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.
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<![CDATA[Speech recognition of moroccan dialect using hidden markov models ]]>
<![CDATA[Bezoui Mouaz]]>;
<![CDATA[Beni-hssane Abderrahim]]>;
<![CDATA[Elmoutaouakkil Abdelmajid]]>
<![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.pp7-13
<![CDATA[This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.]]>
<![CDATA[Distance weighted K-Means algorithm for center selection in training radial basis function networks ]]>
<![CDATA[Lim Eng Aik]]>;
<![CDATA[Tan Wei Hong]]>;
<![CDATA[Ahmad Kadri Junoh]]>
<![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.pp54-62
<![CDATA[The accuracies rates of the neural networks mainly depend on the selection of the correct data centers. The K-means algorithm is a widely used clustering algorithm in various disciplines for centers selection. However, the method is known for its sensitivity to initial centers selection. It suffers not only from a high dependency on the algorithm's initial centers selection but, also from data points. The performance of K-means has been enhanced from different perspectives, including centroid initialization problem over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the centers produces by the algorithm. To solve this problem, a new method to find the initial centers and improve the sensitivity to the initial centers of K-means algorithm is proposed. This paper presented a training algorithm for the radial basis function network (RBFN) using improved K-means (KM) algorithm, which is the modified version of KM algorithm based on distance-weighted adjustment for each centers, known as distance-weighted K-means (DWKM) algorithm. The proposed training algorithm, which uses DWKM algorithm select centers for training RBFN obtained better accuracy in predictions and reduced network architecture compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment; hence, the new network was undergoing a hybrid learning process. The network called DWKM-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to proposed method for root mean square error (RMSE) in radial basis function network (RBFN). The proposed method yielded a promising result with an average improvement percentage more than 50 percent in RMSE.]]>
<![CDATA[An improved radial basis function networks in networks weights adjustment for training real-world nonlinear datasets ]]>
<![CDATA[Lim Eng Aik]]>;
<![CDATA[Tan Wei Hong]]>;
<![CDATA[Ahmad Kadri Junoh]]>
<![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.pp63-76
<![CDATA[In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and the networks weight. The gradient descent algorithm is a widely used weight adjustment algorithm in most of neural networks training algorithm. However, the method is known for its weakness for easily trap in local minima. It suffers from a random weight generated for the networks during initial stage of training at input layer to hidden layer networks. The performance of radial basis function networks (RBFN) has been improved from different perspectives, including centroid initialization problem to weight correction stage over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the weight produces by the algorithm. To solve this problem, an improved gradient descent algorithm for finding initial weight and improve the overall networks weight is proposed. This improved version algorithm is incorporated into RBFN training algorithm for updating weight. Hence, this paper presented an improved RBFN in term of algorithm for improving the weight adjustment in RBFN during training process. The proposed training algorithm, which uses improved gradient descent algorithm for weight adjustment for training RBFN, obtained significant improvement in predictions compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment. The proposed improved network called IRBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to IRBFN for root mean square error (RMSE) values with standard RBFN. The IRBFN yielded a promising result with an average improvement percentage more than 40 percent in RMSE.]]>
<![CDATA[An improved hybrid feature selection method for huge dimensional datasets ]]>
<![CDATA[F. Rosita Kamala]]>;
<![CDATA[P. Ranjit Jeba Thangaiah]]>
<![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.pp77-86
<![CDATA[High dimensions of data cause overfitting in machine learning models, can lead to reduction in accuracy during classification of instances. Variable selection is the most essential function in predictive analytics, that reduces the dimensionality, without losing an appropriate information by selecting a few significant features of machine learning problems. The major techniques involved in this process are filter and wrapper methodologies. While filters measure the weight of features based on the attribute weighting criterion, the wrapper approach computes the competence of the variable selection algorithms. The wrapper approach is achieved by the selection of feature subgroups by pruning the feature space in its search space. The objective of this paper is to choose the most favourable attribute subset from the novel set of features, by using the combination method that unites the merits of filters and wrappers. To achieve this objective, an Improved Hybrid Feature Selection (IHFS) method is performed to create well-organized learners. The results of this study shows that the IHFS algorithm can build competent business applications, which have got a better precision than that of the constructed which is stated by the previous hybrid variable selection algorithms. Experimentation with UCI (University of California, Irvine) repository datasets affirms that this method have got better prediction performance, more robust to input noise and outliers, balances well with the available features, when performed comparison with the present algorithms in the literature review.]]>
<![CDATA[Multilayer neural network synchronized secured session key based encryption in wireless communication ]]>
<![CDATA[Arindam Sarkar]]>
<![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.pp44-53
<![CDATA[In this paper, multilayer neural network synchronized session key based encryption has been proposed for wireless communication of data/information. Multilayer perceptron transmitting systems at both ends accept an identical input vector, generate an output bit and the network are trained based on the output bit which is used to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights or hidden units of this selected hidden layer help to form a secret session key. The plain text is encrypted through chaining, cascaded xoring of multilayer perceptron generated session key. If size of the final block of plain text is less than the size of the key then this block is kept unaltered. Receiver will use identical multilayer perceptron generated session key for performing deciphering process for getting the plain text. Parametric tests have been done and results are compared in terms of Chi-Square test, response time in transmission with some existing classical techniques, which shows comparable results for the proposed technique.]]>
<![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[Optimization study of fuzzy parametric uncertain system ]]>
<![CDATA[Tejal D. Apale]]>;
<![CDATA[Ajay B. Patil]]>
<![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.pp14-25
<![CDATA[This paper deals with the analysis and design of the optimal robust controller for the fuzzy parametric uncertain system. An LTI system in which coefficients depends on parameters described by a fuzzy function is called as fuzzy parametric uncertain system. By optimal control design, we get control law and feedback gain matrix which can stabilize the system. The robust controller design is a difficult task so we go for the optimal control approach. The system can be converted into state space controllable canonical form with the α-cut property fuzzy. For optimal control design, we find control law and get the feedback gain matrix which can stabilize the system and optimizes the cost function. Stability analysis is done by using the Kharitonov theorem and Lyapunov-Popov method. The proposed method applied to a response of Continuous Stirred Tank Reactor (CSTR).]]>
<![CDATA[Academic performance prediction algorithm based on fuzzy data mining ]]>
<![CDATA[Anil Kumar Tiwari]]>;
<![CDATA[G. Ramakrishna]]>;
<![CDATA[Lokesh Kumar Sharma]]>;
<![CDATA[Sunil Kumar Kashyap]]>
<![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.pp26-32
<![CDATA[This paper presents an algorithm for prediction of academic performance of students by fuzzy data mining. The fuzzy-trace concept applied to predict the academic performance of the students. An algorithm is proposed in this paper lies with this idea. The fuzzy academic set is generated from the student’s academic data. This is analyzed by the fuzzy-matrix set. The prediction academic data is referred as the management of data or data mining. Data mining is the science of analyzing the data for obtaining more information than Keywords the current information. The hidden information appears by this technique.]]>
<![CDATA[An enhanced hybridized artificial bee colony algorithm for optimization problems ]]>
<![CDATA[Xingwang Huang]]>;
<![CDATA[Xuewen Zeng]]>;
<![CDATA[Rui Han]]>;
<![CDATA[Xu Wang]]>
<![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.pp87-94
<![CDATA[Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.]]>
<![CDATA[Comparative between (LiNbO3) and (LiTaO3) in detecting acoustics microwaves using classification ]]>
<![CDATA[Hafdaoui Hichem]]>;
<![CDATA[Benatia Djamel]]>
<![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.pp33-43
<![CDATA[Our work is mainly about detecting acoustics microwaves in the type of BAW (Bulk acoustic waves), where we compared between Lithium Niobate (LiNbO3) and Lithium Tantalate (LiTaO3), during the propagation of acoustic microwaves in a piezoelectric substrate. In this paper, We have used the classification by Probabilistic Neural Network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity for conclude whichever is the best in utilization for generating bulk acoustic waves.This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.]]>
