Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

IAES International Journal of Artificial Intelligence (IJ-AI)

IJAI
Website

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

9 10 11 12 13

Prediction of Daily Network Traffic based on Radial Basis Function Neural Network Haviluddin Haviluddin; Imam Tahyudin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 2014
Publisher : Institute of Advanced Engineering and Science

This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21 – 24 June 2013 (192 samples series data) in ICT Unit Universitas Mulawarman, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of percentage error (MPE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic.

Forecasting financial budget time series: ARIMA random walk vs LSTM neural network Maryem Rhanoui; Siham Yousfi; Mounia Mikram; Hajar Merizak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Financial time series are volatile, non-stationary and non-linear data that are affected by external economic factors. There is several performant predictive approaches such as univariate ARIMA model and more recently Recurrent Neural Network. The accurate forecasting of budget data is a strategic and challenging task for an optimal management of resources, it requires the use of the most accurate model. We propose a predictive approach that uses and compares the Machine Learning ARIMA model and Deep Learning Recurrent LSTM model. The application and the comparative analysis show that the LSTM model outperforms the ARIMA model, mainly thanks to the LSTMs ability to learn non-linear relationship from data.

Natural Immune System Response As Complexe Adaptive System Using Learning Fuzzy Cognitive Maps Ahmed Tlili; Salim Chikhi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

In the Natural Immune Systems NIS, adaptive and emergent behaviors result from the behaviors of each cell and their interactions with other cells and environment. Modeling and Simulating NIS requires aggregating these cognitive interactions between the individual cells and the environment. In last years the Fuzzy Cognitive Maps (FCM) has been shown to be a convenient tool for modeling, controlling and simulating complex systems. In this paper, a new type of learning fuzzy cognitive maps (LFCM) have been proposed as an extension of traditional FCM for modeling complex adaptive system is described. Our approach is summarized in two major ideas: The first one is to increase the reinforcement learning capabilities of the FCM by using an adaptation of Q-learning technique and the second one is to foster diversity of concept's states within the FCM by adopting an IF-THEN rule based system. Through modeling and simulating response of natural immune system, we show the effectiveness of the proposed approach in modeling CASs.

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.

Mitigation of Voltage Fluctuations using fuzzy-based D-STATCOM in High Level Penetration of DG Systems M.Padma Lalitha; R.Madhan Mohan; B.Murali Mohan Babu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 2: June 2015
Publisher : Institute of Advanced Engineering and Science

Voltage fluctuations mainly resulting from variable output power of renewable energy sources; these are strictly challenging power quality in distribution-generation systems. The paper presents a control method for fuzzy based D-STATCOM to relieve variation of positive-sequence and negative-sequence voltages. D-STATCOM continuously operates as fundamental positive–sequence admittance and negative-sequence conductance to restore the positive-sequence voltage to the nominal value and negative-sequence voltage to the allowable level. At transient period both admittance and conductance are dynamically tuned to improve the voltage regulation performance. The ability of fuzzy logic to handle rough and unpredictable real world data made it suitable for a wide variety of applications, especially, when the models are too complex to be analyzed by classical methods. This paper presents the computer simulation of fuzzy based D-STATCOM under steady and transient state condition. The reduction of total harmonic distortions (THD) and voltage imbalance factor %VUF is discussed at all buses and maintained in acceptable level.

Improved Sensorless Direct Torque Control of Induction Motor Using Fuzzy Logic and Neural Network Based Duty Ratio Controller Sudheer H; Kodad SF; Sarvesh B
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

This paper presents improvements in Direct Torque control of an induction motor using Fuzzy logic with Fuzzy logic and neural network based duty ratio controller. The conventional DTC (CDTC) of induction motor suffers from major drawbacks like high torque and flux ripples and poor transient response. Torque and flux ripples are reduced by replacing hysteresis controller and switching table with Fuzzy logic switching controller (FDTC). In FDTC the selected switching vector is applied for the complete switching time period. The FDTC steady state performance can be improved by using duty ratio controller, the selected switching vector is applied only for the time determined by the duty ratio (δ) and for the remaining time period zero switching vector is applied. The selection of duty ratio using Fuzzy logic and neural networks is projected in this paper. The effectiveness proposed methods are evaluated using simulation by Matlab/Simulink.

Performance Analysis of ANN Model for Estimation of Trophic Status Index of Lakes Tushar Anthwal; Akanksha Chandola; M P Thapliyal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

The health of water bodies across the globe is of high concern as the pollution is accelerating rigorously. With the interventions of simple technology, some significant changes could be bought up. Lakes are dying because of high Trophic Index Status which shows the eutrophication level of water bodies. Taking this into account, feed forward back propagation neural network model is used to estimate the Trophic Status Index (TSI) of lakes which could compute the value of TSI with the given parameters; pH, temperature, dissolved oxygen, Secchi disk transparency, chlorophyll and total phosphate. Two learning algorithms; Levenberg Marquardt (LM) and Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi Newton were used to train the network, which belongs to different classes. The results were analyzed using mean square error function and further checked for the deviation from actual data. Among both the training algorithm; LM demonstrated better performance with 0.0007 average mean square error for best validation performance and BFGS Quasi Newton shows the average mean square error of 1.07.

Classifying the EEG Signal through Stimulus of Motor Movement Using New Type of Wavelet Endro Yulianto; Adhi Susanto; Thomas Sri Widodo; Samekto Wibowo
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 (125.142 KB)

Brain Computer Interface (BCI) refers to a system designed to translate the brain signal in controlling a computer application. The most widely used brain signal is electroencephalograph (EEG) for using the non-invasive method, and having a quite good resolution and relatively affordable equipments. This research purposively is to obtain the characteristics of EEG signals using the motor movement of “turn right” and “turn left” that is by moving the simulation of steering wheel. The characteristic of signal obtained is subsequently used as a reference to create a new type of wavelet for classification. The signal processing, including a 4 – 20 Hz bandpass filter, signal segmentation in 1 to 2 seconds after stimuli and signal correlation, is used to obtain the characteristic of EEG signal; namely Event–Related Synchronization /Desynchronization (ERS/ERD). The result of test data classification to two new types of wavelet shows that each volunteer has a higher correlation value towards the new type of wavelet that has been designed with various wavelet scales for each individuals.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.843

Solving N-Queens Problem Using Subproblems based on Genetic Algorithm Ismail. A. Humied
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Nowadays, permutation problems with large state spaces and the path to solution is irrelevant such as N-Queens problem has the same general property for many important applications such as integrated-circuit design, factory-floor layout, job-shop scheduling, automatic programming, telecommunications network optimization, vehicle routing, and portfolio management. Therefore, methods which are able to find a solution are very important. Genetic algorithm (GA) is one the most well-known methods for solving N-Queens problem and applicable to a wide range of permutation problems. In the absence of specialized solution for a particular problem, genetic algorithm would be efficient. But holism and random choices cause problem for genetic algorithm in searching large state spaces. So, the efficiency of this algorithm would be demoted when the size of state space of the problem grows exponentially. In this paper, the subproblems used based on genetic algorithm to cover this weakness. This proposed method is trying to provide partial view for genetic algorithm by locally searching the state space. This method works to take shorter steps toward the solution. To find the first solution and other solutions in N-Queens problem using proposed method: dividing N-Queens problem into subproblems, which configuring initial population of genetic algorithm. The proposed method is evaluated and compares it with two similar methods that indicate the amount of performance improvement.

Designing Intelligent Variable Structure Controller for HIV Infection Reza Ghasemi
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 (250.993 KB)

Fuzzy adaptive controller is developed for HIV infection in which functions of the system are unknown. A non-affine nonlinear system is considered for the HIV infection dynamic model. The merits of the proposed method is as the stability of the closed-loop system (HIV + Controller), the convergence of the infected cells concentration rates to zero and the boundedness of the internal signal and infected cell concentration. The simulation results show the promising performance of the proposed method.DOI: http://dx.doi.org/10.11591/ij-ai.v2i2.2024

Page 11 of 173 | Total Record : 1722

9 10 11 12 13

Filter by Year

2012 2025

Filter By Issues

All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue

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

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