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IAES International Journal of Artificial Intelligence (IJ-AI)
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 25 Documents
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Search results for , issue "Vol 9, No 4: December 2020" : 25 Documents clear
Robust nonlinear PD controller for ship steering autopilot system based on particle swarm optimization technique Nihad M. Ameen; Abdulrahim Thiab Humod
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp662-669

Abstract

This paper proposes a new approach for robust nonlinear proportional derivative (PD) controller. In this approach a nonlinear function (sigmoid) is added to the conventional proportional integral derivative (PID) controller with filtering for the derivative, in order to improve system response and to reduce the effects of the nonlinearity and uncertainty due to variations of hydrodynamic coefficients of ship with the speed. The gains of nonlinear PD controller are tuned by applying particle swarm optimization (PSO) technique. The simulated results by MATLAB program give satisfactory performance with regard to maximum overshoot, settling time and zero steady state error for step, ramp and proposed trajectory as input to the system. The robustness of the autopilot was checked by changing the plant parameters and adding disturbance to the plant input. The used autopilot is nonlinear PD controller because the gain of integral term by PSO is approximately zero which simplifies the controller construction. The results show that the proposed controller has superior transient response and robustness on the conventional PID designed by using symmetrical optimum criterion with pole assignment technique.
Classification of white rice grain quality using ANN: a review Anis Sufiya Hamzah; Azlinah Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp600-608

Abstract

Exploring the new method of using technology for classifying rice grain quality is pertinent for rice producers in order to provide quality grains and protect consumers from any contamination exist. This is even more important when in today’s market we can see that rice with low quality is traded without stringent quality control which at the end will affect consumer’s health. This paper will review classification methods in determining quality white rice grain. Although there are many researchers developing new process to do rice classification by using different technique, there are still more advanced technique that can be used to do classification. This paper will focus on classifying rice grain quality using artificial neural network (ANN) approach with the help of image processing to identify the impurities contained in the rice grains. The findings show ANN using BPNN has the highest accuracy of 96%, it is also noted that other methods provide equally better performance. This review indicate hybrid method in ANN should be explored next for future work.
Optimization of detection of a single line to ground fault based on ABCNN algorithm Feryal Ibrahim Jabbar; DurMuhammad Soomro; Adnan Hasan Tawafan; Mohd Noor bin Abdullah; Nur Hanis binti Mohammad Radzi; Mazhar Hussain Baloch
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp623-629

Abstract

One of the most faults found in the electrical distribution network is a single line to ground fault (SLGF). It can be detected and rectified through many methods. The utilization of Peterson coil (PC), reduces the electrical arcs and make the distribution network safe from damage in contrast to the cost value. This paper focuses on the method for its detection on higher and lower values of the ground fault current (GFC). Moreover, it will identify the capacitance and earth leakage of earthling network lines as well as calculate the opposing inductance to compensate for the cause. It also presents the selfextinguishing of GFC by controlling PC through one of the novel optimization techniques called adaptive and artificial bee colony with network neural (ABCNN) to improve the algorithm's performance, like optimization efficiency, speed, solution, and iteration. As a result, the determination of the GFC equals the sound phase current. Also, the extinguishing of an electric arc results in a short time compared with classical methods. The significant advantage of this research is the increment in the system's reliability, protection of devices as well as saving in copper cost. MATLAB was used to carry out this research. For the validity, the proposed algorithm results were compared with the classical method by creating faults on separate phases also.
Classification of vehicles’ types using histogram oriented gradients: comparative study and modification Rania Salah El-Sayed; Mohamed Nour El-Sayed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp700-712

Abstract

This paper proposes an efficient model for recognizing and classifying a vehicle type. The model localizes each object in the image then identifies the vehicle type. The features of an image are extracted using the histogram oriented gradients (HOG) and ant colony optimization (ACO). A vehicle type is determined using different classifiers namely: the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and Softmax classifiers. The model is implemented and operated on two datasets of vehicles' images as test-beds. From the comparative study, the SVM outperforms the other adopted classifiers and is also better using HOG than that using ACO. A modification is done on HOG by adding the Laplacian filter to select the most significant image features. The accuracy of the SVM classifier using modified HOG outperforms that one using the traditional HOG. The proposed model is analyzed and discussed regardless the local geometric and photometric transformations like illumination variations.
Student performance prediction using simple additive weighting method Harco Leslie Hendric Spits Warnars; Arif Fahrudin; Wiranto Herry Utomo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp630-637

Abstract

In the world of student education is an important component where the role of students is as someone who is psychologically ready to receive lessons or other input from the school. However, each student has different performance and development, therefore it is important to do monitoring so that student performance will always be monitored by the school for improving student quality maintenance. Also, in the process of valuing education for students needs to be done by giving an appreciation in the form of giving gifts or just giving words and motivation so that students can perform better in learning and participating in other activities at school. In terms of selecting students with good performance or those who have a very declining development using the school method not only assess students by one criterion but with several criteria to produce a decision that can be accepted by many people. Performance Students must also be monitored by the school or the related rights. In this paper, the student performance prediction was assessed with 5 criteria components and the result shows there are 10 very satisfy students, 10 satisfying students, 10 well students, and 10 Enough students from sample 40 students.
A proposed system for opinion mining using machine learning, NLP and classifiers Poonam Tanwar; Priyanka rai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp726-733

Abstract

In today’s life consumer reviews are the part of everyday life. User read the reviews before purchase, or stores it for finding the best product through comparison of the product review. From customers view point the reviews play vital role to make a decision regarding an online purchase as well as spammers to write the fake reviews which can increase or defame the reputation of any product. Spammers are using these platforms illegally for financial benefits/incentives are involved in writing fake reviews and they are trying to achieve their motive in terms of financial or to defeat the competitor which causes an explosive growth of sentiment/opinion spamming of writing forged/fake reviews. The present studies and research are used to analyse and categorize the opinion spamming into three different detection targets opinion spam, spammers, and to find the collusive opinion spammer groups so that false opinions can be avoided. Opinion spamming further divided into three different types based on textual and linguistic, behavioral, and relational features. The motivation behind this work is to study the dynamics of spam diffusion and extract the latent features that fuel the diffusion process. The user-based features and content-based features have been used for the categorization of spam/non-spam content. The contributions of this work are building the datasetwhich assists as the ground-truth for classifying/analyzing the variation of fraud/genuine and non-spam/spam information diffusion and to analyze the effects of topics over the diffusibility of non-spam and spam evidences/information. The paper, carried out an in-depth analysis of Twitter Spam diffusion.
Effects of kernels and the proportion of training data on the accuracy of SVM sentiment analysis in lecturer evaluation Daniel Febrian Sengkey; Agustinus Jacobus; Fabian Johanes Manoppo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp734-743

Abstract

Support vector machine (SVM) is a known method for supervised learning in sentiment analysis and there are many studies about the use of SVM in classifying the sentiments in lecturer evaluation. SVM has various parameters that can be tuned and kernels that can be chosen to improve the classifier accuracy. However, not all options have been explored. Therefore, in this study we compared the four SVM kernels: radial, linear, polynomial, and sigmoid, to discover how each kernel influences the accuracy of the classifier. To make a proper assessment, we used our labeled dataset of students’ evaluations toward the lecturer. The dataset was split, one for training the classifier, and another one for testing the model. As an addition, we also used several different ratios of the training:testing dataset. The split ratios are 0.5 to 0.95, with the increment factor of 0.05. The dataset was split randomly, hence the splitting-training-testing processes were repeated 1,000 times for each kernel and splitting ratio. Therefore, at the end of the experiment, we got 40,000 accuracy data. Later, we applied statistical methods to see whether the differences are significant. Based on the statistical test, we found that in this particular case, the linear kernel significantly has higher accuracy compared to the other kernels. However, there is a tradeoff, where the results are getting more varied with a higher proportion of data used for training.
An application of machine learning on corporate tax avoidance detection model Rahayu Abdul Rahman; Suraya Masrom; Normah Omar; Maheran Zakaria
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp721-725

Abstract

Corporate tax avoidance reduces government revenues which could limit country development plans. Thus, the main objectives of this study is to establish a rigorous and effective model to detect corporate tax avoidance to assist government to prevent such practice. This paper presents the fundamental knowledge on the design and implementation of machine learning model based on five selected algorithms tested on the real dataset of 3,365 Malaysian companies listed on bursa Malaysia from 2005 to 2015. The performance of each machine learning algorithms on the tested dataset has been observed based on two approaches of training. The accuracy score for each algorithm is better with the cross-validation training approach. Additionationally, with the cross-validation training approach, the performances of each machine learning algorithm were tested on different group of features selection namely industry, governance, year and firm characteristics. The findings indicated that the machine learning models present better reliability with industry, governance and firm characteristics features rather than single year determinant mainly with the Random Forest and Logistic Regression algorithms.
Hyperspectral image classification using support vector machines Jonnadula Dr.J.Harikiran Harikiran
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp684-690

Abstract

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.
Design of meal intake prediction for gestational diabetes mellitus using genetic algorithm Marshima Mohd Rosli; Nor Shahida Mohamad Yusop; Aini Sofea Fazuly
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp591-599

Abstract

Gestational diabetes mellitus (GDM) is frequently described as glucose intolerance for pregnancy women. GDM patients currently practice the traditional method (record book) for recording blood glucose readings and keeping track of meal intake. This practice is not efficient and impractical for monitoring glucose level for GDM patients when we compared with mobile health monitoring technologies available today. Although, many applications have been developed for diabetes patients, but we do not found any application appropriate for GDM monitoring. In this study, we describe the design and development of mobile application for GDM monitoring using genetic algorithm that aims to predict recommended meal intake. We developed the mobile application for the GDM patients to maintain their blood glucose level through their meals. We tested the components of the mobile application and found that the prediction algorithm has successfully predicted the next meal intake according to the patient blood glucose levels. We hope this study will encourage research on development of selfmonitoring applications to improve blood glucose control for GDM.

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