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
1,639 Documents
<![CDATA[Design an Algorithm for Software Development in Cbse Environment using Feed Forward Neural Network ]]>
<![CDATA[Amit Verma]]>;
<![CDATA[Pardeep kaur]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 2: June 2015 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v4.i2.pp53-61
<![CDATA[A In software development organizations, Component based Software engineering (CBSE) is emerging paradigm for software development and gained wide acceptance as it often results in increase quality of software product within development time and budget. In component reusability, main challenges are the right component identification from large repositories at right time. The major objective of this work is to provide efficient algorithm for storage and effective retrieval of components using neural network and parameters based on user choice through clustering. This research paper aims to propose an algorithm that provides error free and automatic process (for retrieval of the components) while reuse of the component. In this algorithm, keywords (or components) are extracted from software document, after by applying k mean clustering algorithm. Then weights assigned to those keywords based on their frequency and after assigning weights, ANN predicts whether correct weight is assigned to keywords (or components) or not, otherwise it back propagates in to initial step (re-assign the weights). In last, store those all keywords into Repositories for effective retrieval. Proposed algorithm is very effective in the error correction and detection with user base choice while choice of component for reusability for efficient retrieval is there. To check the results of our algorithm based on factors like accuracy, precision and recall compare with existing technique i.e. integrated classification scheme for retrieval of components based on keyword search and results are so encouraging.]]>
<![CDATA[Modeling of submerged membrane filtration processes using recurrent artificial neural networks ]]>
<![CDATA[Zakariah Yusof]]>;
<![CDATA[Norhaliza Abdul Wahab]]>;
<![CDATA[Syahira Ibrahim]]>;
<![CDATA[Shafishuhaza Sahlan]]>;
<![CDATA[Mashitah Che Razali]]>
<![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.pp155-163
<![CDATA[The modeling of membrane filtration processes is a challenging task because it involves many interactions from both biological and physical operational behavior. Membrane fouling behaviour in filtration processes is complex and hard to understand, and to derive a robust model is almost not possible. Therefore, it is the aim of this paper to study the potential of time series neural network based dynamic model for a submerged membrane filtration process. The developed model that represent the dynamic behavior of filtration process is later used in control design of the membrane filtration processes. In order to obtain the dynamic behaviour of permeate flux and transmembrane pressure (TMP), a random step was applied to the suction pump. A recurrent neural network (RNN) structure was employed to perform as the dynamic models of a filtration process, based on nonlinear auto-regressive with exogenous input (NARX) model structure. These models are compared with the linear auto-regressive with exogenous input (ARX) model. The performance of the models were evaluated in terms of %R2, mean square error (MSE,) and a mean absolute deviation (MAD). For filtration control performance, a proportional integral derivative (PID) controller was implemented. The results showed that the RNN-NARX structure able to model the dynamic behavior of the filtration process under normal conditions in short range of the filtration process. The developed model can also be a reliable assistant for two different control strategies development in filtration processes.]]>
<![CDATA[CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns ]]>
<![CDATA[Athira TR]]>;
<![CDATA[Abraham Varghese]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v6.i1.pp8-17
<![CDATA[Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.]]>
<![CDATA[Identifying Risk Factors of Diabetes using Fuzzy Inference System ]]>
<![CDATA[Lazim Abdullah]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 4: December 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v6.i4.pp150-158
<![CDATA[Identification of the real risk factors of diabetes is still very much inconclusive. In this paper, fuzzy rules based system was devised to identify risk factors of diabetes. The system consists of five input variables: Body Mass Index, age, blood pressure, Creatinine, and serum cholesterol and one output variable: level of risk. Three Gaussian membership functions for linguistic terms are defined for each input variable. The level of risk is defined using three triangular membership functions to represent output variable. Based on the information from patients’ clinical audit reports, the system was used to classify the level of risk of fifty patients that currently undergoing regular diagnosis for diabetes treatment. The system successfully classified the risk into three levels of Low, Medium and High where three main contributing factors toward developing diabetes were identified. The three risk factors are age, blood pressure and serum cholesterol. The multi-input system that characterised by IF-THEN fuzzy rules provide easily interpretable result for identifying predictors of diabetes. Research to establish reproducibility and validity of the findings are left for future works.]]>
<![CDATA[Multi-Party Security System using Artificial Neural Networks ]]>
<![CDATA[Urvashi Rahul Saxena]]>;
<![CDATA[S.P Singh]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 3: September 2012 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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<![CDATA[Multi-Party Security System is an improvised version of various security systems available using Artificial Neural Networks (ANN’s) as an Intelligent Agent for Intrusion Detection. This Paper focuses how inputs can be preserved to serve as a measure for securing communication protocol between two parties using privacy protocols at the hidden layer of Multi-layer Perceptron model. Various neural network structures are observed for evaluating the optimal network considering the number of hidden layers. Results depict that the generated system is capable of classifying records with about 90% of accuracy when two hidden layers are engulfed and the accuracy reduces to 87% with one hidden layer under observation.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.739]]>
<![CDATA[Analysing Event-Related Sentiments on Social Media with Neural Networks ]]>
<![CDATA[P. Santhi Priya]]>;
<![CDATA[T. Venkate swara Rao]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 3: September 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i3.pp119-124
<![CDATA[Sentiment analysis is performed to determine the polarity of opinion on a subject. It has been applied to text corpora such as movie reviews, financial documents to glean information about overall-sentiment anc produce actionable data. Recent events have demonstrated that polling can be sometimes unreliable. People can be difficult to access through conventional polling methods and less than frank in polls. In the era of social media, voters are likely to more freely express their opinion on social media forums about divisive events especially in media where anonymity exists. Analyzing the prevailing opinion on these forums can indicate if there are any deficiencies in polling and can be a valuable addition to conventional polling. We analyzed text corpora from Reddit forums discussing the recent referendum in Britain to exit from the EU (known as Brexit). Brexit was an important world event and was very divisive in the run-up and post vote. We analyzed sentiment in two ways: Initially we tried to gauge positive, negative, and neutral sentiments. In the second analysis, we further split these sentiments into six different polarities based on the directionality of the positive and negative sentiments (for or against Brexit). Our technique utlilized paragraph vectors (Doc2Vec) to construct feature vectors for sentiment analysis with a Multilayer Perceptron classifier. We found that the second analysis yielded overall better results; although, our classifier didn’t perform as well in classifying positive sentiments. We demonstrate that it is possible glean valuable information from complicated and diverse corpora such as multi-paragraph comments from reddit with sentiment analysis.]]>
<![CDATA[Counting of People in the Extremely Dense Crowd using Genetic Algorithm and Blobs Counting ]]>
<![CDATA[Muhammad Arif]]>;
<![CDATA[Sultan Daud]]>;
<![CDATA[Saleh Basalamah]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 2: June 2013 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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<![CDATA[In this paper, we have proposed a framework to count the moving person in the video automatically in a very dense crowd situation. Median filter is used to segment the foreground from the background and blob analysis is done to count the people in the current frame. Optimization of different parameters is done by using genetic algorithm. This framework is used to count the people in the video recorded in the mattaf area where different crowd densities can be observed. An overall people counting accuracy of more than 96% is obtained.DOI: http://dx.doi.org/10.11591/ij-ai.v2i2.1793]]>
<![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[Artificial bee colony algorithm used for load balancing in cloud computing: review ]]>
<![CDATA[Arif Ullah]]>;
<![CDATA[Nazri Mohd Nawi]]>;
<![CDATA[Jamal Uddin]]>;
<![CDATA[Samad Baseer]]>;
<![CDATA[Ansam Hadi Rashed]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v8.i2.pp156-167
<![CDATA[Cloud computing is emerging technology in IT land. But it still faces challenges like load balancing. It is a technique which dynamic distributed work load among various nodes equally in a situation where some nodes are under load and some are overload. Main achievements of load balancing are resource consumption and reduce energy. Swarm intelligence provides an important role in the field of those problems which cannot easily solve and they need classical and mathematical technique. An artificial bee colony is a foraging behavior inspires algorithm it established by karaboga in 2005. It has fast convergence, strong, robustness, and high flexibility. The different researcher used ABC algorithm for improvement in load balancing. This review paper is a comprehensive study about load balancing in cloud computing using ABC algorithm. It also defines some basic concept about swarm intelligent and its property.]]>
<![CDATA[Self Tuning Based Adaptive Fuzzy Logic Controller in Lab view for Sterilizing Equipments ]]>
<![CDATA[P. J. Ragu]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 2: June 2014 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v3.i2.pp84-89
<![CDATA[In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.]]>
