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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 43 Documents
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Search results for , issue "Vol 11, No 4: December 2022" : 43 Documents clear
Lotus species classification using transfer learning based on VGG16, ResNet152V2, and MobileNetV2 Nachirat Rachburee; Wattana Punlumjeak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Technology has played an increasingly important role in daily life. Especially, technology in object classification that comes in to make human life more comfortable as well as to help people of all ages learning unlimited in anywhere, anytime. Lotus Museum located in Rajamangala University of Technology Thanyaburi (RMUTT) that is open to the general public to learn as well as to cultivate awareness for propagation and result in future preservation. In this paper, we proposed lotus species classification with three pre-trained weights in the ImageNet dataset: visual geometry group (VGG16), residual neural network (ResNet152V2), and MobileNetV2. Fine-turning is used in the last layer after retrained with the custom data we provided. The experimental result shows the accuracy of VGG16, ResNet152V2, and MobileNetV2 are 98.5%, 98.0%, and 99.5% respectively. Therefore, MobileNetV2 not only gives the best accuracy than others but also uses the lowest parameters which are effective in computation time and proper to mobile devices. The proposed research paper on lotus classification base on transfer learning is an effective way to encourage and support people to learn without limitations.
Bidirectional long-short term memory and conditional random field for tourism named entity recognition Annisa Zahra; Ahmad Fathan Hidayatullah; Septia Rani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The common thing to do when planning a trip is to search for a tourist destination. This process is often done using search engines and reading articles on the internet. However, it takes much time to search for such information, as to obtain relevant information, we have to read some available articles. Named entity recognition (NER) can detect named entities in a text to help users find the desired information. This study aims to create a NER model that will help to detect tourist attractions in an article. The articles used for the dataset are English articles obtained from the internet. We built our NER model using bidirectional long-short term memory (BiLSTM) and conditional random fields (CRF), with Word2Vec as a feature. Our proposed model achieved the best with an average F1-Score of 75.25% compared to all scenarios tested.
Identification of polar liquids using support vector machine based classification model Thushara Haridas Prasanna; Mridula Shantha; Anju Pradeep; Pezholil Mohanan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

 The dispersive nature of polar liquids creates ambiguity in their identification process. It requires a long time and effort to compare the measured values with the available standard values to identify the unknown liquid. Nowadays machine learning techniques are being used widely to assist the measurement techniques and make predictions with great accuracy and less human effort. This paper proposes a support vector machine (SVM) based classification model for the identification of six polar liquids- butan-1-ol, dimethyl sulphoxide, ethanediol, ethanol, methanol and propan-1-ol for a temperature range of 10 °C–50 °C and frequency range of 0.1 GHz – 5 GHz. The model is constructed using the data from the National Physical Laboratory (NPL) report MAT 23. The identification of unknown liquid is based on complex permittivity measurement. If the measurement error in complex permittivity is less than ±6% of the standard value in NPL report, the proposed model identifies the liquids with 100% accuracy in the entire temperature and frequency range. The performance of the model is validated by testing the model with data external to the dataset used.  The findings show that the proposed model is a useful and efficient tool for identifying unknown polar liquids.
Simulated trial and error experiments on productivity Karn Thamprasert; Ahmad Yahya Dawod; Nopasit Chakpitak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Trial and error experiments in socioeconomics were proved to be beneficial by Nobel prize laureates. However, replication is challenging and costly in term of time and money. The approach required interventions on human society, and moral issues have to be carefully considered in research designs. This work tried to make the approach more feasible by developing virtual economic environment to allow simulated trial and error experiments to take place. This research demonstrated the framework using 19 macroeconomic indicators in 6 interested categories to study the effect on productivity if each indicator value grew by 5 percent for each of 65 countries. Seven predictive models including some machine learning (ML) models were compared. Neural network dominated in accurateness and was selected as the core of the simulator. Experimented results are in full of surprises, and the framework acted as expected to be a data-driven guide toward country-specific policy making.
Comparison of structural analysis and principle component analysis for leakage prediction on superheater in boiler Eko Mursito Budi; Estiyanti Ekawati; Bobby Efendy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Leakage is one of the failures which commonly happens in boiler operation. Moreover, a continuous unsettled anomaly in a boiler could lead to leakage failure. An algorithm has been developed to predict the failure, consisting of three general procedures: feature selection, followed by hierarchical clustering, and naïve Bayes classification. The hierarchical clustering changes unlabeled data into labeled data, and naïve Bayes classification calculates the probability to justify anomaly occurrence. Meanwhile, this research focused on the effect of the feature selection method on the result of leakage prediction. Two different feature selection methods, namely the structural analysis and the principal component analysis (PCA), were deployed separately and then compared. The result showed that leakage prediction using the structural analysis method gave 13 hours 40 minutes of prediction time, and the PCA method gave 25 hours of prediction time. However, the PCA feature selection method caused more false alarms than feature selection with structural analysis, which only triggered five false alarms a week before leakage. Moreover, the structural analysis offered better traceability than PCA to understand the leakage occurrence.
A deep transfer learning approach for identification of diabetic retinopathy using data augmentation Yerrarapu Sravani Devi; Singam Phani Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

In ophthalmology, deep learning acts as a computer-based tool with numerous potential capabilities and efficacy. Throughout the world, diabetic retinopathy (DR) is considered as a principal cause of disease however loss of sight cannot be seen in adults aged 20-74 years. The primary objective for early detection of DR is screening on a regular basis at separate intervals which should have a time difference of every ten to twenty months for the patients with no or mild case of DR. Regular screening plays a major role to prevent vision loss, the expected cases increase from 415 million in 2015 to 642 million in 2040 means is a challenging task of ophthalmologists to do screening and follow-up representations. In this research, a transfer learning model was proposed with data augmentation techniques and gaussian-blur, circle-crop pre-processing techniques combination to identify every stage of DR using Resnet 50 with top layers. Models are prepared with Kaggle Asia Pacific Tele-Ophthalmology Society blindness dataset on a top line graphical processing data. The result depicts- the comparison of classification metrics using synthetic and non-synthetic images and achieve accuracy of 91% using the synthetic data and 86% accuracy without using synthetic data.
Hybrid algorithms based on historical accuracy for forecasting particulate matter concentrations Thanawut Thanavanich; Mayoon Yaibuates; Pumipong Duangtang; Seksan Winyangkul
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Air pollution has become one of the most significant problems impacting human health. Particulate matter (PM) 2.5 is usually used as an identifier of the intensity of the pollution. The PM2.5 forecasting is essential and gainful for reducing health risks. The efficient model for forecasting PM2.5 concentration can be used in determining the period of outdoor activities, thereby reducing the impact on health. In addition, the government sector can use the forecasting model as a tool for laying down measures a burning control. In this work, the hybrid forecasting algorithms for improving accuracy are presented. The hybrid forecasting algorithms combine neural network models with historical predictive data for improving the accuracy of forecasting. The experimental results show that the proposed algorithms can reduce the mean absolute error and root mean square error of forecasting at 36% and 45%. Therefore, the proposed algorithms are not only effectively used to forecast the PM2.5 concentrations but also apply the lightweight technique based on historical accuracy to forecast other complex problems efficiently.
A new approach for varied speed weigh-in-motion vehicle based on smartphone inertial sensors Ahmed A. Hamad; Yasseen Sadoon Atiya; Hilal Al-Libawy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Dynamic vehicle weight measuring, weigh-in-motion (WIM), is an important metric that can reflect significantly vehicle driving behaviour and in turn, it will affect both safety and traffic status. Several accurate (WIM) systems are developed and implemented successfully. These systems are using under road weighing sensor which are costly to implement. Moreover, it is costly and not very practical to embed a continuous weighing system in used cars. In this work, a low-cost varied-speed weigh-in-motion approach was suggested to continuously measuring vehicle load based on the response of smartphone sensors which is a reflection of vehicle dynamics. This approach can apply to any moving vehicle at any driving speed without the need for extra added hardware which makes it very applicable because smartphone is widely used device. The approach was tested through a six-trips experiment. Three capacities of load had been designed in this approach to be classified using a neural network classifier. The classification performance metrics are calculated and show an accuracy of 91.2%. This accuracy level is within error limits of existing WIM systems especially for high speed and proved the success of the suggested approach.
Classification technique for real-time emotion detection using machine learning models Chanathip Sawangwong; Kritsada Puangsuwan; Nathaphon Boonnam; Siriwan Kajornkasirat; Wacharapong Srisang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

This study aimed to explore models to identify a human by using face recognition techniques. Data were collected from Cohn-Kanade dataset composed of 398 photos having face emotion labeled with eight emotions (i.e., neutral, angry, disgusted, fearful, happy, sad, and surprised). Multi-layer perceptron (MLP), support vector machine (SVM), and random forest were used in model accuracy comparisons. Model validation and evaluation were performed using Python programming. The results on F1 scores for each class in the dataset revealed that predictive classifiers do not perform well for some classes. The support vector machine (RBF kernel) and random forest showed the highest accuracies in both datasets. The results could be used to extract and identify emotional expressions from the Cohn-Kanade dataset. Furthermore, the approach could be applied in other contexts to enhance monitoring activities or facial assessments. 
Sentinel-1A image classification for identification of garlic plants using decision tree and convolutional neural network Risa Intan Komaraasih; Imas Sukaesih Sitanggang; Annisa Annisa; Muhammad Asyhar Agmalaro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The Indonesian government launched a garlic self-sufficiency program by 2033 to reduce imports by monitoring garlic lands in several central garlic areas. Remote sensing using satellite imageries can assist the monitoring program by mapping the garlic lands. A previous study has classified Sentinel-1A satellite imageries to identify garlic lands in Sembalun Lombok Indonesia using the decision tree C5.0 algorithm with three scenarios data input and produced a model with an accuracy of 78.45% using scenarios with two attributes vertical-vertical (VV) and vertical-horizontal (VH) bands. Therefore, this study aims to improve the accuracy of the classification model from the previous study. This study applied two classification algorithms, decision tree C5.0 and convolutional neural network (CNN), with two new scenarios which used two new combinations of attributes). The results show that the use of new data scenarios as input for C5.0 can not increase the previous model's accuracy. While the use of the CNN algorithm shows that it can improve the previous study's accuracy by 7.91% because it produced a model with an accuracy of 86.36%. This study is expected to help garlic land identification in the Sembalun area to support government programs in monitoring garlic lands.

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