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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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Predicting fatalities among shark attacks: comparison of classifiers Lim Mei Shi; Aida Mustapha; Yana Mazwin Mohmad Hassim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

This paper presents the comparisons of different classifiers on predicting Shark attack fatalities. In this study, we are comparing two classifiers which are Support vector machines (SVMs) and Bayes Point Machines (BPMs) on Shark attacks dataset. The comparison of the classifiers were based on the accuracy, recall, precision and F1-score as the performance measurement. The results obtained from this study showed that BPMs predicted the fatality of shack attack victim experiment with higher accuracy and precision than the SVMs because BPMs have “average” identifier which can minimize the probabilistic error measure. From this experiment, it is concluded that BPMs are more suitable in predicting fatality of shark attack victim as BPMs is an improvement of SVMs.

Quantile Regression Neural Networks Based Prediction of Drug Activities Mohammed E. El-Telbany
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 2014
Publisher : Institute of Advanced Engineering and Science

QSAR (quantitative structure-activity relationship) modeling is one of the well developed areas in drug development through computational chemistry. Similar molecules with just a slight variation in their structure can have quit different biological activity. This kind of relationship between molecular structure and change in biological activity is center of focus for QSAR Modeling. Predictions of property and/or activity of interest have the potential to save time, money and minimize the use of expensive experimental designs, such as, for example, animal testing. Intelligent machine learning techniques are important tools for QSAR analysis, as a result, they are integrated into the drug production process. The effective learnable model can reduce the cost of drug design significantly. The quantile estimation via neural network structure technique introduced in this paper is used to predict activity of pyrimidines based on the structure–activity relationship of these compounds which assist for finding potential treatment agents for serious disease. In comparison with statistical quantile regression, the qrnn significantly reduce the prediction error.

Intelligent optimization and management system for renewable energy systems using multi-agent Chahinaze Ameur; Sanaa Faquir; Ali Yahyaouy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Hybrid energy systems (HES) using renewable energy sources are an interesting solution for power stand-alone systems. However, the energy management of such systems is very complex. This paper presents a Multi Agent System (MAS) framework applied to manage the flow of energy in a hybrid stand-alone system. The proposed system consists of photovoltaic panels and a wind turbine along with batteries as storage units. The proposed MAS architecture composed of different agents (photovoltaic agent, wind turbine agent, supervisor agent, load controller agent, and storage agent) was developed to manage the flow of energy between the energy resources and the storage units for an isolated house. The agent-approach for HES is explained and the proposed MAS is presented and a simulation model is developed in the java agent development environment (JADE). The system was tested with empty batteries and full batteries and results showed that the system could satisfy the load demand while maintaining the level of the batteries between 30% (minimum discharging rate) and 80% (maximum charging rate).

Anomalies Detection Based on the ROC Analysis using Classifiers in Tactical Cognitive Radio Systems: A survey Ahmed Moumena
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Receiver operating characteristic (ROC) curve is an important technique for organizing classifiers and visualizing their performance in tactical systems in the presence of jamming signal. ROC curves are commonly used to evaluate the performance of classifiers for anomalies detection. This paper gives a survey of ROC analysis based on the anomaly detection using classifiers for using them in research. In recent years have been increasingly adopted in the machine learning and data mining research communities. This survey gives definitions of the anomaly detection theory and how to use one ROC curve, what a ROC curve, when we use ROC curves.

Improving software development effort estimation using support vector regression and feature selection Abdelali Zakrani; Mustapha Hain; Ali Idri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Accurate and reliable software development effort estimation (SDEE) is one of the main concerns for project managers. Planning and scheduling a software project using an inaccurate estimate may cause severe risks to the software project under development such as delayed delivery, poor quality software, missing features. Therefore, an accurate prediction of the software effort plays an important role in the minimization of these risks that can lead to the project failure. Nowadays, the application of artificial intelligence techniques has grown dramatically for predicting software effort. The researchers found that these techniques are suitable tools for accurate prediction. In this study, an improved model is designed for estimating software effort using support vector regression (SVR) and two feature selection (FS) methods. Prior to building model step, a preprocessing stage is performed by random forest or Boruta feature selection methods to remove unimportant features. Next, the SVR model is tuned by a grid search approach. The performance of the models is then evaluated over eight wellknown datasets through 30%holdout validation method. To show the impact of feature selection on the accuracy of SVR models, the proposed model was compared with SVR model without feature selection. The results indicated that SVR with feature selection outperforms SVR without FS in terms of the three accuracy measures used in this empirical study.

The Optimal Thresholding Technique for Image Segmentaion Using Fuzzy Otsu Method P. Rambabu; C. Naga Raju
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

Image Segmentation plays a very important role in image processing. The single-mindedness of image segmentation is to partition the image into a set of disconnected regions with the homogeneous and uniform attributes like intensity, tone, color and texture. There are various methods for image segmentation but no method is suitable for low contrast images. In this paper, we are presenting an efficient and optimal thresholding image segmentation technique that can be used to separate the object and background pixels of the image to improve the quality of low contrast images. This innovative method consists of two steps. Firstly fuzzy logics are used to find optimum mean value using S-curve with automatic selection of controlled parameters to avoid the fuzziness in the image. Secondly, the fuzzy logic’s optimal threshold value used in Otsu method to improve the contrast of the image. This method, gives better results than traditional Otsu and Fuzzy logic techniques. The graphs and tables of values show that the proposed method is superior to traditional methods.

Incremental Approach of Neural Network in Back Propagation Algorithms for Web Data Mining A. P. Tawdar; M. S. Bewoor; S. H. Patil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Text Classification is also called as Text Categorization (TC), is the task of classifying a set of text documents automatically into different categories from a predefined set. If a text document relates to exactly one of the categories, then it is called as single-label classification task; otherwise, it is called as multi-label classification task. For Information Retrieval (IR) and Machine Learning (ML), TC uses several tools and has received much attention in the last decades. In this paper, first classifies the text documents using MLP based machine learning approach (BPP) and then return the most relevant documents. And also describes a proposed back propagation neural network classifier that performs cross validation for original Neural Network. In order to optimize the classification accuracy, training time. Proposed web content mining methodology in the exploration with the aid of BPP. The main objective of this investigation is web document extraction and utilizing different grouping algorithm. This work extricates the data from the web URL.

Dynamic Particle Swarm Optimization for Multimodal Function H. Omranpour; M. Ebadzadeh; S. Shiry; S. Barzegar
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 (1181.47 KB)

In this paper, a technical approach to particle swarm optimization method is presented. The main idea of the paper is based on local extremum escape. A new definition has been called the worst position. With this definition, convergence and trapping in extremumlocal be prevented and more space will be searched. In many cases of optimization problems, we do not know the range that answer is that.In the results of examine on the benchmark functions have been observed that when initialization is not in the range of the answer, the other known methods are trapped in local extremum. The method presented is capable of running through it and the results have been achieved with higher accuracy.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.367

Direct Torque Control of Doubly Star Induction Motor Using Fuzzy Logic Speed Controller Lallouani Hellali; Saad Belhamdi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

This paper presents the simulation of the control of doubly star induction motor using Direct Torque Control (DTC) based on Proportional and Integral controller (PI) and Fuzzy Logic Controller (FLC). In addition, the work describes a model of doubly star induction motor in α-β reference frame theory and its computer simulation in MATLAB/SIMULINK®. The structure of the DTC has several advantages such as the short sampling time required by the TC schemes makes them suited to a very fast flux and torque controlled drives as well as the simplicity of the control algorithm.the general- purpose induction drives in very wide range using DTC because it is the excellent solution. The performances of the DTC with a PI controller and FLC are tested under differents speeds command values and load torque.

Spatial Information based Image Clustering with A Swarm Approach Ouadfel Salima; Abdelmalik Taleb-Ahmed; Batouche Mohamed
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 (290.249 KB)

Fuzzy c-means algorithm (FCM) is one of the most used clustering methods for image segmentation. However, the conventional FCM algorithm presents some limits like its sensitivity to the noise because it does not take into consideration contextual information and its convergence to local minimum since it is based on a gradient descent method. In this paper, we present a new spatial fuzzy clustering algorithm optimized by the Artificial Bee Colony (ABC) algorithm. ABC-SFCM has two major characteristics. First it tackles better noisy image segmentation by making use of the spatial local information into the membership function. Secondly, it improves the global performance by taking advantages of the global search capability of ABC. Experiments with synthetic and real images show that ABC-SFCM is robust to noise compared to other methods.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.765

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Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

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Journal Mail Official
ijai@iaesjournal.com

Editorial Address
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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