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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 123 Documents
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Search results for , issue "Vol 13, No 2: June 2024" : 123 Documents clear
Neobots: an open-source platform for a low-cost neonatal incubator with internet of things approach Aryanto, I Komang Agus Ady; Maneetham, Dechrit; Triandini, Evi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1817-1837

Abstract

A baby incubator implements the internet of things (IoT) with an architectural design combining several scientific fields, such as networks, software, and hardware. Furthermore, this research develops an open-source platform called Neobots, including open-source program code to create a baby incubator. Then an overview of the system includes sending sensor data to the IoT Broker with the message queuing telemetry transport (MQTT) protocol and automatically storing data in the database. The results of the comparison value on each temperature sensor with a temperature sensor at the midpoint with an error of less than 0.7°C. Then testing the fuzzy between the Neobots program and the simulation in MATLAB got an error rate of 0-28.27%. In addition, in less than 10 minutes, the system response can adjust the temperature conditions to a setpoint value of 34°C from 29°C, and the average error value is 0.35°C during 1 hour of the Fuzzy implementation on the incubator. Then transfer data from the incubator to the database in a room without noise and full noise to get results for lost data less than 16.41% and 42.14%, delay rates between 0-6 seconds and 0-7 seconds with testing for 1 hour at every 1 second.
A triangle decomposition method for the mobility control of mecanum wheel-based robots Olivier Akansie, Kouame Yann; C. Biradar, Rajashekhar; Karthik, Rajendra; D. Devanagavi, Geetha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1326-1338

Abstract

Mobile robots are used in a variety of applications including research, education, healthcare, customer service, security and so on. Based upon the application, the robots employ different locomotion systems for their mobility. When it comes to rolling locomotion, the wheels used to provide mobility to robots can be categorized as: tracks, omnidirectional wheels, and unidirectional wheels with a steering system. The ability of omnidirectional wheels to drive machines in small spaces makes them interesting to use. Among the types of omnidirectional wheels, mecanum wheels are widely used due to their inherent benefits. With the right control strategy, robots equipped with mecanum wheels can move freely, in all possible directions. In this study, a triangle decomposition approach is employed for controlling omnidirectional mecanum wheel-based robots. The method consists of breaking down any path into a set of linear motions that can be horizontal, vertical, or oblique. Furthermore, the oblique paths are divided into smaller segments that can be resolved into a horizontal and vertical component in a right-angle triangle. The suggested control method is tested and proved on a simple scenario using Webots simulation software.
Intelligent fuzzy system to assess the risk of type 2 diabetes and diagnosis in marginalized regions Grande-Ramírez, José Roberto; Meza-Palacios, Ramiro; Aguilar-Lasserre, Alberto A.; Flores-Asis, Rita; Vázquez-Rodríguez, Carlos F.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1935-1944

Abstract

Diabetes is one of the leading causes of death in the world and continues to rise. Type 2 diabetes mellitus is a life-threatening chronic degenerative disease if not appropriately controlled; risk factors and ineffective diagnosis continue to increase its prevalence. This study proposes an intelligent fuzzy system to make a diagnosis and predict the risk of developing type 2 diabetes mellitus. The system consists of two models; the R-T2DM model estimates if a person is at risk of developing type 2 diabetes mellitus. The DT2DM model is based on two systems: the symptomatology system estimates the level of symptoms the patient has, and the diagnosis system diagnoses type 2 diabetes mellitus. The results of this research were compared with those estimated by the team of doctors, and it was observed that the R-T2DM model obtained a success rate of 90.3%. The D-T2DM model got a success rate of 88.3% for the symptomatology system and 95.5% for the diagnosis system. The model developed in this study is focused on being applied in economically marginalized geographic areas of Mexico to improve the patient's quality of life.
An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based Y. Syah, Rahmad B.; Muliono, Rizki; Akbar Siregar, Muhammad; Elveny, Marischa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1547-1556

Abstract

Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR). SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.
Semi-supervised spectral clustering using shared nearest neighbor for data with different shape and density YouSheng, Gao; Abdul Rahim, Siti Khatijah Nor; Hamzah, Raseeda; Ang, Li; Aminuddin, Raihah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2283-2290

Abstract

In the absence of supervisory information in spectral clustering algorithms, it is difficult to construct suitable similarity graphs for data with complex shapes and varying densities. To address this issue, this paper proposes a semisupervised spectral clustering algorithm based on shared nearest neighbor (SNN). The proposed algorithm combines the idea of semi-supervised clustering, adding SNN to the calculation of the distance matrix, and using pairwise constraint information to find the relationship between two data points, while providing a portion of supervised information. Comparative experiments were conducted on artificial data sets and University of California Irvine machine learning repository datasets. The experimental results show that the proposed algorithm achieves better clustering results compared to traditional K-means and spectral clustering algorithms.
Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased Fachrurrozi, Muhammad; Darmawahyuni, Annisa; Samsuryadi, Samsuryadi; Passarella, Rossi; Archibald Hutahaean, Jerrel Adriel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1679-1686

Abstract

Brain tumor is an uncontrolled growth of abnormal cell in the brain. Early diagnosis of brain tumor has a crucial step in this type of cancer, which is fatal. Magnetic resonance imaging (MRI) is one of the examination tools to examine brain anatomy in clinical practice. The high resolution and clear separation of the tissue enable medical experts to identify brain tumor. The earlier of brain tumor is detected, the wider of treatment options. However, manually analysed of brain anatomy on MRI images are time-consuming. Computer-aided diagnosis with automated way is helpful solution to help management with unreliable degrees of automation to trace various tissue boundaries. This study proposes convolutional neural network (CNN) with its excellences to automated features extraction in convolution layer. The popular architectures of CNN, i.e., visual geometry group16 (VGG16), residual network-50 (resNet-50), inceptionV3, mobileNet, and efficientNetB7 in medical image processing are compared to brain tumor classification task. As the results, VGG16 outperformed other architectures of CNN in this study. VGG16 yields 100% accuracy, precision, sensitivity, specificity, and F1-score for testing set data. The results show the excellent performance in classifying brain tumor and no tumor from MRI images that demonstrate the efficiency of system suggested.
Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province Soekirno, Santoso; Ananda, Naufal; Wicaksana, Haryas Subyantara; Yulizar, David; Prabowo, Muhammad Agung; Adi, Suko Prayitno; Santoso, Bayu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1878-1887

Abstract

Drought Early Warning System (DEWS) is an effort to disseminate early warning information based on climate and hydrology aspects. The DEWS design uses ARIMA, MLP, and hybrid ARIMA-MLP models to predict drought based on SPI for 1, 3, and 6 months. Predictions were made using ERA5 monthly rainfall data from 1981-2022 corrected based on observation data on 9 grids of observation rain gauges in Banten Province. The design of the ARIMA model is determined by selecting the combination of p and q parameters with the lowest AIC value, while the MLP architecture is determined by referring to the study literature and by trial and error testing. ARIMA models and hybrid models are not able to follow actual data fluctuations and have high error values in both SPI1, SPI3, and SPI6, so they are not recommended in this study. The MLP model has the best prediction ability, namely in SPI6 prediction with NSE value reaching >0.5 and RMSE value.
Enhancing car plate recognition with convolutional neural network and regular expressions correction Awoseyi, Ayomikun Abayomi; Timothy, Timileyin Favour; Ajagbe, Sunday Adeola; Onuiri, Ernest Enyinnaya; Abdulahi, Qudus Opeyemi; Adekunle, Temitope Samson; Adigun, Matthew Olusegun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2073-2080

Abstract

This research paper presents the development and evaluation of an Automatic Number Plate Recognition (ANPR) system using Convolutional Neural Networks (CNN) with Regex correction. The aim is to enhance the accuracy and effectiveness of car verification and security processes at First Technical University, Ibadan. The ANPR system was implemented both without Regex correction and with Regex correction. The evaluation results demonstrate significant improvements in the system's performance when CNN with Regex correction is employed. The CNN-based ANPR system achieves a precision of 1.00, recall of 0.90, and F1-score of 0.95 in accurately identifying number plates. These scores indicate increased accuracy and reduce false positives compared to the system without Regex correction. The integration of CNN and Regex correction effectively handles variations and errors in the number plate data, leading to a reliable and efficient car verification process. Future work can focus on further refining the CNN model and optimizing the Regex correction algorithms to enhance the system's accuracy and robustness. The developed ANPR system, utilizing CNN with Regex correction, shows great potential for enhancing car verification and security in various domains, including law enforcement, parking management, and traffic monitoring
Towards an optimization of automatic defect detection by artificial neural network using Lamb waves Salah, Nissabouri; Barra Ndiaye, Elhadji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1459-1468

Abstract

This paper presents a damage detection method based on the inverse pattern recognition technique by artificial neural network (ANN) using ultrasonic waves. Lamb waves are guided elastic waves, are widely employed in nondestructive testing thanks to their attractive properties such as their sensitivity to the small defects. In this work, finite element method was conducted by Abaqus to study Lamb modes propagation. A data collection is performed by the signals recorded from the sensor of 300 models: healthy and damaged plates excited by a tone burst signal with the frequencies: 100 kHz, 125 kHz, 150 kHz, 175 kHz, 200 kHz, and 225 kHz. The captured signals in undamaged plat are the baseline, whereas the signals measured in damaged plates are recorded for various positions of external rectangular defects. To reduce the amount of training data, only two peaks of measured signals are required to be the input of the model. Continuous wavelet transform (CWT) was adopted to calculate the key features of the signal in the time domain. The feed forward neural network is implemented using MATLAB program. The data are divided as follows: 70% for training the model, 25% for the validation, and 5% for the test. The proposed model is accurate estimating the position of the defect with an accuracy of 99.98%.
Identification and classification of prakriti of human using facial features Chickaramanna, Suguna Guttoor; Thippeswamy, Veerabhadrappa Sondekere
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2093-2101

Abstract

Changes in the lifestyle of an individual, has lead to several diseases that have emerged due to the imbalance of doshas components. Ayurveda practitioners could identify the imbalance of dosha and relate the root cause of imbalance of doshas. Analysis of dosha varies from practitioner to practitioner and it requires well practiced practitioner to identify dosha. To overcome, darshana method was adopted to implement automatic identification of predominant dosha using facial features such as face, eyes, nose, mouth, and skin color. Computer vision and image processing techniques were made attempt in Ayurveda domain, for identification of predominate prakriti, age, and gender of the subject. Eye aspect ratio (EAR), nose aspect ratio (NAR), mouth aspect ratio (MAR), and skin color was computed based on Euclidean distance to identify on-live predomaint prakriti of an individual. The values of MAR ≤ 0.5, EAR ≤ 0.1, NAR ≤ 0.8 as identified as vata; 0.5 ≥ MAR ≤ 0.6, 0.1 ≥ EAR ≤ 0.2, 0.8 ≥ NAR ≤ 1 as identified as pitta; and MAR ≥ 0.6, EAR ≥ 0.2 and NAR ≥ 1 as identified as kapha dosha. With the features MAR, EAR, and NAR classification of predominant prakriti was carried out with an accuracy of 87.5% with support vector classifier.

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