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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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Application of Multi Layer Artificial Neural Network in the Diagnosis System: A Systematic Review Arvind Singh Rawat; Arti Rana; Adesh Kumar; Ashish Bagwari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Basic hardware comprehension of an artificial neural network (ANN), to a major scale depends on the proficientrealization of a distinctneuron. For hardware execution of NNs, mostly FPGA-designed reconfigurable computing systems are favorable .FPGA comprehension of ANNs through a hugeamount of neurons is mainlyan exigentassignment. This workconverses the reviews on various research articles of neural networks whose concernsfocused in execution of more than one input neuron and multilayer with or without linearity property by using FPGA. An execution technique through reserve substitution isprojected to adjust signed decimal facts. A detailed review of many research papers have been done for the proposed work.

A Fuzzy Model for Ni-Cd Batteries Mohammad Sarvi; Masoud Safari
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 (405.532 KB)

The batteries models found in the literature are based mainly on mathematical descriptions of physical, chemical, and electrochemical properties which are difficult to determine. This paper presents new fuzzy based model for Nickel Cadmium (Ni-Cd) batteries. The main advantage of the proposed models is that, the proposed model is able to predict battery output voltage without knowledge of numerous factors. Inputs of the proposed model are battery current and state of charge while battery voltage is selected as the output. To check the accuracy of the proposed models, simulations results are compared with the measured battery data at different charge current as well as many other battery models for a 7Ah, size F, Ni-Cd battery. Simulated shows good agreements with measured data. The advantage of fuzzy model is that for modeling by fuzzy method experimental data isn’t needed. The proposed models can apply for modeling of other batteries types.DOI: http://dx.doi.org/10.11591/ij-ai.v2i2.1784

Using Black Holes Algorithm in Discrete Space by Nearest Integer Function Mostafa Nemati; Navid Bazrkar; Reza Salimi; Behdad Moshref
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 (426.344 KB)

In this paper we Using Black Holes Algorithm in Discrete Space by Nearest Integer Function. Black holes algorithm is a Swarm Algorithm inspired of Black Holes for Optimization Problems. We suppose each solution of problem as an integer black hole and after calculating the gravity and electrical forces use Nearest Integer Function. The experimental results on different benchmarks show that the performance of the proposed algorithm is better than PSO (Binary Particle Swarms Optimization), and GA (Genetic Algorithm).DOI: http://dx.doi.org/10.11591/ij-ai.v2i4.4319

Overlapped music segmentation using a new effective feature and random forests Duraid Y. Mohammed; Khamis A. Al-Karawi; Philip Duncan; Francis F. Li
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v8.i2.pp181-189

In the field of audio classification, audio signals may be broadly divided into three classes: speech, music and events. Most studies, however, neglect that real audio soundtracks can have any combination of these classes simultaneously. This can result in information loss, thus compromising the knowledge discovery. In this study, a novel feature, “Entrocy”, is proposed for the detection of music in both pure form and overlapping with the other audio classes. Entrocy is defined as the variation of the information (or entropy) in an audio segment over time. Segments, which contain music, were found to have lower Entrocy since there are fewer abrupt changes over time. We have also compared Entrocy with existing music detection features and the entrocy showing a good performance.

Effect of Feature Selection on Small and Large Document Summarization Dipti Yashodhan Sakhare; Rajkumar Rajkumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 3: September 2014
Publisher : Institute of Advanced Engineering and Science

As the amount of textual Information increases, we experience a need for Automatic Text Summarizers. In Automatic summarization a text document or a larger corpus of multiple documents are reduced to a short set of words or paragraph that conveys the main meaning of the text Summarization can be classified into two approaches: extraction and abstraction. This paper focuses on extraction approach.The goal of text summarization based on extraction approach is sentences selection. The first step in summarization by extraction is the identification of important features. In our approach short stories and biographies are used as test documents. Each document is prepared by pre-processing process: sentence segmentation, tokenization, stop word removal, case folding, lemmatization, and stemming. Then, using important features, sentence filtering, data compression and finally calculating score for each sentence is done. In this paper we proposed various features of Summary Extraction and also analyzed features that are to be applied depending upon the size of the Document. The experimentation is performed with the DUC 2002 dataset. The comparative results of the proposed approach and that of MS-Word are also presented here. The concept based features are given more weightage. From these results we propose that use of the concept based features helps in improving the quality of the summary in case of large documents.

Expert judgment Z-Numbers as a ranking indicator for hierarchical fuzzy logic system Shaiful Bakhtiar bin Rodzman; Normaly Kamal Ismail; Nurazzah Abd Rahman; Syed Ahmad Aljunid; Zulhilmi Mohamed Nor; Ku Muhammad Naim Ku Khalif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

In this article, the researchers main contribution is to investigate three factors which may correlate in implementation of Expert Judgment Z-Numbers as new Fuzzy Logic Ranking Indicator such as: expert relevance judgment or score, the expert confidence and the level of expertise. The Expert Judgment Z-Numbers then will be an input to the Hierarchical Fuzzy Logic System of Domain Specific Text Retrieval, along with other indicators such as Ontology BM25 Score, Fabrication Rate, Shia Rate and Positive Rate of hadith document. The results showed, the proposed system, with the additional new indicator of Expert Judgment Z-Numbers, may improve the original BM25 ranking function, by yielding better results on 26 queries, on all evaluation metrics that are measured in this research such as P@10, %no measures and MAP, and has achieved better results in 28 queries on P@10 alone, compared to the BM25 original score, that only yield better results in 2 queries on all evaluation metrics, and also yield better results in 4 queries on the MAP alone. The results proved that the proposed system has a capability to utilize the expert confidence and their relevant judgment that are represented in Z-Number, as an indicator to optimize the existing ranking function system and has a potential for a further research to be conducted on these domains. For the future works, the researchers would like to enhance this research by including a variety of expert’s level confidence and their judgment, also a new calculation to represent the value of Z-Numbers.

An SVD based Real Coded Genetic Algorithm for Graph Clustering Parthajit Roy; Jyotsna Kumar Mandal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 2: June 2016
Publisher : Institute of Advanced Engineering and Science

This paper proposes a novel graph clustering model based on genetic algorithm using a random point bipartite graph. The model uses random points distributed uniformly in the data space and the measurement of distance from these points to the test points have been considered as proximity. Random points and test points create an adjacency matrix. To create a similarity matrix, correlation coefficients are computed from the given bipartite graph. The eigenvectors of the singular value decomposition of the weighted similarity matrix are considered and the same are passed to an elitist GA model for identifying the cluster centers. The model has been tasted with the standard datasets and the performance has been compared with existing standard algorithms.

Artificial neural network forecasting performance with missing value imputations Nur Haizum Abd Rahman; Muhammad Hisyam Lee
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents time series forecasting method in order to achieve high accuracy performance. In this study, the modern time series approach with the presence of missing values problem is developed. The artificial neural networks (ANNs) is used to forecast the future values with the missing value imputations methods used known as average, normal ratio and also the modified method. The results are validated by using mean absolute error (MAE) and root mean square error (RMSE). The result shown that by considering the right method in missing values problems can improved artificial neural network forecast accuracy. It is proven in both MAE and RMSE measurements as forecast improved from 8.75 to 4.56 and from 10.57 to 5.85 respectively. Thus, this study suggests by understanding the problem in time series data can produce accurate forecast and the correct decision making can be produced.

Improvement of Power Quality for Microgrid using Fuzzy Based UPQC Controller Abdul Rasheed; G. Keshava Rao
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 2: June 2015
Publisher : Institute of Advanced Engineering and Science

Generally, the power systems are mainly effected by the continuous changes in operational requirement and increasing amount of distributed energy systems. This paper proposes a new concept of power-control strategies for a micro grid generation system for better transfer of power. The micro grids are obtained with the general renewable energy sources and this concept provides the maximum utilization of power at environmental free conditions with low losses; then the system efficiency is also improved. This paper proposes a single stage converter based micro grid to reduce the number of converters in an individual ac or dc grid. The proposed micro grid concept can work in both stand-alone mode and also in grid interfaced mode. The distortions that occur in power system due to changes in load or because of usage of non-linear loads, can be eliminated by using control strategies designed for shunt active hybrid filters such as series and shunt converters. A conventional Proportional Integral (PI) and Fuzzy Logic Controllers are used for power quality enhancement by reducing the distortions in the output power. The simulation results are compared among the two control strategies, that fuzzy logic controller and pi controller.

Fault detection for air conditioning system using machine learning Noor Asyikin Sulaiman; Md Pauzi Abdullah; Hayati Abdullah; Muhammad Noorazlan Shah Zainudin; Azdiana Md Yusop
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Air conditioning system is a complex system and consumes the most energy in a building. Any fault in the system operation such as cooling tower fan faulty, compressor failure, damper stuck, etc. could lead to energy wastage and reduction in the system’s coefficient of performance (COP). Due to the complexity of the air conditioning system, detecting those faults is hard as it requires exhaustive inspections. This paper consists of two parts; i) to investigate the impact of different faults related to the air conditioning system on COP and ii) to analyse the performances of machine learning algorithms to classify those faults. Three supervised learning classifier models were developed, which were deep learning, support vector machine (SVM) and multi-layer perceptron (MLP). The performances of each classifier were investigated in terms of six different classes of faults. Results showed that different faults give different negative impacts on the COP. Also, the three supervised learning classifier models able to classify all faults for more than 94%, and MLP produced the highest accuracy and precision among all.

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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
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Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

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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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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