IAES International Journal of Artificial Intelligence (IJ-AI)
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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<![CDATA[The role of chatterbots in enhancing tourism: a case study of Penang tourism spots ]]>
<![CDATA[Vinothini Kasinathan]]>;
<![CDATA[Aida Mustapha]]>;
<![CDATA[Mohamad Firdaus Che Abdul Rani]]>;
<![CDATA[Salama A. Mostafa]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp569-575
<![CDATA[Chatterbots have been widely used as a tool for conversational booking assistance mainly for hotels such as the Expedia. This paper extends the use of chatterbot beyond booking by presenting the proof of concept of a chatterbot expert system called the VIZARD. The proposed VIZARD is developed using an expert system shell called verbot. The core of Vertbot 5 is the natural language processing (NLP) engine based on pattern matching. The core Verbot 5 engine is responsible for finding matches to a given user input string and firing the appropriate rule. The findings from the user acceptance test concluded that majority of the respondents agreed that the VIZARD expert system stands at an unbiased state while being more aligned on supporting the usefulness of the system.]]>
<![CDATA[Contrastive analysis of rice grain classification techniques: multi-class support vector machine vs artificial neural network ]]>
<![CDATA[Shafaf Ibrahim]]>;
<![CDATA[Saadi Bin Ahmad Kamaruddin]]>;
<![CDATA[Azlee Zabidi]]>;
<![CDATA[Nor Azura Md. Ghani]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp616-622
<![CDATA[Rice is a staple food for 80% of the population in Southeast Asia. Thus, the quality control and classification of rice grain are crucial for more productive and sustainable production. This paper examines the contrastive analysis of rice grain classification performance between multi-class support vector machine (SVM) and artificial neural network (ANN). The analysis has been tested on three types of rice grain images which are Ponni, Basmati, and Brown rice. A digital image transformation analysis based on shape and color features was developed to classify the three types of rice grain. The performance of the proposed study is evaluated to 90 testing images of each rice variation. The ANN is observed to return higher classification accuracy at 93.34% using Level Sweep image transformation technique. Based on the results, it signifies that the ANN performs better classification than the multiclass SVM.]]>
<![CDATA[A modified correlation in principal component analysis for torrential rainfall patterns identification ]]>
<![CDATA[Shazlyn Milleana Shaharudin]]>;
<![CDATA[Norhaiza Ahmad]]>;
<![CDATA[Siti Mariana Che Mat Nor]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp655-661
<![CDATA[This paper presents a modified correlation in principal component analysis (PCA) for selection number of clusters in identifying rainfall patterns. The approach of a clustering as guided by PCA is extensively employed in data with high dimension especially in identifying the spatial distribution patterns of daily torrential rainfall. Typically, a common method of identifying rainfall patterns for climatological investigation employed T mode-based Pearson correlation matrix to extract the relative variance retained. However, the data of rainfall in Peninsular Malaysia involved skewed observations in the direction of higher values with pure tendencies of values that are positive. Therefore, using Pearson correlation which was basing on PCA on rainfall set of data has the potentioal to influence the partitions of cluster as well as producing exceptionally clusters that are eneven in a space with high dimension. For current research, to resolve the unbalanced clusters challenge regarding the patterns of rainfall caused by the skewed character of the data, a robust dimension reduction method in PCA was employed. Thus, it led to the introduction of a robust measure in PCA with Tukey’s biweight correlation to downweigh observations along with the optimal breakdown point to obtain PCA’s quantity of components. Outcomes of this study displayed a highly substantial progress for the robust PCA, contrasting with the PCA-based Pearson correlation in respects to the average amount of acquired clusters and indicated 70% variance cumulative percentage at the breakdown point of 0.4.]]>
<![CDATA[A real-time drowsiness and fatigue recognition using support vector machine ]]>
<![CDATA[Nur Nabilah Abu Mangshor]]>;
<![CDATA[Iylia Ashiqin Abdul Majid]]>;
<![CDATA[Shafaf Ibrahim]]>;
<![CDATA[Nurbaity Sabri]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp584-590
<![CDATA[A drowsiness and fatigue problems among the drivers are the main factor that contributes to road accidents. These problems are vital to be resolved as they could contribute to damage of road facilities, vehicles and most importantly the loss of lives. In avoiding these matters, a proper mechanism is needed to alert the driver to stay awake throughout the driving journey. Thus, this study proposed a real-time prototype for recognizing the drowsiness and fatigue face expression of the driver. The methodology of this study involves facial features detection using Viola-Jones algorithm to detect the exact position of both left and right eyes and mouth. Next, based on the detected eyes and mouth beforehand, the segmentation processes performed on both eyes and mouth using Sobel edge detection to obtain facial regions. The feature extraction phase is conducted using shape-based feature to obtain the extraction values. Support vector machine (SVM) classifier is deployed for the recognition task. A total of 100 images are used during the testing stages. This study achieved a competetive result of 90.00% of accuracy. Yet, hybridization or integration of more image processing techniques will be performed in the future to improve the current accuracy obtained.]]>
<![CDATA[Optimal distributed decision in wireless sensor network using gray wolf optimization ]]>
<![CDATA[Ibrahim Ahmed Saleh]]>;
<![CDATA[Omar Ibrahim Alsaif]]>;
<![CDATA[Maan A. Yahya]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp646-654
<![CDATA[The distributed object decision (DOD) was applied to choose a single solution for problem among many complexes solutions. Most of DOD systems depend on traditional technique like small form factor optical (SFFO) method and scalable and oriented fast-based local features (SOFF) method. These two methods were statistically complex and depended to an initial value. In this paper proposed new optimal technical called gray wolf optimization (GWO) which is used to determine threshold of sensor decision rules from fusion center. The new algorithm gave better performance for fusion rule than numerical results. The results are providing to demonstrate of fusion system reduced of bayes risk by a high rate of 15%-20%. This algorithm also does not depend on the initial values and shows the degree of complexity is better than other algorithms.]]>
<![CDATA[Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images ]]>
<![CDATA[Stefanus Kieu Tao Hwa]]>;
<![CDATA[Abdullah Bade]]>;
<![CDATA[Mohd Hanafi Ahmad Hijazi]]>;
<![CDATA[Mohammad Saffree Jeffree]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp713-720
<![CDATA[Tuberculosis (TB) is a disease that causes death if not treated early. Ensemble deep learning can aid early TB detection. Previous work trained the ensemble classifiers on images with similar features only. An ensemble requires a diversity of errors to perform well, which is achieved using either different classification techniques or feature sets. This paper focuses on the latter, where TB detection using deep learning and contrast-enhanced canny edge detected (CEED-Canny) x-ray images is presented. The CEED-Canny was utilized to produce edge detected images of the lung x-ray. Two types of features were generated; the first was extracted from the Enhanced x-ray images, while the second from the Edge detected images. The proposed variation of features increased the diversity of errors of the base classifiers and improved the TB detection. The proposed ensemble method produced a comparable accuracy of 93.59%, sensitivity of 92.31% and specificity of 94.87% with previous work.]]>
<![CDATA[The selection of the relevant association rules using the ELECTRE method with multiple criteria ]]>
<![CDATA[Azzeddine Dahbi]]>;
<![CDATA[siham jabri]]>;
<![CDATA[youssef balouki]]>;
<![CDATA[Taoufiq Gadi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp638-645
<![CDATA[The extraction of association rules is a very attractive data mining task and the most widespread in the business world and in modern society, trying to obtain the interesting relationship and connection between collections of articles, products or items in high transactional databases. The immense quantity of association rules obtained expresses the main obstacle that a decision maker can handle. Consequently, in order to establish the most interesting association rules, several interestingness measures have been introduced. Currently, there is no optimal measure that can be chosen to judge the selected association rules. To avoid this problem we suggest to apply ELECTRE method one of the multi-criteria decision making, taking into consideration a formal study of measures of interest according to structural properties, and intending to find a good compromise and select the most interesting association rules without eliminating any measures. Experiments conducted on reference data sets show a significant improvement in the performance of the proposed strategy.]]>
<![CDATA[A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems ]]>
<![CDATA[Abdulrahman Jassam Mohammed]]>;
<![CDATA[Muhanad Hameed Arif]]>;
<![CDATA[Ali Adil Ali]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp609-615
<![CDATA[Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.]]>
<![CDATA[Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning ]]>
<![CDATA[Aliyu Muhammad Abdu]]>;
<![CDATA[Musa Mohd Muhammad Mokji]]>;
<![CDATA[Usman Ullah Ullah Sheikh]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp670-683
<![CDATA[Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as "shallow" machine learning architectures. The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis. Regardless, there is a lack of studies concerning its application in plant leaves disease detection. Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security]]>
<![CDATA[Prevalence of hypertension: predictive analytics review ]]>
<![CDATA[Nur Arifah Mohd Nor]]>;
<![CDATA[Azlinah Mohamed]]>;
<![CDATA[Sofianita Mutalib]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i4.pp576-583
<![CDATA[Hypertension is one of the non-communicable disease (NCD) that is classify as a global health risk with many critical health cases. Malaysia raise the same concern of the increasing NCD health problem. This paper aims to study the techniques used in predictive analytics namely healthcare and identify the factors of prevalence on hypertension. This review would give a better understanding of proper techniques and suggest the technique commonly used in predictive analytics especially for medical data and at the same time provide significant factors of prevalence hypertension. A total of 27 papers reviewed, several techniques on predictive analytics in healthcare are neural network, decision tree, naïve bayes, regression and support vector machine. The rise of economic growth and correlated socio-demographic have cause rise in hypertension problem over past years. The factors of hypertension depicted in this review namely gender, age, locality, family history, physically inactive and unhealthy life style not conform to any boundaries thus far. Thus, the choice on the technique and hypertension factors for predictive analytics is significant to come out with the significant predictive model. The predictive model on prevalence of hypertension may predict the severity of adult having hypertension in future work.]]>
