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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 20 Documents

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Search results for , issue "Vol 9, No 1: March 2020" : 20 Documents clear

Iris segmentation using a new unsupervised neural approach Hicham Ohmaid; S. Eddarouich; A. Bourouhou; M. Timouyas
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristic possessed by an individual. Iris recognition is a biometric identification method that uses pattern recognition on the images of the iris. Owing to the unique epigenetic patterns of the iris, Iris recognition is considered as one of the most accurate methods in the field of biometric identification. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction the iris. The segmentation algorithm proposed in this article starts with determining the regions of the eye using unsupervised neural approach, after the outline of the eye is found using the Canny edge, The Hough Transform is employed to determine the center and radius of the pupil and the iris.

Modeling of submerged membrane filtration processes using recurrent artificial neural networks Zakariah Yusof; Norhaliza Abdul Wahab; Syahira Ibrahim; Shafishuhaza Sahlan; Mashitah Che Razali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

The modeling of membrane filtration processes is a challenging task because it involves many interactions from both biological and physical operational behavior. Membrane fouling behaviour in filtration processes is complex and hard to understand, and to derive a robust model is almost not possible. Therefore, it is the aim of this paper to study the potential of time series neural network based dynamic model for a submerged membrane filtration process. The developed model that represent the dynamic behavior of filtration process is later used in control design of the membrane filtration processes. In order to obtain the dynamic behaviour of permeate flux and transmembrane pressure (TMP), a random step was applied to the suction pump. A recurrent neural network (RNN) structure was employed to perform as the dynamic models of a filtration process, based on nonlinear auto-regressive with exogenous input (NARX) model structure. These models are compared with the linear auto-regressive with exogenous input (ARX) model. The performance of the models were evaluated in terms of %R2, mean square error (MSE,) and a mean absolute deviation (MAD). For filtration control performance, a proportional integral derivative (PID) controller was implemented. The results showed that the RNN-NARX structure able to model the dynamic behavior of the filtration process under normal conditions in short range of the filtration process. The developed model can also be a reliable assistant for two different control strategies development in filtration processes.

Supervised evolutionary programming based technique for multi-DG installation in distribution system Muhammad Firdaus Shaari; Ismail Musirin; Muhamad Faliq Mohamad Nazer; Shahrizal Jelani; Farah Adilah Jamaludin; Mohd Helmi Mansor; A.V.Senthil Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Installing DG in network system, has supported the distribution system to provide the increasing number of consumer demand and load, in order to achieve that this paper presents an efficient and fast converging optimization technique based on a modification of traditional evolutionary programming method for obtain the finest optimal location and power loss in distribution systems. The proposed algorithm that is supervised evolutionary programming is implemented in MATLAB and apply on the 69-bus feeder system in order to minimize the system power loss and obtaining the best optimal location of the distributed generators.

Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm Nur Alisa Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Autism Spectrum Disorder (ASD) is a neurodevelopmental that impact the social interaction and communication skills. Diagnosis of ASD is one of the difficult problems facing researchers. This research work aimed to reveal the different pattern between autistic and normal children via electroencephalogram (EEG) by using the deep learning algorithm. The brain signal database used pattern recognition where the extracted features will undergo the multilayer perceptron network for the classification process. The promising method to perform the classification is through a deep learning algorithm, which is currently a well-known and superior method in the pattern recognition field. The performance measure for the classification would be the accuracy. The higher percentage means the more effectiveness for the ASD diagnosis. This can be seen as the ground work for applying a new algorithm for further development diagnosis of autism to see how the treatment is working as well in future.

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.

Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0 M. Aldiki Febriantono; Sholeh Hadi Pramono; Rahmadwati Rahmadwati; Golshah Naghdy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

The multiclass imbalanced data problems in data mining were an interesting to study currently. The problems had an influence on the classification process in machine learning processes. Some cases showed that minority class in the dataset had an important information value compared to the majority class. When minority class was misclassification, it would affect the accuracy value and classifier performance. In this research, cost sensitive decision tree C5.0 was used to solve multiclass imbalanced data problems. The first stage, making the decision tree model uses the C5.0 algorithm then the cost sensitive learning uses the metacost method to obtain the minimum cost model. The results of testing the C5.0 algorithm had better performance than C4.5 and ID3 algorithms. The percentage of algorithm performance from C5.0, C4.5 and ID3 were 40.91%, 40, 24% and 19.23%.

Development of option c measurement and verification model using hybrid artificial neural network-cross validation technique to quantify saving Wan Nazirah Wan Md Adnan; Nofri Yenita Dahlan; Ismail Musirin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

This paper aims to develop a hybrid artificial neural network for Option C Measurement and Verification model to predict monthly building energy consumption. In this work, baseline energy model development using artificial neural networks embedded with artificial bee colony optimization and cross validation technique for a small dataset were considered. Artificial bee colony optimization with coefficient of correlation fitness function was used in optimizing the neural network training process and selecting the optimal values of initial weights and biases. Working days, class days and cooling degree days were used as input meanwhile monthly electricity consumption as an output of artificial neural network. The results indicated that this hybrid artificial neural network model provided better prediction results compared to the other model. The best model with the highest value of coefficient of correlation was selected as the baseline model hence is used to determine the saving.

Estimation of water quality index using artificial intelligence approaches and multi-linear regression Muhammad Sani Gaya; Sani Isah Abba; Aliyu Muhammad Abdu; Abubakar Ibrahim Tukur; Mubarak Auwal Saleh; Parvaneh Esmaili; Norhaliza Abdul Wahab
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Water quality index is a measure of water quality at a certain location and over a period of time. High value indicates that the water is unsafe for drinking and inadequate in quality to meet the designated uses. Most of the classical models are unreliable producing unpromising forecasting results. This study presents Artificial Intelligence (AI) techniques and a Multi Linear Regression (MLR) as the classical linear model for estimating the Water Quality Index (WQI) of Palla station of Yamuna river, India. Full-scale data of the river were used in validating the models. Performance measures such as Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Determination Coefficient (DC) were utilized in evaluating the accuracy and performance of the models. The obtained result depicted the superiority of AI models over the MLR model. The results also indicated that, the best model of both ANN and ANFIS proved high improvement in performance accuracy over MLR up to 10% in the verification phase. The difference between ANN and ANFIS accuracy is negligible due to a slight increment in performance accuracy indicating that both ANN and ANFIS could serve as reliable models for the estimation of WQI.

Feature selection for DDoS detection using classification machine learning techniques Andi Maslan; Kamaruddin Malik Bin Mohamad; Feresa Binti Mohd Foozy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Computer system security is a factor that needs to be considered in the era of industrial revolution 4.0, namely by preventing various threats to the system, as well as being able to detect and repair any damage that occurs to the computer system. DDoS attacks are a threat to the company at this time because this attack is carried out by making very large requests for a site or website server so that the system becomes stuck and cannot function at all. DDoS attacks in Indonesia and developed countries always increase every year to 6% from only 3%. To minimize the attack, we conducted a study using Machine Learning techniques. The dataset is obtained from the results of DDoS attacks that have been collected by the researchers. From the datasets there is a training and testing of data using five techniques classification: Neural Network, Naïve Bayes and Random Forest, KNN, and Support Vector Machine (SVM), datasets processed have different percentages, with the aim of facilitating in classifying. From this study it can be concluded that from the five classification techniques used, the Forest random classification technique achieved the highest level of accuracy (98.70%) with a Weighted Avg 98.4%. This means that the technique can detect DDoS attacks accurately on the application that will be developed.

DeepOSN: Bringing deep learning as malicious detection scheme in online social network Putra Wanda; Marselina Endah Hiswati; Huang J. Jie
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier 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

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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