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 1,639 Documents

5 6 7 8 9

Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energyefficient data Saadi Bin Ahmad Kamaruddin; Nor Azura Md Ghani; Hazrita Ab Rahim; Ismail Musirin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Green technology building is not newly introduced to the world nor Malaysia, but it is rarely practiced globally and now it has promoted noteworthy due to destructions caused by human hands towards the nature. Now people started to realize that the world is polluted by many hazardous substances. Therefore, Help University came up with the effort of preserving the nature through a new Green Technology campus, which has been fully operated since year 2017. In this research, neural network forecasting models on energy-efficient data of Help University, Subang 2 green technology campus at Subang Bistari, Selangor has been done with respect to value-formoney (VFM) attribute. Previously there were no similar research done on energy-efficient data of Help University, Subang 2 campus. The significant factors with respect to energy or electricity saved (MW/hr) in the year 2017 variable were studied as recorded by Building Automation and Control System (BAS) of Help University Subang 2 campus. Using multiple linear regression (stepwise method), the significant predictor towards energy saved (MW/hr) was Building Energy Index (BEI) (kWh/m2/year) based p-value<α=0.05. A mathematical model was developed. Moreover, the proposed neural network forecasting model using Killer WhaleBackpropagation Algorithm (KWBP) were found to better than existing conventional techniques to forecast BEI data. This research is expected to specifically assist maintenance department of Help University, Subang 2 campus towards load forecasting for power saving planning in years to come.

Expert System for Decision Support Division of Inheritance According to Islamic Law Adi Fitra Andikos; Gunawan Ali; Wulan Andang Purnomo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Develop an expert system as supporting legacy property distribution of decision which based on the Islamic law. This expert system expected can help everyone who need distribution value of legacy property by using distribution method based on the Islamic law. The legacy property value which will be distributed is legacy property after taken by the will if it was. And debt, corpse of administration cost. The distribution result is an percentage value for each heir who have right to get the property legacy after distribution process. Determination of nominal value of legacy property will not be count in this system. The user system can obtain nominal value of distribution property by multiplying the distribution percentage with whole value of legacy property. The result that taken form this expert system is output as information of heir group who has right to the legacy, and percentage value for each heir who has right to get the legacy. The inference method that used in this expert system is Forward Chaining Method. The method that used for system analysis and designing is Data Flow Oriented method by using Data Flow Diagram (DFD) tool. The database design is using Entity-Relationship Diagram (E-R Diagram) relation model.

IQ level prediction and cross-relational analysis with perceptual ability using EEG-based SVM classification model Noor Hidayah Ros Azamin; Mohd Nasir Taib; Aisyah Hartini Jahidin; Dyg Suzana Awang; Megat Syahirul Amin Megat Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

This paper presents IQ level prediction and cross-relational analysis with perceptual ability using EEG-based SVM classification model. The study hypothesized that measure of perceptual ability and intelligence is strongly connected through the brain’s attention regulatory mechanism. Therefore, an intelligent classification model should be able to predict and map IQ levels from a dataset associated with varying levels of perception. 115 samples of resting EEG is acquired from the left prefrontal cortex. Sixty-five is used for perceptual ability analysis via CTMT, while another fifty is used in the development of IQ level classification model using SVM. The mean pattern of theta, alpha and beta bands show positive correlation between perceptual ability and IQ level datasets. Meanwhile, the developed SVM model outperforms the previous ANN method; yielding 100% accuracy for training and testing. Subsequently, the classification model successfully predicts and mapped samples from the perceptual ability dataset to its corresponding IQ levels with 98.5% accuracy. Therefore, validity of the study is confirmed through positive correlation demonstrated by both traits of cognition using the pattern of mean power ratio features, and successful prediction of IQ level for perceptual ability dataset via SVM classification model.

Refined Clustering of Software Components by Using K-Mean and Neural Network Indu Verma; Amarjeet Kaur; Iqbaldeep Kaur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 2: June 2015
Publisher : Institute of Advanced Engineering and Science

Data Mining is extraction of relevant information about data set. A data-warehouse is a location where information is stored. There are various services of data mining, clustering is one of them. Clustering is an effort to group similar data onto single cluster. In this paper we propose and implement k-mean and neural network for clustering same components in single cluster. Clustering reduces the search space by grouping similar test cases together according to the requirements and, hence minimizing the search time, for the retrieval of the test cases, resulting in reduced time complexity. In this research paper we proposed approach for re-usability of test cases by unsupervised approach and supervised approach. In unsupervised learning we proposed k-mean and in supervised learning neural network. We have designed the algorithm for requirement and test case document clustering according to its tf-idf vector space and the output is set of highly cohesive pattern groups.

Fuzzy Information Modeling in a Database System Salam Ismaeel; Ayman Al-Khazraji; Karama Al-delimi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

A Fuzzy logic (FL) provides a remarkably simple way to draw definite conclusions from vague, ambiguous or imprecise information. In a sense, fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions. In this paper a fuzzy information modeling system was developed then used in a database, which contains fuzzy data and real data, to create new information assistance capable of making any decision about this data. The proposed system is implemented on a special database used to evaluation workers or users in any formal organizations.

An improved hybrid feature selection method for huge dimensional datasets F. Rosita Kamala; P. Ranjit Jeba Thangaiah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

High dimensions of data cause overfitting in machine learning models, can lead to reduction in accuracy during classification of instances. Variable selection is the most essential function in predictive analytics, that reduces the dimensionality, without losing an appropriate information by selecting a few significant features of machine learning problems. The major techniques involved in this process are filter and wrapper methodologies. While filters measure the weight of features based on the attribute weighting criterion, the wrapper approach computes the competence of the variable selection algorithms. The wrapper approach is achieved by the selection of feature subgroups by pruning the feature space in its search space. The objective of this paper is to choose the most favourable attribute subset from the novel set of features, by using the combination method that unites the merits of filters and wrappers. To achieve this objective, an Improved Hybrid Feature Selection (IHFS) method is performed to create well-organized learners. The results of this study shows that the IHFS algorithm can build competent business applications, which have got a better precision than that of the constructed which is stated by the previous hybrid variable selection algorithms. Experimentation with UCI (University of California, Irvine) repository datasets affirms that this method have got better prediction performance, more robust to input noise and outliers, balances well with the available features, when performed comparison with the present algorithms in the literature review.

Ontology-based Social Recommender System Abeer Mohamed El-korany; Salma Mokhtar Khatab
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 (481.65 KB)

Knowledge sharing is vital in collaborative work environments.People working in the same environment aid better communication due to sharing information and resources within a contextual knowledge structure constructed based on their scope. Social networks play important role in our daily live as it enables people to communicate, and share information. The main idea of social network is to represent a group of users joined by some kind of voluntary relation without considering any preference. This paper proposes a social recommender system that follows user’s preferences to provide recommendation based on the similarity among users participating in the social network. Ontology is used to define and estimate similarity between users and accordingly being able to connect different stakeholders working in the community field such as social associations and volunteers.This approach is based on integration of major characteristics of content-based and collaborative filtering techniques. Ontology plays a central role in this system since it is used to store and maintain the dynamic profiles of the users which is essential for interaction and connection of appropriate knowledge flow and transaction.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.778

Design Process to Reduce Production Cycle Time in Product Development Mahesh Mallampati; Kolla Srivinivas; Tirumala Krishna. M
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

In today’s business climate, the old adage “time is money” has been expanded to mean that time is competitive weapon. Today customer’s demands are quick delivery and good quality at reasonable price. When entering the global market the companies encounter several difficulties, the most important one being excessive time for new product development. Thus to perform in a global market, short lead times are essential to provide customer satisfaction. Lead time in manufacturer point of view is the time elapse between placing of an order and the receipt of goods ordered. There are various components of lead time such as setup time, process time, move time and waiting time. This paper deals with review of various tools and techniques to reduce lead time. This problem can be solved by transition from sequential engineering to concurrent engineering, A survey of published works in the field of designing teams in big companies has revealed that in big companies a three-level team structure is recommended, as well as a workgroup, consisting of four basic teams. Method study techniques use to examine current way of work and develop effective method base on elimination, combining, changing and simplification of activities. Various lean tools such as Single Minute Exchange of Dies (SMED), 5S, Poka-yoke, Kanban, Just-in-time (JIT), Value Stream Mapping (VSM), Jidoka, Cellular manufacturing etc. helps in reducing lead time. Also Manufacturing Resource Planning (MRPII), Theory of Constraints (TOC) classic approaches of Production Planning and Control (PPC) are use to reduce Work in Process (WIP) and flow time.

Stable intelligent Controller Design for Generalized Flow Shop Systems 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 (410.905 KB)

Designing a stable fuzzy controller for a class of generalized flow shop systems is addressed in this paper based on max-plus algebra. The proposed controller is multi-input single-output. Robustness against uncertainties in the service times, stabilizing the closed loop system and withholding the blocking effect are the main properties of the proposed controller. An illustrative example is given to show the effectiveness of the proposed method.DOI: http://dx.doi.org/10.11591/ij-ai.v2i2.1325

Design Controller for a Class of Nonlinear Pendulum Dynamical System Mohamad Reza Rahimi Khoygani; Reza Ghasemi; Davoud Sanaei
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 (485.563 KB)

Designing proportional integral derivative (PID), Linear Quadratic Regulator (LQR), Fuzzy Logic Controller (FLC) and Self-Tuning Fuzzy PID (STFP) controller is used for nonlinear pendulum dynamic system in this paper.The promising performance of the proposed controllers investigates in simulation. The effectiveness, robustness against noise and the comparison of the controller methods for Nonlinear Pendulum Dynamical System are delivered in this paper.DOI: http://dx.doi.org/10.11591/ij-ai.v2i4.4164

Page 7 of 164 | Total Record : 1639

5 6 7 8 9

Filter by Year

2012 2025

Filter By Issues

All Issue 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