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 16 Documents

1 2

Search results for , issue "Vol 8, No 4: December 2019" : 16 Documents clear

Intelligent optimization and management system for renewable energy systems using multi-agent Chahinaze Ameur; Sanaa Faquir; Ali Yahyaouy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Hybrid energy systems (HES) using renewable energy sources are an interesting solution for power stand-alone systems. However, the energy management of such systems is very complex. This paper presents a Multi Agent System (MAS) framework applied to manage the flow of energy in a hybrid stand-alone system. The proposed system consists of photovoltaic panels and a wind turbine along with batteries as storage units. The proposed MAS architecture composed of different agents (photovoltaic agent, wind turbine agent, supervisor agent, load controller agent, and storage agent) was developed to manage the flow of energy between the energy resources and the storage units for an isolated house. The agent-approach for HES is explained and the proposed MAS is presented and a simulation model is developed in the java agent development environment (JADE). The system was tested with empty batteries and full batteries and results showed that the system could satisfy the load demand while maintaining the level of the batteries between 30% (minimum discharging rate) and 80% (maximum charging rate).

Improving software development effort estimation using support vector regression and feature selection Abdelali Zakrani; Mustapha Hain; Ali Idri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Accurate and reliable software development effort estimation (SDEE) is one of the main concerns for project managers. Planning and scheduling a software project using an inaccurate estimate may cause severe risks to the software project under development such as delayed delivery, poor quality software, missing features. Therefore, an accurate prediction of the software effort plays an important role in the minimization of these risks that can lead to the project failure. Nowadays, the application of artificial intelligence techniques has grown dramatically for predicting software effort. The researchers found that these techniques are suitable tools for accurate prediction. In this study, an improved model is designed for estimating software effort using support vector regression (SVR) and two feature selection (FS) methods. Prior to building model step, a preprocessing stage is performed by random forest or Boruta feature selection methods to remove unimportant features. Next, the SVR model is tuned by a grid search approach. The performance of the models is then evaluated over eight wellknown datasets through 30%holdout validation method. To show the impact of feature selection on the accuracy of SVR models, the proposed model was compared with SVR model without feature selection. The results indicated that SVR with feature selection outperforms SVR without FS in terms of the three accuracy measures used in this empirical study.

Intelligent credit scoring system using knowledge management Bazzi Mehdi; Chamlal Hasna; El Kharroubi Ahmed; Ouaderhman Tayeb
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Promoting entrepreneurship among Moroccan young people has been challenged by a plethora of economic and social problems in the aftermath of the Arab Spring. Several government programs have been set up for young entrepreneurs. Thus, faced with the large number of credit applications solicited by these young entrepreneurs, banks resorted to artificial intelligence techniques. In this respect, this article aims at proposing a decision-making system enabling the bank to automate its credit granting process. It is a tool that allows the bank, in the first instance, to select promising projects through a scoring approach adapted to this segment of entrepreneurs. In the second step, the tool allows the setting of the maximum credit amount to be allocated to the selected project. Finally, based on the knowledge of the bank’s experts, the tool proposes a breakdown of the amount granted by the bank into several products adapted to the needs of the entrepreneur.

Predictive analytics of university student intake using supervised methods Muhammad Yunus Iqbal Basheer; Sofianita Mutalib; Nurzeatul Hamimah Abdul Hamid; Shuzlina Abdul-Rahman; Ariff Md Ab Malik
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Predictive analytics extract important factors and patterns from historical data to predict future outcomes. This paper presents predictive analytics of university student intake using supervised methods. Every year, universities face a lot of academic offer rejection by the applicants. Hence, this research aims to predict student acceptance and rejection towards academic offer given by a university using supervised methods subject to past student intake data. To solve this problem, a lot of past studies had been reviewed starting from nineties era till now. From the analysis, two algorithms had been selected namely Decision Tree and k Nearest Neighbor. The dataset of past student intake was obtained with fifteen attributes, which are applicants’ gender, applicants studied stream during Sijil Peperiksaan Malaysia (SPM), university campuses, applicants’ hometown, disability, campus visit, course choice order in application form, applicant’s six SPM subjects result, orphan and status of acceptance. Several experiments were implemented to find the best model to predict the student’s offer acceptance by evaluating the model accuracy. Both models yield best accuracy at 66 percent with the selected attributes. This research gives a huge impact in selecting which applicants is suitable to be offered as well as adapting the university’s academic offering process in much intelligence way in the future.

Towards a semantic web of things framework Nadim Ismai; El Ghayam Yassine; Abdelalim Sadiq
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Information and communication teFchnologies (ICT) know a significant development especially in terms of hardware miniaturization, cost reduction and energy consumption optimization. This advancement enables the interconnection of a large number of physical objects namely using the Internet, forming what is called the Internet of Things (IoT). The IoT provides the opportunity to interact with these objects through sensors, actuators and smart applications which may help users in several areas such as transport, logistics, health care, agriculture, etc. However, building the IoT requires a strong interoperability between thousands of heterogeneous devices and services. In this context, the SWoT (Semantic Web of Things) uses semantic Web technologies to enrich these devices and services with semantic annotations which enables the semantic interoperability. However, the development of SWOT-based systems on a large scale faces many challenges especially due to the large number of devices and services, their geographical distribution as well as their mobility. These challenges-which may affect the system performance as a whole-require innovative industry and research efforts. The current paper proposes a SWoT framework architecture that take into account the main SWoT challenges.

Hybridisation of RF(Xgb) to improve the tree-based algorithms in learning style prediction Haziqah Shamsudin; Maziani Sabudin; Umi Kalsom Yusof
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

This paper presents hybridization of Random Forest (RF) and Extreme Gradient Boosting (Xgb), named RF(Xgb) to improve the tree-based algorithms in learning style prediction. Learning style of specific users in an online learning system is determined based on their interaction and behavior towards the system. The most common online learning theory used in determining the learning style is the Felder-Silverman’s Learning Style Model (FSLSM). Many researchers have proposed machine learning algorithms to establish learning style by using the log file attributes. This helps in determining the learning style automatically. However, current researches still perform poorly, where the range of accuracy is between 58%-89%. Hence, RF(Xgb) is proposed to help in improving the learning style prediction. This hybrid algorithm was further enhanced by optimizing its parameters. From the experiments, RF(Xgb) was proven to be more effective, with accuracy of 96% compared to J48 and LSID-ANN algorithm from previous literature.

Page 2 of 2 | Total Record : 16

1 2

Filter by Year

2019 2019

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

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

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