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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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Computational Morphological Analysis of Yorùbá Language Words Safiriyu Ijiyemi Eludiora; O R Ayemonisan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Nigeria official languages are English, Yorùbá, Igbo and Hausa. The focus of the study reported in this paper is to develop learning tool that can assist learners to learn the Yorùbá language using its alphabets. The study is critical to Yorùbá language, because of its endangerment. There is need to introduce different learning tools that can mitigate its extinction. A Yorùbá word perfect system was developed to assist people in learning the Yorùbá language. English and Yorùbá words formation are experimented using computational morphological approach (word formation). The theoretical framework considered Finite state automata (FSA) to realise different ways of combining the consonants and vowels to form word. Two to five letter words were considered. The system was designed and implemented using UML tools and python programming language.The system will teach the users on how the words are formed, and the number of syllables in each word. The user need not to know how to tone mark word before he/she can use the system. Any word typed will be analysed according to its number of syllables. This approach produces representatives of all parts of speech (POS) of the two languages. It produces corpora for the two languages

Independent Task Scheduling in Grid Computing Based on Queen Bee Algorithm Zahra Pooranian; Mohammad Shojafar; Bahman Javadi
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 (619.519 KB)

The inherent dynamicity in grid computing has made it extremely difficult to come up with near-optimal solutions to efficiently schedule tasks in grids. Task Scheduling plays crucial role in Grid computing. It is a challengeable issue among scientists to achieve better results especially in makespan based on various AI methods. Nowadays, non deterministic algorithms provide better results for these tasks. In this study the task scheduling problem in Grid computing environments has been addressed. In this paper, Queen Bee Algorithm is used for resolving scheduling problem and the obtained results are compared with several Meta–heuristic Algorithms which are developed to solve the problem. As it illustrated, queen bee algorithm is declined considerably makespan and execution time parameters rather than others in different states.DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.1229

Challenging Issues in Automated Oil Palm Fruit Grading Gaurang S Patkar; Anjaneyulu G.S.G.N; Chandra Mouli P.V.S.S.R
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Late advancement in Agriculture segment utilizing Image preparing and fuzzy logic methods has empowered ranchers to expand the yield of harvest and served the nourishment needs of the whole people. Look into in horticulture is pointed towards increment in the profitability, quality and lessening the likelihood of blunder presented by people. The biggest oil palm creation is in Malaysia and Indonesia and they send out palm oil to different nations on the planet. The most outrageous enthusiasm for palm oil is in India. This came to fruition India into Palm Oil advancement and era in various states . With a specific end goal to expand the efficiency of palm oil organic products, palm oil industry and in addition analysts utilizes different machine-vision systems to review the natural products. Tragically, the information caught and prepared is confronted with restricted learning and accuracy. There are a few difficulties required with the outline and usage of palm oil organic product reviewing. This paper introduces an outline of different Image handling and fuzzy logic methods, distinguishes and addresses testing issues in computerized palm natural product evaluating.

A Data Mining Approach for the Detection of Denial of Service Attack Hoda Waguih
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 (259.597 KB)

Denial of Service (DoS) attacks constitutes one of the major threats and among the hardest security problems currently facing computer networks and particularly the Internet. A DoS attack can easily exhausts the computing and communication resources of its victim within a short period of time. Because of the seriousness of the problem many defense mechanisms have been proposed to fight these attacks. In this paper, we propose an approach that detects DoS attacks using data mining classification techniques. The approach is based on classifying “normal” traffic against “abnormal” traffic in the sense of DoS attacks. The paper investigates and evaluates the performance of J48 decision tree algorithm for the detection of DoS attacks and compares it with two rule based algorithms, namely OneR and Decision table. The selected algorithms were tested with benchmark 1998 DARPA Intrusion Detection data. Our research results show that both Decision tree and rule based classifiers deliver highly accurate results – greater than 99% accuracy – and exhibit high level of overall performance.DOI: http://dx.doi.org/10.11591/ij-ai.v2i2.1937

Towards Coalition in a Multi-Agent Based Simulation for The Bomber Problem Boutheina Jlifi; Zina Elguedria; Khaled Ghedira
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 (904.722 KB)

The Bomber Problem BP can be considered as a discrete time model in which a bomber must survive for t epochs before reaching the target where it will drop its bombs. The Bomber problem is unsolved despite his appearance date since the 1960s. It is classified in the heading of research problems unsolved by Richard Weber. In fact, it can be classified as an NP-hard combinatorial optimization problem. Multi-agent simulation is for a long time privileged for modeling and experimentation of complex systems. This term includes concepts as diverse as strategic decision support or staff training. In this paper, we explore the challenge of simulating a system as complex as the Bomber problem with a MAS approach. Particularly, we demonstrate that Coalition forming in a MAS, models and simulates the collective resolution of the Bomber Problem within a dynamic agent organization in an efficient way. We illustrate our discussion with developed simulation results. DOI: http://dx.doi.org/10.11591/ij-ai.v2i4.2434

Intelligent swarm-based optimization technique for oscillatory stability assessment in power system N. A. M. Kamari; I. Musirin; A. A. Ibrahim; S. A. Halim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

This paper discussed the prediction of oscillatory stability condition of the power system using a particle swarm optimization (PSO) technique. Indicators namely synchronizing (Ks) and damping (Kd) torque coefficients is appointed to justify the angle stability condition in a multi-machine system. PSO is proposed and implemented to accelerate the determination of angle stability. The proposed algorithm has been confirmed to be more accurate with lower computation time compared with evolutionary programming (EP) technique. This result also supported with other indicators such as eigenvalues determination, damping ratio and least squares method. As a result, proposed technique is achievable to determine the oscillatory stability problems.

Fingerprint Classification Using Fuzzy-neural Network and Other Methods Idriss Tazight; Mohamed Fakir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

The fingerprints are unique to each individual; they can be used as a means to distinguish one individual from another.Therefore they are used to identify a person. Fingerprint Classification is done to associate a given fingerprint to one of the existing classes, such as left loop, right loop, arch, tented arch and whorl. Classifying fingerprint images is a very complex pattern recognition problem, due to properties of intra-class diversitiesand inter-class similarities. Its objective is to reduce the responsetime and reducing the search space in an automatic identificationsystem fingerprint (AIS), in classifying fingerprints. In these papers we present a system of fingerprint classificationbased on singular characteristics for extracting feature vectorsand neural networks and fuzzy neural networks, SVM and Knearest neighbour for classifying.

Performance analysis of supervised learning models for product title classification Norsyela Muhammad Noor Mathivanan; Nor Azura Md. Ghani; Roziah Mohd Janor
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Online business development through e-commerce platforms is a phenomenon which change the world of promoting and selling products in this 21st century. Product title classification is an important task in assisting retailers and sellers to list a product in a suitable category. Product title classification is a part of text classification problem but the properties of product title are different from general document. This study aims to evaluate the performance of five different supervised learning models on data sets consist of e-commerce product titles with a very short description and they are incomplete sentences. The supervised learning models involve in the study are Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM) and Random Forest. The results show KNN model is the best model with the highest accuracy and fastest computation time to classify the data used in the study. Hence, KNN model is a good approach in classifying e-commerce products.

Development of an Efficient Face Recognition System Based on Linear and Nonlinear Algorithms Araoluwa Simileolu Filani; Adebayo Olusola Adetunmbi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 2: June 2016
Publisher : Institute of Advanced Engineering and Science

This paper presents appearance based methods for face recognition using linear and nonlinear techniques. The linear algorithms used are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The two nonlinear methods used are the Kernel Principal Components Analysis (KPCA) and Kernel Fisher Analysis (KFA). The linear dimensional reduction projection methods encode pattern information based on second order dependencies. The nonlinear methods are used to handle relationships among three or more pixels. In the final stage, Mahalinobis Cosine (MAHCOS) metric is used to define the similarity measure between two images after they have passed through the corresponding dimensional reduction techniques. The experiment showed that LDA and KFA have the highest performance of 93.33 % from the CMC and ROC results when used with Gabor wavelets. The overall result using 400 images of AT&T database showed that the performance of the linear and nonlinear algorithms can be affected by the number of classes of the images, preprocessing of images, and the number of face images of the test sets used for recognition.

Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images Stefanus Kieu Tao Hwa; Mohd Hanafi Ahmad Hijazi; Abdullah Bade; Razali Yaakob; Mohammad Saffree Jeffree
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Tuberculosis (TB) is a disease caused by Mycobacterium Tuberculosis. Detection of TB at an early stage reduces mortality. Early stage TB is usually diagnosed using chest x-ray inspection. Since TB and lung cancer mimic each other, it is a challenge for the radiologist to avoid misdiagnosis. This paper presents an ensemble deep learning for TB detection using chest x-ray and Canny edge detected images. This method introduces a new type of feature for the TB detection classifiers, thereby increasing the diversity of errors of the base classifiers. The first set of features were extracted from the original x-ray images, while the second set of features were extracted from the edge detected image. To evaluate the proposed approach, two publicly available datasets were used. The results show that the proposed ensemble method produced the best accuracy of 89.77%, sensitivity of 90.91% and specificity of 88.64%. This indicates that using different types of features extracted from different types of images can improve the detection rate.

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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

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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