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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.

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Articles 15 Documents

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Search results for , issue "Vol 8, No 3: September 2019" : 15 Documents clear

Intelligent risk management framework Wissam Abbass; Zineb Bakraouy; Amine Baina; Mostafa Bellafkih
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

The Internet of Things (IoT) is rapidly increasing and enhancing today’s world by introducing a large set of interconnected devices. Several beneficial services are produced by these devices as for area monitoring and process control. However, IoT security is still a major problem. In fact, IoT’ security beggings largely whith an effective Risk Management process. However, the essense of this process is to acquire a risk inventory cibling the IoT devices. Nevertheless, it is quite difficult to obtaining this latter which significantly adds complication issues to the Risk Management. Without the ability of holisticly identify the IoT critical devices, inaccurate Risk Management is achieved which leads unfortunately to novel risk exposures. Traditional Riskbased approaches fails drastically at apprending IoT’ potential attacks. The dynamic structure, the heteregouns nature of devices, the various security objectives and infrastructure pervasiveness are key factors impacting the overall perfomance. Thus, a holistic Risk Management witihin the IoT is indispensable. Accordingly, we propose an intelligent Risk Management framework using Mobile Agents in order to deliver preventive and responsive assessment.

Performance analysis of supervised learning models for product title classification Norsyela Muhammad Noor Mathivanan; Nor Azura Md. Ghani; Roziah Mohd Janor
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Online business development through e-commerce platforms is a phenomenon which change the world of promoting and selling products in this 21st century. Product title classification is an important task in assisting retailers and sellers to list a product in a suitable category. Product title classification is a part of text classification problem but the properties of product title are different from general document. This study aims to evaluate the performance of five different supervised learning models on data sets consist of e-commerce product titles with a very short description and they are incomplete sentences. The supervised learning models involve in the study are Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM) and Random Forest. The results show KNN model is the best model with the highest accuracy and fastest computation time to classify the data used in the study. Hence, KNN model is a good approach in classifying e-commerce products.

A multiple mitosis genetic algorithm K. Kamil; K. H Chong; H. Hashim; S. A. Shaaya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum. This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.

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.

An efficient method to improve the clustering performance using hybrid robust principal component analysis-spectral biclustering in rainfall patterns identification Shazlyn Milleana Shaharudin; Shuhaida Ismail; Siti Mariana Che Mat Nor; Norhaiza Ahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

In this study, hybrid RPCA-spectral biclustering model is proposed in identifying the Peninsular Malaysia rainfall pattern. This model is a combination between Robust Principal Component Analysis (RPCA) and biclustering in order to overcome the skewness problem that existed in the Peninsular Malaysia rainfall data. The ability of Robust PCA is more resilient to outlier given that it assesses every observation and downweights the ones which deviate from the data center compared to classical PCA. Meanwhile, two way-clustering able to simultaneously cluster along two variables and exhibit a high correlation compared to one-way cluster analysis. The experimental results showed that the best cumulative percentage of variation in between 65%-70% for both Robust and classical PCA. Meanwhile, the number of clusters has improved from six disjointed cluster in Robust PCA-kMeans to eight disjointed cluster for the proposed model. Further analysis shows that the proposed model has smaller variation with the values of 0.0034 compared to 0.030 in Robust PCAkMeans model. Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation of the clustering result.

Extracting hidden patterns from dates' product data using a machine learning technique Mohammed Abdullah Al-Hagery
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Mining in data is an important step for knowledge discovery, which leads to extract new patterns from datasets. It is a widespread methodology that has the capability to help ministries, companies, and experts for diving into the data to find important insights and patterns to help them take suitable decisions. The farmers and marketers of the date product in the production regions lack to discover the most important characteristics of dates types from the economically, healthy, and the type of consumers point of view to achieve the highest profits by choosing the best types and the most consumed. The research objective is to extract interesting patterns from the dates’ product dataset, using Machine Learning, based on association rules generation. This, in turn, will support the farmers, and marketers to discover new features related to the production, consumption, and marketing processes. This research used a real dataset collected from KSA, Qassim region, which is the first region of cultivation of palm, that produces the best types of dates in the Arab region. The data preprocessed and analyzed by the Apriori algorithm. The results show important features and insights related to the health benefits of dates, production, its consumption, consumers types, and marketing. Consequently, these results can be employed, for instance, to encourage individuals to consume dates for their nutritional value and their important health benefits. Furthermore, the results encourage producers to focus on the production of preferable types and to improve the marketing policies of the other types.

Review of single clustering methods Nurshazwani Muhamad Mahfuz; Marina Yusoff; Zakiah Ahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Clustering provides a prime important role as an unsupervised learning method in data analytics to assist many real-world problems such as image segmentation, object recognition or information retrieval. It is often an issue of difficulty for traditional clustering technique due to non-optimal result exist because of the presence of outliers and noise data. This review paper provides a review of single clustering methods that were applied in various domains. The aim is to see the potential suitable applications and aspect of improvement of the methods. Three categories of single clustering methods were suggested, and it would be beneficial to the researcher to see the clustering aspects as well as to determine the requirement for clustering method for an employment based on the state of the art of the previous research findings.

Multi-verse optimization based evolutionary programming technique for power scheduling in loss minimization scheme Muhamad Hazim Lokman; Ismail Musirin; Saiful Izwan Suliman; Hadi Suyono; Rini Nur Hasanah; Sharifah Azma Syed Mustafa; Mohamed Zellagui
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

The growth of computational intelligence technology has witnessed its application in numerous fields. Power system study is not left behind as far as computational intelligence trend is concerned. In power system community, optimization process is one of the crucial efforts for most remedial action to maintain the power system security. Basically, power scheduling refers to prior to fact action (such as scheduling generators to generate certain powers for next week). Power scheduling process is one of the most important routines in power systems. Scheduling of generators in a power transmission system is an important scheme; especially its offline studies to identify the security status of the system. This determines the cost effectiveness in power system planning. This paper investigates the performance of multi-verse based evolutionary programming (lowest EP) technique in the application of power system scheduling to ensure loss is gained by the system. Losses in the system can be controlled through this implementation which can be realized through the validation on a chosen reliability test system as the main model. Validation on IEEE 30-Bus Reliability Test System resulted that both techniques are reliable and robust in addressing this issue.

Polarity classification tool for sentiment analysis in Malay language Normi Sham Awang Abu Bakar; Ros Aziehan Rahmat; Umar Faruq Othman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

The popularity of the social media channels has increased the interest among researchers in the sentiment analysis (SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool (MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.

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.

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Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

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Journal Mail Official
ijai@iaesjournal.com

Editorial Address
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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

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

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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