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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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Sizing and Implementation of Photovoltaic Water Pumping System for Irrigation Santosh S. Raghuwanshi; Vikas Khare
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Solar photovoltaic systems convert energy of light directly into electrical energy. This work presents, a process to compute the required size of the stand-alone solar photovoltaic generator based water pumping system for an existing area. In addition solar photovoltaic generator is connecting voltage source inverter fed vector controlled induction motor-pump system. Perturb and observe are used for harvesting maximum power of PV generator in between buck-boost DC converter and inverter system. In this paper system result is validated by fuzzy logic system and compare with variable frequency drives based PI controllers, driving motor-pump system. The operational performance at 60 m head, VFD based controllers in terms overshoot and setting time and also analysis performance of motor-pump set under different weather conditions. By assessment of system we find that speed and torque variation, overshoot and settling time is more with PI controller, Fuzzy logic controller (FLC) performance have dominance to VFD based PI controller.

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)

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

Cat Swarm Optimization to Shunt Capacitor Allocation in Algerian Radial Distribution Power System Amar Hamzi; Rachide Meziane
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

This paper presents a Cat Swarm Optimization (CSO) Algorithm optimization method to shunt capacitor placement on distribution systems under capacitor switching constraints. The optimum capacitor allocation solution is found for the system of feeders fed through their transformer and not for any individual feeder. The main advantages due to capacitor installation, such as capacity release and reduction of overall power and energy losses are considered. The capacitor allocation constraints due to capacitor-switching transients are taken into account. These constraints are extremely important if pole-mounted capacitors are used together with station capacitor bank. Cat Swarm search algorithm is used as an optimization tool. An illustrative example for Algerian example is presented.

Artificial Bee Colony Algorithm for Economic Load Dispatch Problem Hardiansyah Hardiansyah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 2: June 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (237.487 KB)

In practical cases, the fuel cost of generators can be represented as a quadratic function of real power generation and satisfied constraints for minimizing of fuel cost. Artificial Bee Colony (ABC) algorithm is used for the optimization of active power dispatch of generating units. The proposed method is able to determine, the output power generation for all of the power generation units, so that the total cost is minimized. Simulation and analysis of economic load dispatch using Artificial Bee Colony (ABC) algorithm is proposed. The obtained results are compared with the conventional method, genetic algorithm (GA) and shows that the ABC algorithm approach is more feasible and efficient for finding minimum cost.DOI: http://dx.doi.org/10.11591/ij-ai.v2i2.1613

Memetic Algorithm for the Minimum Edge Dominating Set Problem Abdel-Rahman Hedar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 4: December 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.728 KB)

The minimum edge dominating set (MEDS) is one of the fundamental covering problems ingraph theory, which finds many practical applications in diverse domains. In this paper, wepropose a meta-heuristic approach based on genetic algorithm and local search to solve theMEDS problem. Therefore, the proposed method is considered as a memetic search algorithmwhich is called Memetic Algorithm for minimum edge dominating set (MAMEDS). Inthe MAMEDS method, a new fitness function is invoked to effectively measure the solutionqualities. The search process in the proposed method uses intensification schemes besidethe main genetic search operations in order to achieve faster performance. The experimentalresults proves that the proposed method is promising in solving the MEDS problem.DOI: http://dx.doi.org/10.11591/ij-ai.v2i4.3481

Prediction of bankruptcy using big data analytic based on fuzzy c-means algorithm Arup Guha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

This paper has suggested an optimization approach of the cluster-based sampling using Fuzzy c means algorithm to the classifier in order to select the most appropriate instances of bankruptcy. This method was examined with the help of a clustering method and GA based artificial neural network in order to solve the existing data imbalance issue. The objective of this paper is to optimize the selected design model of GA-ANN by using Fuzzy C means algorithm to predict corporate bankruptcies by considering different financial ratios of companies across several industries within the period from 1994 to 2014. Effectiveness of this method was proved by comparing its accuracy rate with the results of existing method. From the performance result the accuracy rate of this method was found to be 78.2% and misclassification rate to be 0.2178.

Optical Character Recognition of Off-Line Typed and Handwritten English Text Using Morphological and Template Matching Techniques Olakanmi Olufemi Oladayo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

The existence of several documents in historical archives which need to be edited and stored in a computer has been one of the drives of Optical Character Reader (OCR) research. Earlier scanner has been used to achieve this tedious task however scanner only produces picture images of the documents.This makes the documents unreadable and un-editable through other word processing applications.This paper proposed an OCR system which converts off line typed and handwritten texts into their editable textual representations.The morphological correlation technique improves the mapping and recognition efficiency of the OCR system.

Review of anomalous sound event detection approaches Amirul Sadikin Md Affendi; Marina Yusoff
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

This paper presents a review of anomalous sound event detection (SED) approaches. SED is becoming more applicable for real-world appliactaions such as security, fire determination or olther emergency alarms. Despite many research outcome previously, further research is required to reduce false positives and improve accurracy. SED approaches are comprehensively organized by methods covering system pipeline components of acoustic descriptors, classification engine, and decision finalization method. The review compares multiple approaches that is applied on a specific dataset. Security relies on anomalous events in order to prevent it one must find these anomalous events. Audio surveillance has become more efficient as that artificial intelligence has stepped up the game. Autonomous SED could be used for early detection and prevention. It is found that the state of the art method viable used in SED using features of log-mel energies in convolutional recurrent neural network (CRNN) with long short term memory (LSTM) with a verification step of thresholding has obtained 93.1% F1 score and 0.1307 ER. It is found that feature extraction of log mel energies are highly reliable method showing promising results on multiple experiments.

Rule Based and Expectation Maximization algorithm for Arabic-English Hybrid Machine Translation Arwa Hatem Alqudsi; Nazlia Omar; Rabha W. Ibrahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 2: June 2016
Publisher : Institute of Advanced Engineering and Science

It is practically impossible for pure machine translation approach to process all of translation problems; however, Rule Based Machine Translation and Statistical Machine translation (RBMT and SMT) use different architectures for performing translation task. Lexical analyser and syntactic analyser are solved by Rule Based and some amount of ambiguity is left to be solved by Expectation–Maximization (EM) algorithm, which is an iterative statistic algorithm for finding maximum likelihood. In this paper we have proposed an integrated Hybrid Machine Translation (HMT) system. The goal is to combine the best properties of each approach. Initially, Arabic text is keyed into RBMT; then the output will be edited by EM algorithm to generate the final translation of English text. As we have seen in previous works, the performance and enhancement of EM algorithm, the key of EM algorithm performance is the ability to accurately transform a frequency from one language to another. Results showing that, as proved by BLEU system, the proposed method can substantially outperform standard Rule Based approach and EM algorithm in terms of frequency and accuracy. The results of this study have been showed that the score of HMT system is higher than SMT system in all cases. When combining two approaches, HMT outperformed SMT in Bleu score.

Hybridisation of RF(Xgb) to improve the tree-based algorithms in learning style prediction Haziqah Shamsudin; Maziani Sabudin; Umi Kalsom Yusof
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

This paper presents hybridization of Random Forest (RF) and Extreme Gradient Boosting (Xgb), named RF(Xgb) to improve the tree-based algorithms in learning style prediction. Learning style of specific users in an online learning system is determined based on their interaction and behavior towards the system. The most common online learning theory used in determining the learning style is the Felder-Silverman’s Learning Style Model (FSLSM). Many researchers have proposed machine learning algorithms to establish learning style by using the log file attributes. This helps in determining the learning style automatically. However, current researches still perform poorly, where the range of accuracy is between 58%-89%. Hence, RF(Xgb) is proposed to help in improving the learning style prediction. This hybrid algorithm was further enhanced by optimizing its parameters. From the experiments, RF(Xgb) was proven to be more effective, with accuracy of 96% compared to J48 and LSID-ANN algorithm from previous literature.

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OAI Link

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Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

Phone
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Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

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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