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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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Neural Network Controller for Power Electronics Circuits K.J. Rathi; M. S. Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Artificial Intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics. This paper explores the perspective of neural network applications in the intelligent control for power electronics circuits. The Neural Network Controller (NNC) is designed to track the output voltage and to improve the performance of power electronics circuits. The controller is designed and simulated using MATLAB-SIMULINK

Prediction of Future Stock Close Price using Proposed Hybrid ANN Model of Functional Link Fuzzy Logic Neural Model Kumaran Kumar. J; Kailas A
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (266.671 KB)

In this paper, the prediction of future stock close price of SENSEX & NSE stock exchange is found using the proposed Hybrid ANN model of Functional Link Fuzzy Logic Neural Model. The historic raw data’s of SENSEX & NSE stock exchange has been pre-processed to the range of (0 to 1). After pre-processing the inputs and forwarded to functional expansion function to perform neural operation. The activation function of neuron has fuzzy sets in order to show the future close price range of SENSEX & NSE stock exchange. The model is trained with the pre-processed historic data’s of stock exchange and the prediction rate (Performance & Error rate) of the Proposed Hybrid ANN model of Functional Link Fuzzy Logic Neural Model is calculated at the testing phase using the performance metrics (MAPE & RMSE).DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.362

Artificial Intelligence: Way Forward for India Sunil Kumar Srivastava
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Artificial Intelligence (AI) is likely to transform the way we live and work. Due to its high potential, its adoption is being treated as the fourth industrial revolution. As with any major advancement in technology, it brings with it a spectrum of opportunities as well as challenges. On one hand, several applications have been developed or under development with potential to improve the quality of life significantly. As per a study, it is expected to double the annual economic growth rate of 12 developed countries by 2035. On the other hand, there is a possibility of loss of jobs. As per the available reports, the loss of jobs during the next 10-20 years is estimated to be 47% in the US, 35% in the UK, 49% in Japan, 40% in Australia, and 54% in the EU. In the era of globalization, no country can isolate itself from the impact of the advances in technology. However, the benefits can be maximized and losses can be minimized by putting necessary infrastructure and policy in place. Though several countries have decided their strategy for AI, India has not yet formulated its strategy. The report reviews the international as well as national scenario and suggests way forward for India.

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)

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 Novel Optimization Algorithm Based on Stinging Behavior of Bee S. Jayalakshmi; R. Aswini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Optimization algorithms are search methods to find an optimal solution to a problem with a set of constraints. Bio-Inspired Algorithms (BIAs) are based on biological behavior to solve a real world problem. BIA with optimization technique is to improve the overall performance of BIA. The aim of this paper is to introduce a novel optimization algorithm which is inspired by natural stinging behavior of honey bee to find the optimal solution. This algorithm performs both monitor and sting if any occurrence of predators. By applying a novel optimization algorithm based on stinging behavior of bee, used to solve the intrusion detection problems. In this paper, a new host intrusion detection system based on novel optimization algorithm has been proposed and implemented. The performance of the proposed Anomaly-based Host Intrusion Detection System (A-HIDS) using a novel optimization algorithm based on stinging behavior of bee has been tested. In this paper, after an explanation of the natural stinging behavior of honey bee, a novel optimization algorithm and A-HIDS are described and implemented. The results show that the novel optimization algorithm offers some advantage according to the nature of the problem.

Unknown Word Detection via Syntax Analyzer Soe Lai Phyue
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 (72.889 KB)

A knowledge resource is the central repository of data for all Natural Language Processing (NLP) applications and development of NLP applications mostly depend on coverage of knowledge resources. The multipurpose Myanmar Language Lexico-conceptual Knowledge Resource (ML2KR) and Myanmar function tagged corpus were developed as initial resources by using semiautomatic approach. ML2KR consists of Myanmar WordNet, Myanmar English bilingual computational lexicon and morphological processor. Myanmar language is morphologically rich and agglutinative language. Therefore, it is usually required to segment Myanmar texts prior to further processing. Segmentation has two main problems, word ambiguity that more than one meaning and unknown word occurrence that a word does not have in the lexicon. In this paper, we address on the unknown word occurrence issue. To detect the new unrestricted character patterns of words, character based parsing syntax analyzer is built by using Context Free Grammar (CFG). Firstly, unknown words are considered as a Name by Name Entity Recognition with forward and backward rule based approach. If the name does not agree with syntax analyzer, all possible unknown words are verified to update the lexicon and Myanmar WordNet.DOI: http://dx.doi.org/10.11591/ij-ai.v2i3.1802

An improved radial basis function networks in networks weights adjustment for training real-world nonlinear datasets Lim Eng Aik; Tan Wei Hong; Ahmad Kadri Junoh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 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 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.

Utilizing CommonKADS as Problem-Solving and Decision-Making for Supporting Dynamic Virtual Organization Creation Morcous M. Yassa; Hesham A. Hassan; Fatma A. Omara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

Business Opportunity (BO) needs business collaboration and rapid distributed solution. Legacy systems are not enough to cope with it and there is a need to create Dynamic Virtual Organizations (DVO). While ecosystems have no agree in this area of business markets, some earlier DVO work used ecosystems to handle BO. The main objective of this paper is to show how CommonKADS knowledge engineering methodology is used to model DVO; life cycle, identification, and formation. Towards this objective, different perspectives used to analyze Collaboration Network Organization (CNO) have been discussed. Also, four more perspectives (CNO boundary fixing, organizational behavior, CNO federation modeling, and external environments) have been suggested to obtain what we called a Federated CNO Model (FCNOM). We believe that according to the work in this paper, the negotiations within CNO components during its life cycle will be minimized, the DVO configuration automation will be support, and more harmonization between CNO partners will be accomplished.

Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0 M. Aldiki Febriantono; Sholeh Hadi Pramono; Rahmadwati Rahmadwati; Golshah Naghdy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

The multiclass imbalanced data problems in data mining were an interesting to study currently. The problems had an influence on the classification process in machine learning processes. Some cases showed that minority class in the dataset had an important information value compared to the majority class. When minority class was misclassification, it would affect the accuracy value and classifier performance. In this research, cost sensitive decision tree C5.0 was used to solve multiclass imbalanced data problems. The first stage, making the decision tree model uses the C5.0 algorithm then the cost sensitive learning uses the metacost method to obtain the minimum cost model. The results of testing the C5.0 algorithm had better performance than C4.5 and ID3 algorithms. The percentage of algorithm performance from C5.0, C4.5 and ID3 were 40.91%, 40, 24% and 19.23%.

Enhanced Camera Calibration for Machine Vision using OpenCV Shubham Rohan Asthana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

In several machine vision applications, a fundamental step is to precisely determine the relation between the image of the object and its physical dimension by performing a calibration process. The aim is to devise an enhanced mechanism for camera calibration in order to improve the already existing methods in OpenCV. A good calibration is important when we need to reconstruct a world model or interact with the world as in case of robot, hand-eye coordination. In order to meet the rising demands for higher accuracy various calibration techniques have been developed but they are unable in obtaining precise results. In this paper we propose an enhanced camera calibration procedure using a special grid pattern of concentric circles with special markers. The overall objective is to minimize the re-projection for good camera calibration.

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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