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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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A Fuzzy Controller for Compensation of Voltage Sag/Swell Problems Using Reduced Rating Dynamic Voltage Restorer Rajesh Damaraju; S.V.N.L. Lalitha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 2: June 2015
Publisher : Institute of Advanced Engineering and Science

Non linear loads are highly effected by variations in voltages. Dynamic voltage restorer is one of the most popular compensating devices due to its low cost and better performance. Usage of Park’s transformation technique effectively reduces the rating of Dynamic voltage restorer. Application of fuzzy logic controller for getting the better result is proposed in this paper. The results are verified in Matlab/Simulink environment.

Optimization of artificial neural network topology for membrane bioreactor filtration using response surface methodology Syahira Ibrahim; Norhaliza Abdul Wahab; Fatimah Sham Ismail; Yahaya Md Sam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

The optimization of artificial neural networks (ANN) topology for predicting permeate flux of palm oil mill effluent (POME) in membrane bioreactor (MBR) filtration has been investigated using response surface methodology (RSM). A radial basis function neural network (RBFNN) model, trained by gradient descent with momentum (GDM) algorithms was developed to correlate output (permeate flux) to the four exogenous input variables (airflow rate, transmembrane pressure, permeate pump and aeration pump). A second-order polynomial model was developed from training results for natural log mean square error of 50 developed ANNs to generate 3D response surfaces. The optimum ANN topology had minimum ln MSE when the number of hidden neurons, spread, momentum coefficient, learning rate and number of epochs were 16, 1.4, 0.28, 0.3 and 1852, respectively. The MSE and regression coeffcient of the ANN model were determined as 0.0022 and 0.9906 for training, 0.0052 and 0.9839 for testing and 0.0217 and 0.9707 for validation data sets. These results confirmed that combining RSM and ANN was precise for predicting permeates flux of POME on MBR system. This development may have significant potential to improve model accuracy and reduce computational time.

A Low Power, Low Noise Amplifier for Recording Neural Signals G. Deepika; K.S. Rao
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

The design of a low power amplifier for recording EEG signals is presented. The low noise design techniques are used in this design to achieve low input referred noise that is near the theoretical limit of any amplifier using a differential pair as input stage. To record the neural spikes or local field potentials (LFP’s) the amplifier’s bandwidth can be adjusted. In order to reject common-mode and power supply noise differential input pair need to be included in the design. The amplifier achieved a gain of 53.7dB with a band width of 0.5Hz to1.1 kHz and input referred noise measured as 357 nVrms operated with a supply voltage of 1.0V. The total power consumed is around 3.19µW. When configured to record neural signals the gain measured is 54.3 dB for a bandwidth of 100 Hz and the input referred noise is 1.04µ Vrms. The amplifier was implemented in 180nm technology and simulated using Cadence Virtuoso.

Classification of Road Damage from Digital Image Using Backpropagation Neural Network Sutikno Sutikno; Helmie Arif Wibawa; Prima Yusuf Budiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

One of the biggest causes of death in the world is a traffic accident. Road damage is one of the cause factors from the traffic accident. To reduce this problem is required an early detection against road damage. This paper describes how to classify road damage using image processing and backpropagation neural network. Image processing is used to obtain binary image consists of a normalization, grayscaling, edge detection and thresholding, while the backpropagation neural network algorithm is used for classifying. The conclusion of this test that the algorithm is able to provide the accuracy rate of 83%. The results of this research may contribute to the development of road damage detection system based on the digital image so that the traffic accidents caused by road damage can be reduced.

Implementation of Artificial Bee Colony Algorithm Vimal Nayak; Haresh A. Suthar; Jagrut Gadit
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 3: September 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (238.577 KB)

Evolutionary algorithm is a stochastic search method that mimics the natural biological evolution and the social behavior of species. Artificial bee colony algorithm is also a kind of evolutionary algorithm which was proposed by Dervis karaboga in 2005.Such algorithms have been developed to arrive at near-optimum solutions of multimodal optimization problems, which may not be possible with traditional algorithms. This paper describes implementation of ABC algorithm on complex benchmark functions like rastrigin, rosenbrock; sphere and schwefel the analysis of the performance of ABC algorithm were compared for the optimization of above benchmark functions with Partical Swarm Optimization (PSO). The ABC algorithm was successfully implemented in software tool ‘c’.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.588

An enhanced hybridized artificial bee colony algorithm for optimization problems Xingwang Huang; Xuewen Zeng; Rui Han; Xu Wang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

Support Vector Machines for Object Based Building Extraction in Suburban Area using Very High Resolution Satellite Images, a Case Study: Tetuan, Morocco Omar Benarchid; Naoufal Raissouni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (397.694 KB)

Many fields of artificial intelligence have been developed such as computational intelligence and machine learning involving neural networks, fuzzy systems, genetic algorithms, intelligent agents and Support Vector Machines (SVM). SVM is a machine learning methodology with great results in image classification. In this paper, we present the potential of SVMs to automatically extract buildings in suburban area using Very High Resolution Satellite (VHRS) images. To achieve this goal, we use object based approach: Segmentation before classification in order to create meaningful image objects using color features. In the first step, we form objects with the aid of mean shift clustering algorithm. Then, SVM classifier was used to extract buildings. The proposed method has been applied on a suburban area in Tetuan city (Morocco) and 83.76% of existing buildings have been extracted by only using color features. This result can be improved by adding other features (e.g., spectral, texture, morphology and context).DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1781

Comparative between (LiNbO3) and (LiTaO3) in detecting acoustics microwaves using classification Hafdaoui Hichem; Benatia Djamel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Our work is mainly about detecting acoustics microwaves in the type of BAW (Bulk acoustic waves), where we compared between Lithium Niobate (LiNbO3) and Lithium Tantalate (LiTaO3), during the propagation of acoustic microwaves in a piezoelectric substrate. In this paper, We have used the classification by Probabilistic Neural Network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity for conclude whichever is the best in utilization for generating bulk acoustic waves.This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Suggestive GAN for supporting Dysgraphic drawing skills Smita Pallavi; Akash Kumar; Abhinav Ankur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

The squat competence of dysgraphia affected students in drawing graphics on paper may deter the normal pace of learning skills of children. Convolutional neural network may tend to extract and stabilize the actionmotion disorder by reconstructing features and inferences on natural drawings. The work in this context is to devise a scalable Generative Adversarial Network system that allows training and compilation of image generation using real time generated images and Google QuickDraw dataset to use quick and accurate modalities to provide feedback to empower the guiding software as an apt substitute for human tutor. The training loss accuracy of both discriminator and generator networks is also compared for the SGAN optimizer.

Handover Decision Mechanism in Interworking Technologies Using Radial Basis Functions Payal Mahajan; Kuldeep Singh; Hardeep Kaur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 2: June 2014
Publisher : Institute of Advanced Engineering and Science

As a mobile user travels between radio networks, a handover mechanism is required to vary its radio connection. The persistence of a call is one of the major quality measurements in wireless cellular networks. Handover mechanism permits a cellular network to offer such a facility by again allocating an ongoing call from one base station to another base station. To achieve handover neural network techniques can be used. In this paper, a handover decision mechanism is proposed using Radial Basis function (RBF) of neural networks.

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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

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