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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 1,722 Documents
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Massively scalable density based clustering (DBSCAN) on the HPCC systems big data platform Yatish H. R.; Shubham Milind Phal; Tanmay Sanjay Hukkeri; Lili Xu; Shobha G; Jyoti Shetty; Arjuna Chala
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp207-214

Abstract

Dealing with large samples of unlabeled data is a key challenge in today’s world, especially in applications such as traffic pattern analysis and disaster management. DBSCAN, or density based spatial clustering of applications with noise, is a well-known density-based clustering algorithm. Its key strengths lie in its capability to detect outliers and handle arbitrarily shaped clusters. However, the algorithm, being fundamentally sequential in nature, proves expensive and time consuming when operated on extensively large data chunks. This paper thus presents a novel implementation of a parallel and distributed DBSCAN algorithm on the HPCC Systems platform. The algorithm seeks to fully parallelize the algorithm implementation by making use of HPCC Systems optimal distributed architecture and performing a tree-based union to merge local clusters. The proposed approach* was tested both on synthetic as well as standard datasets (MFCCs Data Set) and found to be completely accurate. Additionally, when compared against a single node setup, a significant decrease in computation time was observed with no impact to accuracy. The parallelized algorithm performed eight times better for higher number of data points and takes exponentially lesser time as the number of data points increases.
CLG clustering for dropout prediction using log-data clustering method Agung Triayudi; Wahyu Oktri Widyarto; Lia Kamelia; Iksal Iksal; Sumiati Sumiati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp764-770

Abstract

Implementation of data mining, machine learning, and statistical data from educational department commonly known as educational data mining. Most of school systems require a teacher to teach a number of students at one time. Exam are regularly being use as a method to measure student’s achievement, which is difficult to understand because examination cannot be done easily. The other hand, programming classes makes source code editing and UNIX commands able to easily detect and store automatically as log-data. Hence, rather that estimating the performance of those student based on this log-data, this study being more focused on detecting them who experienced a difficulty or unable to take programming classes. We propose CLG clustering methods that can predict a risk of being dropped out from school using cluster data for outlier detection.
Comparison some of kernel functions with support vector machines classifier for thalassemia dataset Ilsya Wirasati; Zuherman Rustam; Jane Eva Aurelia; Sri Hartini; Glori Stephani Saragih
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp430-437

Abstract

In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%.
EEG signal classification for drowsiness detection using wavelet transform and support vector machine Novie Theresia Br. Pasaribu; Timotius Halim; Ratnadewi Ratnadewi; Agus Prijono
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp501-509

Abstract

There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using support vector machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class.
Gait cycle prediction model based on gait kinematic using machine learning technique for assistive rehabilitation device Che Ani Adi Izhar; Z. Hussain; M. I. F. Maruzuki; Mohd Suhaimi Sulaiman; A. A. Abd. Rahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp752-763

Abstract

The gait cycle prediction model is critical for controlling assistive rehabilitation equipment like orthosis. The human gait model has recently used statistical models, but the dynamic properties of human physiology limit the current approach. Current human gait cycle prediction models need detailed kinematic and kinetic data of the human body as input parameters, and measuring them requires special instruments, making them difficult to use in real-world applications. In our study, three separate machine learning algorithms were used to create a human gait model: Gaussian process regression, support vector machine, and decision tree. The algorithm used to create the model's input parameters are height, weight, hip and knee angle, and ground reaction force (GRF). For better gait cycle model prediction, the models produced were enhanced by incorporating different sliding window data. The best gait period prediction model was DT with sliding window data (t−3), which had a root mean square error of 3.3018 and the R-squared (R-Value) of 0.97. The projection model focused on hip and knee angle and GRF was a feasible solution to controlling assistive rehabilitation devices during the gait cycle.
A simulation energy management system of a multi-source renewable energy based on multi agent system Aoukach Basma; Oukarfi Benyounes
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp191-199

Abstract

The intermittent nature of renewable energies sources makes their control difficult. One of the solutions to overcome this handicap is to promote hybridization (multi-source system). To ensure continuity of service, a storage system must be coupled to the system. To do so, artificial intelligence based models are developed to respond optimally to the dilemma of energy supply and demand. These models allow the management of the energy flow between the sources (photovoltaic, wind, battery, super capacitor, and generator) and the variable loads by controlling electronic switches according to the availability of the sources. The artificial intelligence algorithm used in this study is multi agent system (MAS). The simulation results and validation tests shows the effectivenes of the proposed approach.
Heart rate events classification via explainable fuzzy logic systems Anis Jannah Dahalan; Tajul Rosli Razak; Mohammad Hafiz Ismail; Shukor Sanim Mohd Fauzi; Ray Adderley JM Gining
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp1036-1047

Abstract

As people, we have no way of knowing whether our heart rate is considered normal or not. The strength and quality of our pulses will deteriorate as we get older. As a result, this may indicate a heart attack or another illness that requires immediate attention. The main goal of this paper is to define the heart rate stage using fuzzy logic systems (FLSs). In practice, however, designing or developing fuzzy logic systems is extremely difficult. To achieve this aim, we suggested a solution that involves: i) classifying the medical expert's criterion for signs of heart rate; ii) developing an explainable fuzzy logic system for heart rate measurement; and iii) evaluating the proposed system with human experts. In addition, the aim of this research was to provide an explainable fuzzy system that people could use to self-monitor heart rate levels and determine their health status. As a result, it is hoped that this research would provide insight into how to improve the development of fuzzy logic systems, especially in the field of medical applications.
Measuring scientific collaboration in co-authorship networks Basim Mahmood; Nagham A. Sultan; Karam H. Thanoon; Dheyaa S. Kadhim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp1103-1114

Abstract

Scientific research is currently considered one of the key factors in the development of our life. It plays a significant role in managing our business, study, and work more conveniently. One of the important aspects when it comes to scientific research is the level of collaboration among researchers/disciplines. The collaboration between two different disciplines contributes to obtaining more reliable solutions for our everyday issues. Therefore, it is needed to understand the collaboration patterns among researchers and come up with convenient strategies for strengthening this kind of collaboration. In this work, we aim at investigating the patterns of scientific collaboration among researchers across disciplines. To this end, we generate a co-authorship network for several disciplines. The generated network reveals many interesting facts regarding the collaboration patterns among researchers who work in the same/different disciplines. We involve several measurements in this study that evaluate different aspects, which is of interest to the research communities since most of the studies in the literature measure specific aspects. Moreover, we propose a novel metric for measuring scientific collaboration in a research community and use it to benchmark the collaboration among disciplines. Finally, we use the obtained results/facts in providing recommendations for scientific communities.
Enhancing digital marketing performance through usage intention of AI-powered websites Dawud Adaviruku Suleiman; Tahir Mumtaz Awan; Maria Javed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp810-817

Abstract

Digital and wireless technology are a crucial part of today’s modern life. Artificial intelligence (AI) uses different technologies and systems for speech recognition, visual perception and decision making to mimic human functions. This study explores the impact of AI on website interactivity and the ease of use for enhancing digital marketing performance. The methodology used is qualitative with structured interviews, using three artificial intelligence-powered websites (Amazon, Alibaba, and Uber) as reference. The participants' structured interview responses were grouped into different thematic heading for coding and were subsequently analyzed by NVivo. The result found that artificial intelligence empowered websites were interactive, participants don’t feel safe and secure, easy to use, and tend to boost digital marketing performances. This study implies that more digital marketing companies should consider integrating artificial intelligence capabilities in their business operations. More security features should be embedded to help customers calm the fears of web insecurities.
A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization Olief Ilmandira Ratu Farisi; Budi Setiyono; R. Imbang Danandjojo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp910-918

Abstract

This study proposed a hybrid approach of firefly algorithm (FA) and ant colony optimization (ACO) for solving multi-depot multiple traveling salesman problem, a TSP with more than one salesman and departure city. The FA is fast converging but easily trapped into the local optimum. The ACO has a great ability to search for the solution but it converges slowly. To get a better result and convergence time, we integrate FA to find the local solutions and ACO to find a global solution. The local solutions of the FA are normalized then initialized to the quantity of pheromones for running the ACO. Furthermore, we experimented with the best parameters in order to optimize the solution. In justification, we used the sea transportation route in Indonesia as a case study. The experimental results showed that the hybrid approach of FA and ACO has superior performance with an average computational time of 26.90% and converges 32.75% faster than ACO.

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