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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 1,639 Documents

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Dwindling of Real Power Loss by Enriched Big Bang-Big Crunch Algorithm K. Lenin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

In this paper, Enriched Big Bang-Big Crunch (EBC) algorithm is proposed to solve the reactive power problem. The problem of converging to local optimum solutions occurred for the Bang-Big Crunch (BB-BC) approach due to greedily looking around the best ever found solutions. The proposed algorithm takes advantages of typical Big Bang-Big Crunch (BB-BC) algorithm and enhances it with the proper balance between exploration and exploitation factors. Proposed EBC algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the improved performance of the proposed algorithm in reducing the real power loss.

An Expert System with Neural Network and Decision Tree for Predicting Audit Opinions Seyed Mojtaba Saif; Mehdi Sarikhani; Fahime Ebrahimi
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 (360.222 KB)

Nowadays expert system, being used in various fields has received a great deal of attention. Auditing is one such field, along with determining the audit opinion type. An expert system consists of a knowledge database and an inference engine. The objective of this research is to make an expert system that will be of help to auditors in predicting and determining the different types of audit reports. The expert system receives data or knowledge from financial reports and determines the types of audit opinions by using an artificial neural network and a decision tree as an inference engine. An expert system should able to explain the solution, but presenting the reason for the results obtained with a neural network is difficult. This study attempts to provide a method that will present simple and understandable reasons for the results obtained with neural networks.DOI: http://dx.doi.org/10.11591/ij-ai.v2i4.3950

Adaptive real time traffic prediction using deep neural networks Parinith R Iyer; Shrutheesh Raman Iyer; Raghavendran Ramesh; Anala M R; K.N. Subramanya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

The ever-increasing sale of vehicles and the steady increase in population density in metropolitan cities have raised many growing concerns, most importantly commute time, air and noise pollution levels. Traffic congestion can be alleviated by opting adaptive traffic light systems, instead of fixedtime traffic signals. In this paper, a system is proposed which can detect, classify and count vehicles passing through any traffic junction using a single camera (as opposed to multi-sensor approaches). The detection and classification are done using SSD Neural Network object detection algorithm. The count of each class (2-wheelers, cars, trucks, buses etc.) is used to predict the signal green-time for the next cycle. The model selfadjusts every cycle by utilizing weighted moving averages. This system works well because the change in the density of traffic on any given road is gradual, spanning multiple traffic stops throughout the day.

Fuzzy Black Holes Algorithm Mostafa Nemati; Reza Salimi; Behdad Moshref
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

In this paper fuzzy version for black holes algorithm is proposed. The main idea of this article is based upon this principle that we should consider the distance between two black holes for calculating gravitational force (global search) and electrical force (local search). For this purpose, we have suggested Fuzzy distance notion. In this proposed idea, for calculating two forces, FQ and FG, considering the distance between black holes, we have defined a Fuzzy function, which receives distance value and depending on this value being low or high, produces a membership degree for gravitational and electrical constants to be used in the formulas related to the calculation of FG and FQ. The proposed method is verified using several benchmark problems used in the area of optimization. The experimental results on different benchmarks show that the performance of the proposed algorithm is better than basic BLA (Black holes Algorithm) and FPSO (fuzzy Particle Swarms Optimization).

Feature selection for human membrane protein type classification using filter methods Glenda Anak Kaya; Nor Ashikin Mohamad Kamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

As the number of protein sequences in the database is increasing, effective and efficient techniques are needed to make these data meaningful. These protein sequences contain redundant and irrelevant features that cause lower classification accuracy and increase the running time of the computational algorithm. In this paper, we select the best features using Minimum Redundancy Maximum Relevance (mRMR) and Correlationbased feature selection (CFS) methods. Two datasets of human membrane protein are used, S1 and S2. After the features have been selected by mRMR and CFS, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers are used to classify these membrane proteins. The performance of these techniques is measured using accuracy, specificity and sensitivity. and F-measure. The proposed algorithm managed to achieve 76% accuracy for S1 and 73% accuracy for S2. Finally, our proposed methods present competitive results when compared with the previous works on membrane protein classification.

Elastic Bunch Graph Matching Based Face Recognition Under Varying Lighting, Pose, and Expression Conditions Farooq Ahmad Bhat; M. Arif Wani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 2014
Publisher : Institute of Advanced Engineering and Science

In this paper performance of elastic bunch graph matching (EBGM) for face recognition under variation in facial expression, variation in lighting condition and variation in poses are given. In this approach faces are represented by labelled graphs. Experimental results of EBGM on ORL, Yale B and FERET datasets are provided. Strong and weak features of EBGM algorithm are discussed.

Exploration on digital marketing as business strategy model among Malaysian entrepreneurs via neurocomputing Hazrita Ab Rahim; Shafaf Ibrahim; Saadi Bin Ahmad Kamaruddin; Nor Azura Md. Ghani; Ismail Musirin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Artificial Intelligence is great when it comes to routine activities and vast amounts of data are analyzed. This can be done more quickly and efficiently than men. In the world of digital marketing, Artificial Intelligence is quickly coming into play. With Artificial Intelligence joining the digital marketing environment, predicting user behavior, search cycles, and much more will be easier. This can support websites that are highly user-friendly for organizations. Moreover, with the aid of Artificial Intelligence, content creation has become a faster and easier task for brands. Practically, a company's degree of enterprise marketing can have an effect on its overall business efficiency. Entrepreneurial marketing is driven by entrepreneurial opportunities which involves the proactive identification and exploitation of opportunities for acquiring and retaining profitable customers through Digital approaches to risk management, resource leveraging and value creation. This research was done by collecting data using semi structure questionnaire distributed to 169 start up owners in Klang Valley area. Using two-layer 6-3-1 with hyperbolic tangent-purelin configurations neural network model, it was found that proactiveness, risk taking, resource leveraging, opportunity focus, intensity and value add are the significant factors towards digital marketing respectively. It is expected that the findings would give some inputs to the Malaysian entrepreneurs on innovative digital marketing in their businesses, regardless the sizes.

Web-Based Yorùbá Numeral Translation System Abayomi Olusola Agbeyangi; Safiriyu Eludiora; Popoola O. A
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

Yorùbá numerals have been seen as one of the most interesting but quite complicated numeral system. In this paper we present the development of a web-based English to Yorùbá numeral translation system. The system translates English numbers both in figure and text to its standard Yorùbá form. The computational processes underlying both numerals were used to formulate the model for the work. Unified Modeling language (UML) and Automata theory was used for the system design and specification. The designed system was implemented using Google Web App Engine with support for python. The result of the system evaluation using mean opinion score approach shows that the system gives a recall of 100% on all the output considered.

Convolutional neural networks for leaf image-based plant disease classification Sachin B. Jadhav; Vishwanath R. Udupi; Sanjay B. Patil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold crossvalidation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4%, 96.4%, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy

Fuzzy Logic Controller for Cascaded H-Bridge Multilevel Inverter N. Sivakumar; A. Sumathi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

This paper proposes fuzzy logic controller based seven-level hybrid inverter for photovoltaic systems with sinusoidal pulse width-modulation (SPWM) techniques. Multi-Level Inverter technology have been developed in the area of high-power medium-voltage energy scheme, because of their advantages such as devices of high dv/dt rating, higher switching frequency, unlimited power processing, shape of output waveform and desired level of output voltage, current and frequency adjustment.This topology can be used there by enabling the scheme to reduce the Total Harmonic Distortion (THD) for high voltage applications. The Maximum Power Point Tracking algorithm is also used for extracting maximum power from the PV array connected to each DC link voltage level. The Maximum Power Point Tracking algorithm is solved by Perturb and Observer method.It has high performance with low Total Harmonic Distortion and reduced by this control strategy. The proposed system has verified and THD is obtained by using MATLAB/simulink.The result is compared with the hardware prototype working model.

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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

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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