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IAES International Journal of Artificial Intelligence (IJ-AI)

IJAI
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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 1,722 Documents

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Online dictionary learning for car recognition using sparse coding and LARS Ilias Kamal; Khalid Housni; Youssef Hadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

The bag of feature method coupled with online dictionary learning is the basis of our car make and model recognition algorithm. By using a sparse coding computing technique named LARS (Least Angle Regression) we learn a dictionary of codewords over a dataset of Square Mapped Gradient feature vectors obtained from a densely sampled narrow patch of the front part of vehicles. We then apply SVMs (Support Vector Machines) and KMeans supervised classification to obtain some promising results.

Conventional DC Voltage Operating System for High Speeed Traction Power Supplies Using LLC-HPQC Control Ch. Lenin Babu; P. Harinath Reddy; T. Reddi Sekhar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 4: December 2015
Publisher : Institute of Advanced Engineering and Science

In this paper a hybrid power quality compensator (HPQC) is proposed for compensation in cophase traction power supply and minimum dc operation voltage is achievable for high-speed traction power supply. The parameter design procedure for minimum dc operation voltage in HPQC as well as minimum voltage rating with load PF is discussed. The detailed discussions of proposed circuit configurations of HPQC are provided in section II, together with comparison with conventional RPC. In comparison with conventional railway power compensator proposed HPQC can achieve reduced dc link voltage level. It is also verified through simulations results that the LLC-HPQC would operate at the minimum voltage with the proposed parameter design. HPQC is able to provide system unbalances, reactive power, and harmonic compensation in cophase traction power with reduced operation voltage. The cophase traction power supply with proposed HPQC is suitable for high-speed traction applications.

An Approch based on Genetic Algorithm for Multi-tenant Resource Allocation in SaaS Applications Elaheh kheiri; Mostafa Ghobaei Arani; Alireza Taghizadeh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

In recent years, the use of cloud services has been significantly expanded. The providers of software as a service employ multi-tenant architectures to deliver services to their users. In these multi-tenant applications the resource allocation would suffer from over-utilization or under-utilization issues. Considering the significant effects of resource allocation on the service performance and cost, in this paper we have proposed an approach based on genetic algorithm for resource allocation which guarantees service quality through providing adequate resources. The proposed approach also improves system performance, meets the requirements of users and provides maximum resource efficiency. Simulation results show that the proposed approach has better response rate and availability comparing to other approaches, while provides an efficient resource usage.

RDVBT: Resource Distance Vector Binary Tree Algorithm for Resource Discovery in Grid SeyedElyar Hashemseresht; Ali Asghar Pourhaji Kazem
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (477.909 KB)

Nowadays, with the increasing variety of computer systems, resource discovery in the Grid environment has been very important due to their applications; thus, offering optimal and dynamic algorithms for discovering resources in which users need a short period is an important task in grid environments.One of the methods used in resource discovery in grid is to use routing tables RDV (resource distance vector) in which the resources are based on certain criteria clustering and the clusters form a graph. In this way, some information about the resources is stored in RDV tables. Due to the environmental cycle in the graph, there are some problems; for example there are multiple paths to resources, most of which are repeated. Also, in large environments, due to the existence of many neighbors, updating the graph is time-consuming. In this paper, the structure of RDV was presented as a binary tree and these two methods (RDV graph-algorithm and RDVBT) were compared. Simulation results showed that, as a result of converting the structure to a binary tree, much better results were obtained for routing time, table updating time and number of successful requests; also the number of unsuccessful requests was reduced.DOI: http://dx.doi.org/10.11591/ij-ai.v1i2.442

Optimization of Digital Histopathology Image Quality Furat N Tawfeeq; Nada A.S. Alwan; Basim M. Khashman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

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.

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (333.514 KB)

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

Improved Time Training with Accuracy of Batch Back Propagation Algorithm Via Dynamic Learning Rate and Dynamic Momentum Factor Mohammed Sarhan Al_Duais; Fatma Susilawati. Mohamad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

The man problem of batch back propagation (BBP) algorithm is slow training and there are several parameters needs to be adjusted manually, also suffers from saturation training.The learning rate and momentum factor are significant parameters for increasing the efficiency of the (BBP). In this study, we created a new dynamic function of each learning rate and momentum facor. We present the DBBPLM algorithm, which trains with a dynamic function for each the learning rate and momentum factor. A Sigmoid function used as activation function. The XOR problem, balance, breast cancer and iris dataset were used as benchmarks for testing the effects of the dynamic DBBPLM algorithm. All the experiments were performed on Matlab 2012 a. The stop training was determined ten power -5. From the experimental results, the DBBPLM algorithm provides superior performance in terms of training, and faster training with higher accuracy compared to the BBP algorithm and with existing works.

Designing Observer Based Variable Structure Controller for Large Scale Nonlinear Systems Reza Ghasemi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (222.463 KB)

Designing observer based decentralized fuzzy adaptive controller is discussed for a class of large scale non-canonical nonlinear systems with unknown functions of the subsystems in this paper. The On-line adaptation of the controller and the observer parameters, boundedness of the output and the observer errors, robustness against external disturbance are the advantages of the proposed method. The simulation results show the promising performance of the proposed method.DOI: http://dx.doi.org/10.11591/ij-ai.v2i3.2025

An improved radial basis function networks based on quantum evolutionary algorithm for training nonlinear datasets Lim Eng Aik; Tan Wei Hong; Ahmad Kadri Junoh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and spread value. Radial basis function network (RBFN) is a type of feedforward network that capable of perform nonlinear approximation on unknown dataset. It has been widely used in classification, pattern recognition, nonlinear control and image processing. Thus, with the increases in RBFN application, some problems and weakness of RBFN network is identified. Through the combination of quantum computing and RBFN provides a new research idea in design and performance improvement of RBFN system. This paper describes the theory and application of quantum computing and cloning operators, and discusses the superiority of these theories and the feasibility of their optimization algorithms.This proposed improved RBFN (I-RBFN) that combined with cloning operator and quantum computing algorithm demonstrated its ability in global search and local optimization to effectively speed up learning and provides better accuracy in prediction results. Both the algorithms that combined with RBFN optimize the centers and spread value of RBFN. The proposed I-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 I-RBFN for root mean square error (RMSE) values with standard RBFN. The proposed I-RBFN yielded better results with an average improvement percentage more than 90 percent in RMSE.

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor Minakshi Sharma; Saourabh Mukherjee
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties. Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

Phone
-

Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Imam Much Ibnu Subroto

Contact Email imam@unissula.ac.id

Phone -

Journal Mail Official ijai@iaesjournal.com

Editorial Address -

Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

Phone
-

Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA