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
744 Documents
<![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[Quality Model and Artificial Intelligence Base Fuel Ratio Management with Applications to Automotive Engine ]]>
<![CDATA[Farzin Piltan]]>;
<![CDATA[Mansour Bazregar]]>;
<![CDATA[Marzieh Kamgari]]>;
<![CDATA[Mojdeh Piran]]>;
<![CDATA[Mehdi Akbari]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v3.i1.pp36-48
<![CDATA[In this research, manage the Internal Combustion (IC) engine modeling and a multi-input-multi-output artificial intelligence baseline chattering free sliding mode methodology scheme is developed with guaranteed stability to simultaneously control fuel ratios to desired levels under various air flow disturbances by regulating the mass flow rates of engine PFI and DI injection systems. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity. The fuzzy inference baseline sliding methodology performance was compared with a well-tuned baseline multi-loop PID controller through MATLAB simulations and showed improvements, where MATLAB simulations were conducted to validate the feasibility of utilizing the developed controller and state estimator for automotive engines. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired FR and fuel mass over a given time interval.]]>
<![CDATA[Adaptive ANN based differential protective relay for reliable power transformer protection operation during energisation ]]>
<![CDATA[Azniza Ahmad]]>;
<![CDATA[Mohammad Lufti Othman]]>;
<![CDATA[Kurreemun Khudsiya Bibi Zainab]]>;
<![CDATA[Hashim Hizam]]>;
<![CDATA[Norhafiz Azis]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v8.i4.pp307-316
<![CDATA[Power transformer is the most expensive equipment in electrical power system that needs continuous monitoring and fast protection response. Differential relay is usually used in power transformer protection scheme. This protection compares the difference of currents between transformer primary and secondary sides, with which a tripping signal to the circuit breaker is asserted. However, when power transformers are energized, the magnetizing inrush current is present and due to its high magnitude, the relay mal-operates. To prevent mal-operation, methods revolving around the fact that the relay should be able to discriminate between the magnetizing inrush current and the fault current must be studied. This paper presents an Artificial Neural Network (ANN) based differential relay that is designed to enable the differential relay to correct its mal-operation during energization by training the ANN and testing it with harmonic current as the restraining element. The MATLAB software is used to implement and evaluate the proposed differential relay. It is shown that the ANN based differential relay is indeed an adaptive relay when it is appropriately trained using the Network Fitting Tool. The improved differential relay models also include a reset part which enables automatic reset of the relays. Using the techniques of 2nd harmonic restraint and ANN to design a differential relay thus illustrates that the latter can successfully differentiate between magnetizing inrush and internal fault currents. With the new adaptive ANN-based differential relay, there is no mal-operation of the relay during energization. The ANN based differential relay shows better performance in terms of its ability to differentiate fault against energization current. Amazingly, the response time, when there is an internal fault, is 1 ms compared to 4.5 ms of the conventional 2nd harmonic restraint based relay.]]>
<![CDATA[An Optimized Takagi-Sugeno Fuzzy-Based Satellite Attitude Controller by Two State Actuator ]]>
<![CDATA[Sobutyeh Rezanezhad]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 2014 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v3.i4.pp166-176
<![CDATA[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.]]>
<![CDATA[Intelligent risk management framework ]]>
<![CDATA[Wissam Abbass]]>;
<![CDATA[Zineb Bakraouy]]>;
<![CDATA[Amine Baina]]>;
<![CDATA[Mostafa Bellafkih]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v8.i3.pp278-285
<![CDATA[The Internet of Things (IoT) is rapidly increasing and enhancing today’s world by introducing a large set of interconnected devices. Several beneficial services are produced by these devices as for area monitoring and process control. However, IoT security is still a major problem. In fact, IoT’ security beggings largely whith an effective Risk Management process. However, the essense of this process is to acquire a risk inventory cibling the IoT devices. Nevertheless, it is quite difficult to obtaining this latter which significantly adds complication issues to the Risk Management. Without the ability of holisticly identify the IoT critical devices, inaccurate Risk Management is achieved which leads unfortunately to novel risk exposures. Traditional Riskbased approaches fails drastically at apprending IoT’ potential attacks. The dynamic structure, the heteregouns nature of devices, the various security objectives and infrastructure pervasiveness are key factors impacting the overall perfomance. Thus, a holistic Risk Management witihin the IoT is indispensable. Accordingly, we propose an intelligent Risk Management framework using Mobile Agents in order to deliver preventive and responsive assessment.]]>
<![CDATA[The Cheapest Shop Seeker : A New Algorithm For Optimization Problem in a Continous Space ]]>
<![CDATA[Peter Bamidele Shola]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 3: September 2016 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v5.i3.pp119-126
<![CDATA[In this paper a population-based meta-heuristic algorithm for optimization problems in a continous space is presented.The algorithm,here called cheapest shop seeker is modeled after a group of shoppers seeking to identify the cheapest shop (among many available) for shopping. The algorithm was tested on many benchmark functions with the result compared with those from some other methods. The algorithm appears to have a better success rate of hitting the global optimum point of a function and of the rate of convergence (in terms of the number of iterations required to reach the optimum value) for some functions in spite of its simplicity.]]>
<![CDATA[Iris segmentation using a new unsupervised neural approach ]]>
<![CDATA[Hicham Ohmaid]]>;
<![CDATA[S. Eddarouich]]>;
<![CDATA[A. Bourouhou]]>;
<![CDATA[M. Timouyas]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i1.pp58-64
<![CDATA[A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristic possessed by an individual. Iris recognition is a biometric identification method that uses pattern recognition on the images of the iris. Owing to the unique epigenetic patterns of the iris, Iris recognition is considered as one of the most accurate methods in the field of biometric identification. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction the iris. The segmentation algorithm proposed in this article starts with determining the regions of the eye using unsupervised neural approach, after the outline of the eye is found using the Canny edge, The Hough Transform is employed to determine the center and radius of the pupil and the iris.]]>
<![CDATA[Type2 Fuzzy Soft Computing Technique for Image Enhancement ]]>
<![CDATA[U. Sesadri]]>;
<![CDATA[B. Siva Sankar]]>;
<![CDATA[C. Nagaraju]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 3: September 2015 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v4.i3.pp97-104
<![CDATA[The mainpurpose of Image enhancement is to process an image so that outcome is more appropriate than original image for definite application. The fuzzy logic isone of the soft computing techniques to enhance the images by eliminating uncertainty.In this paper efficient type2 fuzzy logic technique is used to get betterquality image. This method consists of two steps. In the First step fisher criterion function is useful to generate type1 fuzzy membership value. In the second step based on type1 membership value fuzzy rules are derived to enhance the image. The type2 fuzzy method is compared with type1 fuzzy. The table values and graphs provethat the proposed method gives better results compared with fuzzy type1 method.]]>
<![CDATA[A Decision System for Predicting Diabetes using Neural Networks ]]>
<![CDATA[K. Chandana Rani]]>;
<![CDATA[Y. Prasanth]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 2: June 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v6.i2.pp56-65
<![CDATA[Diabetic retinopathy (DR) is an eye fixed ill complete by the impairment of polygenic disorder and that we purchased to acknowledge it before of calendar for sensible treatment. On these lines, 2 social occasions were perceived, specifically non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR). During this paper, to dissect diabetic retinopathy, 3 models like Probabilistic Neural framework (PNN), Bayesian Classification and Support vector machine (SVM) square measure pictured and their displays square measure thought-about. The live of the unwellness unfold within the membrane are often recognized by analytic the elements of the membrane. The elements like veins, hemorrhages of NPDR image and exudates of PDR image square measure off from the unrefined photos victimization the icon prepare techniques, fed to the classifier for gathering a complete of 350 structure photos were used, out of that100 were used for designing and 250 pictures were used for testing. Exploratory results show that PNN has an accuracy of 89.6 % Bayes Classifier incorporates a exactness of 94.4% and SVM has an exactitude of 97.6%. What is more our system is equally continue running on 130 pictures open from "DIARETDB0: Evaluation Database and Procedure for Diabetic Retinopathy" and also the results show that PNN incorporates a exactness of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has a precision of 95.38%.]]>
<![CDATA[Estimating Processed Cheese Shelf Life with Artificial Neural Networks ]]>
<![CDATA[Sumit Goyal]]>;
<![CDATA[Gyanendra Kumar Goyal]]>
<![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[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.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.336]]>
