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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
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.
Arjuna Subject : -
Articles 6 Documents
 1 
Search results for , issue "Vol 1, No 4: December 2012" : 6 Documents clear
Independent Task Scheduling in Grid Computing Based on Queen Bee Algorithm Zahra Pooranian; Mohammad Shojafar; Bahman Javadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 4: December 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (619.519 KB)

Abstract

The inherent dynamicity in grid computing has made it extremely difficult to come up with near-optimal solutions to efficiently schedule tasks in grids. Task Scheduling plays crucial role in Grid computing. It is a challengeable issue among scientists to achieve better results especially in makespan based on various AI methods. Nowadays, non deterministic algorithms provide better results for these tasks. In this study the task scheduling problem in Grid computing environments has been addressed. In this paper, Queen Bee Algorithm is used for resolving scheduling problem and the obtained results are compared with several Meta–heuristic Algorithms which are developed to solve the problem. As it illustrated, queen bee algorithm is declined considerably makespan and execution time parameters rather than others in different states.DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.1229
Performance Analysis of Granular Computing Model on the Basis of S/W Engineering and Data Mining Rajashree Sasamal; Rabindra Kumar Shial
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 4: December 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (252.416 KB)

Abstract

Granular Computing is not only a computing model for computer centered problem solving, but also a thinking model for human centered problem solving.Some authors have presented the structure of such kind models and investigated various perspectives of granular computing from different application  point of views.  In this paper we discuss the archeitectue of Granular computing  models, strategies, and applications. Especially, the perspectives of granular computing in various aspects as data mining and  phases of software engineering are presented, including recquirement specification system analysis and design, algorithm design,structured programming,software tesing.AI is used for measuring the three perspective  of Granular Computing model. Here we have discovered the patterns in sequence of events has been an area  of active research in AI. However, the focus in this body of work is on discovering the rule underlying the generation of a given sequence in order to be able to predict a plausible sequence continuation ( the rule to predict what number will come next, given a sequence of numbers).DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.1181
A Multi-Agent Task Scheduling In University Environment Tariq Mahmood; M. Shahid Farid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 4: December 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.682 KB)

Abstract

Task scheduling problems are involved in almost every field of life from industry, where scheduling of employees on different machines with different shifts with respect to various constraints, to universities where scheduling involved in time tabling of classes and faculty, in examination scheduling, laboratory scheduling, staff scheduling and so on. Scheduling problem involves scheduling of different resources under various constraints to attain optimal results. In this paper we present a multi-agent based solution to Task Scheduling Problem (TSP) in university environment. It involves two main scheduling  problmes; first, time tabling probelm (TTP) and second  examination scheduling problem (ESP). In time tabling problem, a time table of classes is consturcted subject to different constraints; like rooms, subjects, teachers, degrees and semester with in a degree program. in examination scheduling problem is central to scheduling issue to every university. In ESP, the schedule of the examination of different courses of different degrees invigilated by different faculty members each with his/her availability constraints, is carried out. The problem is even worse when students of different degrees takes a shared course and when there are add-drops students in a course. In this case, the complexity of the scheduling problem doubles, now scheduling has to done with respect to the constraints of faculty, degree and also to  decrease the number of clashes in examination. An agent based solution to TSP is proposed in this paper which is also implemented and tested over different scenarios and optimal results are achieved in negligible amount of time.DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.708
A Semi-Automated Lyrics Generation Tool for Mauritian Sega Sameerchand Pudaruth; Bibi Feenaz Bhaukaurally; Mohammad Haydar Ally Didorally
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 4: December 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper, we give an overview of how Sega lyrics, in Mauritian Creole language, are being written by Mauritian Lyricists and a tool which has been developed to automatically generate Sega lyrics. Research shows that song writing is not always an easy task. Someone cannot be told exactly how to write lyrics, but that does not mean there are not ways in which he/she can learn to do it better. In-depth analysis has been carried out on Natural Language Processing, Text Mining, Machine Learning and existing Sega lyrics to consolidate the foundation of the project. Interviews have been done with a domain expert to learn the process of conventional song writing. Thus a tool, Paroles Sega Morisien, was developed. Paroles Sega Morisien enables users to generate Sega lyrics from randomly selected Mauritian Creole keywords. It is the first time that such a tool has been developed. An evaluation, consisting of a comparability study, was carried out to compare existing lyrics against lyrics generated by the tool. The result obtained was favorable.DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.801
Support Vector Machines Regression for MIMO-OFDM Channel Estimation Anis Charrada
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 4: December 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (357.255 KB)

Abstract

In this paper, we propose a robust highly selective nonlinear channel estimator for Multiple -Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system using complex Support Vector Machines Regression (SVR) and applied to Long Term Evolution (LTE) downlink under high mobility conditions .The new method uses the information provided by the pilot signals to estimate the total frequency response of the channel in two phases: learning phase and estimation phase. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the highly selective multipath channel in the presence of non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm maps trained data into a high dimensional feature space and uses the Structural Risk Minimization (SRM) principle to carry out the regression estimation for the frequency response function of the highly selective channel. The simulations show the effectiveness of the proposed method which has good performance and high precision to track the variations of the fading channels compared to the conventional LS method and it is robust under high mobility conditions.DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.1832
Face Recognition Using Two Dimensional Discrete Cosine Transform, Linear Discriminant Analysis And K Nearest Neighbor Classifier D. Sridhar; I. V. Murali Krishna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 4: December 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (376.936 KB)

Abstract

In this paper, a new Face Recognition method based on Two Dimensional Discrete Cosine Transform with Linear Discriminant Analysis (LDA) and K Nearest neighbours (KNN) classifier is proposed. This method consists of three steps, i) Transformation of images from special to frequency domain using Two dimensional discrete cosine transform ii) Feature extraction using Linear Discriminant Analysis and iii) classification using K Nearest Neighbour  classifier. Linear Disceminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Two Dimensional   Discrete Cosine transform and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. K Nearest Neighbour classifier gives fast and accurate classification of face images that makes this method useful in online applications. Evaluation was performed on two face data bases. First database of 400 face images from AT&T face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers. The main advantage of this method is its high speed processing capability and low  computational requirements in terms of both speed and memory utilizations.DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.767

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