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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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Comparison of Neural Network Training Algorithms for Classification of Heart Diseases Hesam Karim; Sharareh R. Niakan; Reza Safdari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Heart disease is the first cause of death in different countries. Artificial neural network (ANN) technique can be used to predict or classification patients getting a heart disease. There are different training algorithms for ANN. We compared eight neural network training algorithms for classification of heart disease data from UCI repository containing 303 samples. Performance measures of each algorithm containing the speed of training, the number of epochs, accuracy, and mean square error (MSE) were obtained and analyzed. Our results showed that training time for gradient descent algorithms was longer than other training algorithms (8-10 seconds). In contrast, Quasi-Newton algorithms were faster than others (<=0 second). MSE for all algorithms was between 0.117 and 0.228. While there was a significant association between training algorithms and training time (p<0.05), the number of neurons in hidden layer had not any significant effect on the MSE and/or accuracy of the models (p>0.05). Based on our findings, for development an ANN classification model for heart diseases, it is best to use Quasi-Newton training algorithms because of the best speed and accuracy.

Black Holes Algorithm: A Swarm Algorithm inspired of Black Holes for Optimization Problems Mostafa Nemati; Reza Salimi; Navid Bazrkar
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 (350.577 KB)

In this paper a swarms algorithms, for optimization problem is proposed. This algorithm is inspired of black holes. A black hole is a region of space-time whose gravitational field is so strong that nothing which enters it, not even light, can escape. Every black hole has mass, and charge. In this Algorithm we suppose each solution of problem as a black hole and use of gravity force for global search and electrical force for local search. The proposed method is verified using several benchmark problems commonly used in the area of optimization. The experimental results on different benchmarks indicate that the performance of the proposed algorithm is better than PSO (Particle Swarms Optimization), AFS (Artifitial Fish Swarm Algorithm) and RBH-PSO (random black hole particle swarm optimization Algorithm).DOI: http://dx.doi.org/10.11591/ij-ai.v2i3.3226

Comparison between SVM and KNN classifiers for iris recognition using a new unsupervised neural approach in segmentation Hicham Ohmaid; S. Eddarouich; A. Bourouhou; M. Timouya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Un système biométrique d'identification et d'authentification permet la reconnaissance automatique d'un individu en fonction de certaines caractéristiques ou caractéristiques uniques qu'il possède. La reconnaissance de l'iris est une méthode d'identification biométrique qui applique la reconnaissance des formes aux images de l'iris. En raison des motifs épigénétiques uniques de l'iris, la reconnaissance de l'iris est considérée comme l'une des méthodes les plus précises dans le domaine de l'identification biométrique. L'algorithme de segmentation proposé dans cet article commence par déterminer les régions de l'œil à l'aide d'une approche neuronale non supervisée, après que le contour de l'œil a été trouvé à l'aide du bord de Canny, la transformation de Hough est utilisée pour déterminer le centre et le rayon de la pupille et de l'iris. . Ensuite, la normalisation permet de transformer la région de l'iris circulaire segmenté en une forme rectangulaire de taille fixe en utilisant le modèle de feuille de caoutchouc de Daugman. Une transformation en ondelettes discrètes (DWT) est appliquée à l'iris normalisé pour réduire la taille des modèles d'iris et améliorer la précision du classificateur. Enfin, la base de données URIBIS iris est utilisée pour la vérification individuelle de l'utilisateur en utilisant le classificateur KNN ou la machine à vecteur de support (SVM) qui, sur la base de l'analyse du code de l'iris lors de l'extraction des caractéristiques, est discutée.

Application of artificial neural network to predict amount of carried weight of cargo train in rail transportation system Siti Nasuha Zubir; S. Sarifah Radiah Shariff; Siti Meriam Zahari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Derailments of cargo have frequently occurred in Malaysian train services during the last decade. Many factors contribute to this incident, especially its total amount of carried weight. It is found that severe derailments cause damage to both lives and properties every year. If the amount of carried weight of cargo train could be accurately forecasted in advance, then its detrimental effect could be greatly minimized. This paper presents the application of Artificial Neural Network (ANN) to predict the amount of carried weight of cargo train, with KTMB used as the study case. As there are many types of cargo being carried by KTMB, this study focuses only on cement that being carried in twelve (12) different routes. In this study, Artificial Neural Network (ANN) has been incorporated for developing a predictive model with three (3) different training algorithms, Levenberg-Marquardt (LM), Quick Propagation (QP) and Conjugate Gradient Descent (CGD). The best training algorithm is selected to predict the amount of carried weight by comparing the error measures of all the training algorithm which are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The obtained results indicated that the ANN technique is suitable for predicting the amount of carried weight.

Age-based facial recognition using convoluted neural network deep learning algorithm Julius Yong Wu Jien; Aslina Baharum; Shaliza Hayati A. Wahab; Nordin Saad; Muhammad Omar; Noorsidi Aizuddin Mat Noor
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Face recognition is the use of biometric innovations that can see or validate a person by seeing and investigating designs depending on the shape of the individual. Face recognition is used largely for the purpose of well-being, despite the fact that passion for different areas of use is growing. Overall, face recognition innovations are worth considering because they have the potential for broad legal jurisdiction and different business applications. It is widely used in many spaces. How it works is a product of facial recognition processing facial geometry. The hole between the ear and the good way from the front to the jaw are the main variables. This code distinguishes the highlight of the face that is important for your facial separation and creates your facial expression. Therefore, this study gives an overview of age detection using a different combination of machine learning and image processing methods on the image dataset.

OPF for large scale power system using ant lion optimization: a case study of the Algerian electrical network Ramzi Kouadri; Ismail Musirin; Linda Slimani; Tarek Bouktir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents a study of the optimal power flow (OPF) for a large scale power system. A metaheuristic search method based on the Ant Lion Optimizer (ALO) algorithm is presented and has been confirmed in the real and larger scale Algerian 114-bus system for the OPF problem with and without static VAR compensator (SVC) devices. To get the highest impact of SVC devices in terms of improving the voltage profile, minimize the total generation cost and reduction of active power losses, the ALO algorithm was applied to determine the optimal allocation of SVC devices. The results obtained by the ALO method were compared with other methods in the literature such as DE, GA-ED-PS, QP, and MOALO, to see the efficiency of the proposed method. The proposed method has been tested on the Algerian 114-bus system with objective functions is the minimization of total generation cost (TGC) with two different vectors of variables control.

Different mutation and crossover set of genetic programming in an automated machine learning Suraya Masrom; Masurah Mohamad; Shahirah Mohamed Hatim; Norhayati Baharun; Nasiroh Omar; Abdullah Sani Abd. Rahman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement. One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. This paper presents the effect of different pairs of mutation and crossover rates on the automated machine learning performances that tested on different types of datasets. The finding can be used to support the theory that higher crossover rates used to improve the algorithm accuracy score while lower crossover rates may cause the algorithm to converge at earlier stage.

Intelligent cluster connectionist recommender system using implicit graph friendship algorithm for social networks Arnold Adimabua Ojugo; Debby Oghenevwede Otakore
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Implicit clusters are formed as a result of the many interactions between users and their contacts. Online social platforms today provide special link-types that allows effective communication. Thus, many users can hardly categorize their contacts into groups such as “family”, “friends” etc. However, such contact clusters are easily represented via implicit graphs. This has arisen the need to analyze users’ implicit social graph and enable the automatic add/delete of contacts from/to a group via a suggestion algorithm – making the group creation process dynamic (instead of static, where users are manually added or removed). The study implements the friend suggest algorithm, which analyzes a user’s implicit social graph to create custom contact group using an interaction-based metric to estimate a user’s affinity to his contacts and groups. The algorithm starts with a small seed set of contacts – already categorized by a user as friends/groups; And, then suggest other contacts to be added to a group. The result inherent demonstrates the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.

Machine learning building price prediction with green building determinant Thuraiya Mohd; Syafiqah Jamil; Suraya Masrom
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

In the era of Industrial 4.0, many urgent issues in the industries can be effectively solved with artificial intelligence techniques, including machine learning. Designing an effective machine learning model for prediction and classification problems is an ongoing endeavor. Besides that, time and expertise are important factors that are needed to tailor the model to a specific issue, such as the green building housing issue. Green building is known as a potential approach to increase the efficiency of the building. To the best of our knowledge, there is still no implementation of machine learning model on GB valuation factors for building price prediction compared to conventional building development. This paper provides a report of an empirical study that model building price prediction based on green building and other common determinants. The experiments used five common machine learning algorithms namely Linear Regression, Decision Tree, Random Forest, Ridge and Lasso tested on a set of real building datasets that covered Kuala Lumpur District, Malaysia. The result showed that the Random Forest algorithm outperforms the other four algorithms on the tested dataset and the green building determinant has contributed some promising effects to the model.

Vietnamese handwritten character recognition using convolutional neural network Truong Quang Vinh; Le Hoai Duy; Nguyen Thanh Nhan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Handwriting recognition is one of the core applications of computer vision for real-word problems and it has been gaining more interest because of the progression in this field. This paper presents an efficient model for Vietnamese handwriting character recognition by Convolutional Neural Networks (CNNs) – a kind of deep neural network model can achieve high performance on hard recognition tasks. The proposed architecture of the CNN network for Vietnamese handwriting character recognition consists of five hidden layers in which the first 3 layers are convolutional layers and the last 2 layers are fully-connected layers. Overfitting problem is also minimized by using dropout techniques with the reasonable drop rate. The experimental results show that our model achieves approximately 97% accuracy.

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Home Page

OAI Link

Editorial Team

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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

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

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Abstract

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Abstract

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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