IAES International Journal of Artificial Intelligence (IJ-AI)
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.
Articles
689 Documents
<![CDATA[Optimization of Digital Histopathology Image Quality ]]>
<![CDATA[Furat N Tawfeeq]]>;
<![CDATA[Nada A.S. Alwan]]>;
<![CDATA[Basim M. Khashman]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 2: June 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i2.pp71-77
<![CDATA[One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis. Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather than using difficult numerical equation. Mainly there are five parts, starting with accepting the image, passing through removing the bubbles, and ending with predict the tissues. These were implemented by defining membership functions between colours range using MATLAB. Results: 50 histopathological images were tested on four types of membership functions (MF); the results show that (nine-triangular) MF get 75.4% correctly predicted pixels versus 69.1, 72.31 and 72% for (five- triangular), (five-Gaussian) and (nine-Gaussian) respectively. Conclusions: In line with the era of digitally driven e-pathology, this process is essentially recommended to ensure quality interpretation and analyses of the processed slides; thus overcoming relevant limitations.]]>
<![CDATA[Performance Analysis of ANN Model for Estimation of Trophic Status Index of Lakes ]]>
<![CDATA[Tushar Anthwal]]>;
<![CDATA[Akanksha Chandola]]>;
<![CDATA[M P Thapliyal]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i1.pp1-10
<![CDATA[The health of water bodies across the globe is of high concern as the pollution is accelerating rigorously. With the interventions of simple technology, some significant changes could be bought up. Lakes are dying because of high Trophic Index Status which shows the eutrophication level of water bodies. Taking this into account, feed forward back propagation neural network model is used to estimate the Trophic Status Index (TSI) of lakes which could compute the value of TSI with the given parameters; pH, temperature, dissolved oxygen, Secchi disk transparency, chlorophyll and total phosphate. Two learning algorithms; Levenberg Marquardt (LM) and Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi Newton were used to train the network, which belongs to different classes. The results were analyzed using mean square error function and further checked for the deviation from actual data. Among both the training algorithm; LM demonstrated better performance with 0.0007 average mean square error for best validation performance and BFGS Quasi Newton shows the average mean square error of 1.07.]]>
<![CDATA[Direct Torque Control of Doubly Star Induction Motor Using Fuzzy Logic Speed Controller ]]>
<![CDATA[Lallouani Hellali]]>;
<![CDATA[Saad Belhamdi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i1.pp42-53
<![CDATA[This paper presents the simulation of the control of doubly star induction motor using Direct Torque Control (DTC) based on Proportional and Integral controller (PI) and Fuzzy Logic Controller (FLC). In addition, the work describes a model of doubly star induction motor in α-β reference frame theory and its computer simulation in MATLAB/SIMULINK®. The structure of the DTC has several advantages such as the short sampling time required by the TC schemes makes them suited to a very fast flux and torque controlled drives as well as the simplicity of the control algorithm.the general- purpose induction drives in very wide range using DTC because it is the excellent solution. The performances of the DTC with a PI controller and FLC are tested under differents speeds command values and load torque.]]>
<![CDATA[Sizing and Implementation of Photovoltaic Water Pumping System for Irrigation ]]>
<![CDATA[Santosh S. Raghuwanshi]]>;
<![CDATA[Vikas Khare]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i1.pp54-62
<![CDATA[Solar photovoltaic systems convert energy of light directly into electrical energy. This work presents, a process to compute the required size of the stand-alone solar photovoltaic generator based water pumping system for an existing area. In addition solar photovoltaic generator is connecting voltage source inverter fed vector controlled induction motor-pump system. Perturb and observe are used for harvesting maximum power of PV generator in between buck-boost DC converter and inverter system. In this paper system result is validated by fuzzy logic system and compare with variable frequency drives based PI controllers, driving motor-pump system. The operational performance at 60 m head, VFD based controllers in terms overshoot and setting time and also analysis performance of motor-pump set under different weather conditions. By assessment of system we find that speed and torque variation, overshoot and settling time is more with PI controller, Fuzzy logic controller (FLC) performance have dominance to VFD based PI controller.]]>
<![CDATA[Shrinkage of real power loss by enriched brain storm optimization algorithm ]]>
<![CDATA[K. Lenin]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v8.i1.pp1-6
<![CDATA[This paper proposes Enriched Brain Storm Optimization (EBSO) algorithm is used for soving reactive power problem. Human being are the most intellectual creature in this world. Unsurprisingly, optimization algorithm stimulated by human being inspired problem solving procedure should be advanced than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, we commence a new Enriched brain storm optimization algorithm, which was enthused by the human brainstorming course of action. In the projected Enriched Brain Storm Optimization (EBSO) algorithm, the vibrant clustering strategy is used to perk up the kmeans clustering process. The most important view of the vibrant clustering strategy is that; regularly execute the k-means clustering after a definite number of generations, so that the swapping of information wrap all ideas in the clusters to accomplish suitable searching capability. This new approach leads to wonderful results with little computational efforts. In order to evaluate the efficiency of the proposed Enriched Brain Storm Optimization (EBSO) algorithm, has been tested standard IEEE 118 & practical 191 bus test systems and compared to other standard reported algorithms. Simulation results show that Enriched Brain Storm Optimization (EBSO) algorithm is superior to other algorithms in reducing the real power loss.]]>
<![CDATA[Age Constraints Effectiveness on the Human Community Based Genetic Algorithm (HCBGA) ]]>
<![CDATA[Nagham A. Al-Madi]]>;
<![CDATA[Amnah A. El-Obaid]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 2: June 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i2.pp78-82
<![CDATA[In this paper, we use under-age constraints and apply it to the Traveling Salesman Problem (TSP). Values and results concerning the averages and best fits of both, the Simple Standard Genetic Algorithm (SGA), and an improved approach of Genetic Algorithms named Human Community Based Genetic Algorithm (HCBGA) are being compared. Results from the TSP test on Human Community Based Genetic Algorithm (HCBGA) are presented. Best fit solutions towards slowing the convergence of solutions in different populations of different generations show better results in the Human Community Based Genetic Algorithm (HCBGA) than the Simple Standard Genetic Algorithm (SGA).]]>
<![CDATA[Improvement of Power Quality Using Fuzzy Controlled D-STATCOM in Distribution System ]]>
<![CDATA[B. Santhosh Kumar]]>;
<![CDATA[K.B. Madhu Sahu]]>;
<![CDATA[K.B. Saikiran]]>;
<![CDATA[CH. Krishna Rao]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 2: June 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i2.pp83-89
<![CDATA[This paper investigates the problems associated with distribution system in terms of delivery of clean power and their solutions. Power quality has become a major issue in the present power system network. The network has mostly inductive nature. This draws more reactive power. This causes harmonics and voltage unbalance problems. So maintain the proper operation of interconnected power system, we are using one of the facts devices such as fuzzy controlled D-statcom. It provides suitable compensation and there by maintain proper power factor and also reduces harmonic contents. The simulation is taken out by MATLAB/SIMULINK and the result shows the effectiveness of GA (Genetic algorithm) simulation. Optimized Fuzzy controlled D-STATCOM for improvement of power quality.]]>
<![CDATA[Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data ]]>
<![CDATA[Manaswini Pradhan]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 2: June 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i2.pp95-104
<![CDATA[In DNA microarray technology, gene classification is considered to be difficult because the attributes of the data, are characterized by high dimensionality and small sample size. Classification of tissue samples in such high dimensional problems is a complicated task. Furthermore, there is a high redundancy in microarray data and several genes comprise inappropriate information for accurate classification of diseases or phenotypes. Consequently, an efficient classification technique is necessary to retrieve the gene information from the microarray experimental data. In this paper, a classification technique is proposed that classifies the microarray gene expression data well. In the proposed technique, the dimensionality of the gene expression dataset is reduced by Probabilistic PCA. Then, an Artificial Neural Network (ANN) is selected as the supervised classifier and it is enhanced using Evolutionary programming (EP) technique. The enhancement of the classifier is accomplished by optimizing the dimension of the ANN. The enhanced classifier is trained using the Back Propagation (BP) algorithm and so the BP error gets minimized. The well-trained ANN has the capacity of classifying the gene expression data to the associated classes. The proposed technique is evaluated by classification performance over the cancer classes, Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The classification performance of the enhanced ANN classifier is compared over the existing ANN classifier and SVM classifier.]]>
<![CDATA[Artificial Neural Network for Healthy Chicken Meat Identification ]]>
<![CDATA[Fajar Yumono]]>;
<![CDATA[Imam Much Ibnu Subroto]]>;
<![CDATA[Sri Arttini Dwi Prasetyowati]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i1.pp63-70
<![CDATA[Indonesia is the country with the largest number of Muslims in the world. Every Muslim is taught to consume thoyyiban halal meat or healthy chicken because it is slaughtered in the right way and stored in a good way too. But the reality in the market of many chicken meat on the market can not meet that criteria. Identification of healthy chicken meat can be done with laboratory experiments, but that is not simple and takes time. This experiment offers a cheaper, faster approach, with very high accuracy. The experimental approach is based on color and texture analysis on 5 types of meat quality based on healthy value. Color analysis was performed using artificail neural network (ANN) while texture analysis used Canny edge detection. Experimental results show that the color histogram approach with ANN is better than the texture approach, ie 94% versus 66%. It can be concluded that the freshness of a chicken does not have much effect on the texture of the meat but it has an effect on the color change in the meat.]]>
<![CDATA[Dynamic Particle Swarm Optimization for Multimodal Function ]]>
<![CDATA[H. Omranpour]]>;
<![CDATA[M. Ebadzadeh]]>;
<![CDATA[S. Shiry]]>;
<![CDATA[S. Barzegar]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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<![CDATA[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.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.367]]>
