzegar, S.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper, a technical approach to particle swarm optimization method is presented. The main idea of the paper is based on local extremum escape. A new definition has been called the worst position. With this definition, convergence and trapping in extremumlocal be prevented and more space will be searched. In many cases of optimization problems, we do not know the range that answer is that.In the results of examine on the benchmark functions have been observed that when initialization is not in the range of the answer, the other known methods are trapped in local extremum. The method presented is capable of running through it and the results have been achieved with higher accuracy.
Design and Implementation of Fuzzy Position Control System for Tracking Applications and Performance Comparison with Conventional PID Jamali Soufi Amlashi, Nader
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

This paper was written to demonstrate importance of a fuzzy logic controller in act over conventional methods with the help of an experimental model. Also, an efficient simulation model for fuzzy logic controlled DC motor drives using Matlab/Simulink is presented. The design and real-time implementation on a microcontroller presented. The scope of this paper is to apply direct digital control technique in position control system. Two types of controller namely PID and fuzzy logic controller will be used to control the output response. The performance of the designed fuzzy and classic PID position controllers for DC motor is compared and investigated. Digital signal Microcontroller ATMega16 is also tested to control the position of DC motor. Finally, the result shows that the fuzzy logic approach has minimum overshoot, and minimum transient and steady state parameters, which shows the more effectiveness and efficiency of FLC than conventional PID model to control the position of the motor. Conventional controllers have poorer performances due to the non-linear features of DC motors like saturation and friction.
Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System Tawafan, Adnan; Sulaiman, Marizan Bin; Ibrahim, Zulkifilie Bin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

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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 system
Fuzzy Controller Design of Lighting Control System By Using VI Package Saravanan, Ragavan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

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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.
RDVBT: Resource Distance Vector Binary Tree Algorithm for Resource Discovery in Grid Hashemseresht, SeyedElyar; Pourhaji Kazem, Ali Asghar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

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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. 
Hybrid Genetic Algorithms for Solving Winner Determination Problem in Combinatorial Double Auction in Grid Gorbanzadeh, Farhad; Pourhaji Kazem, Ali Asghar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

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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.
A Projection Algorithm to Detect Cancer Using Microarray D. Ramirez-Beltran, Nazario; Castro, Joan Manuel; Rodriguez, Harry
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

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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 algorithms

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