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AHP approach for employee recruitment with COVID-19 situation in Thailand
(2026-01-01)
Narabin, Akan
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Boonjing, Veera
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Wuttanachamsri, Kanognudge
With the COVID-19 situation being unlike the usual, selecting an appropriate person for a job position requires the criteria and weight of each criterion be determined and adjusted to suit the crisis. The criteria chosen in this work are emphasised on selecting applicants who, while studying, have been in the midst of the COVID-19 situation. In this research, we employ analytic hierarchy process to assist the committee to have an agreement. In this study, each person in the committee can have his/her own pairwise comparison matrix of the criteria with a three-level hierarchical model. Some ambiguity may occur when initiating a hierarchical model; therefore, in this work, the criteria used in the three-level model are properly adjusted to create a four-level hierarchical model. The comparison inspected, provide some guidance in both theoretical manner and applications.
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Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes
(2024-01-01)
Kirdponpattara, Suppakrit
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Sooraksa, Pitikhate
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Boonjing, Veera
The manufacturing of hard disk drives involves the intricate assembly of numerous components, making the testing process time-consuming and resource intensive. To optimize the manufacturing process and increase testing efficiency, the development of a rule-based expert system is proposed. This system leverages predictive models constructed from assembly process data to identify potentially defective hard drives before undergoing extensive testing. By preemptively identifying defects, this approach substantially reduces testing time and enhances tester capacity. Given the categorical and imbalanced nature of assembly data, Decision Trees are employed as the prediction model. Specifically, three Decision Tree algorithms are explored: ID3, C4.5, and CART. In addition, four feature selection techniques, namely Information Gain, Gain Ratio, Chi-Square, and Symmetrical Uncertainty, are utilized to identify high-impact features. Our experimental findings reveal that Information Gain coupled with the C4.5 algorithm yields the most favorable results in terms of prediction accuracy, modeling efficiency, and rule generation. Moreover, our study establishes that setting the failure probability threshold between 0.15 and 0.70 provides the shortest total test time for the proposed process, as supported by a 95% confidence level. This achievement represents a statistically significant enhancement compared with the existing manufacturing process.
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An Alternative Method for Upgrading the Conventional Decision Tree Algorithm
(2024-01-01)
Kirdponpattara, Suppakrit
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Boonjing, Veera
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Sooraksa, Pitikhate
Decision tree algorithms are widely used for solving classification and regression problems. Their popularity can be attributed to their transparent nature, simplicity, easy interpretability, faster classification speed, and strong decision rules. However, decision tree induction algorithms face various inherent and external limitations, such as overfitting, high sensitivity to noise and outliers, and instability with minimal data variations. In this study, we introduce an innovative approach to enhance traditional decision tree algorithms [e.g., Iterative Dichotomiser 3 (ID3), C4.5, and Classification and Regression Trees (CART)] by incorporating feature selection techniques. The proposed approach aims to enhance the accuracy and efficiency of decision tree models. Experiments were conducted on a real-world dataset of a hard disk drive (HDD) manufacturing process using the proposed approach. In comparison with a baseline where all features were utilized, the study highlighted a significant improvement in accuracy, indicating that the approach holds immense potential for optimizing decision tree algorithms and improving the HDD manufacturing process.
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Coral Reef Bleaching under Climate Change: Prediction Modeling and Machine Learning
(2022-05-01)
Boonnam, Nathaphon
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Udomchaipitak, Tanatpong
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Puttinaovarat, Supattra
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Chaichana, Thanapong
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Boonjing, Veera
The coral reefs are important ecosystems to protect underwater life and coastal areas. It is also a natural attraction that attracts many tourists to eco-tourism under the sea. However, the impact of climate change has led to coral reef bleaching and elevated mortality rates. Thus, this paper modeled and predicted coral reef bleaching under climate change by using machine learning techniques to provide the data to support coral reefs protection. Supervised machine learning was used to predict the level of coral damage based on previous information, while unsupervised machine learning was applied to model the coral reef bleaching area and discovery knowledge of the relationship among bleaching factors. In supervised machine learning, three widely used algorithms were included: Naïve Bayes, support vector machine (SVM), and decision tree. The accuracy of classifying coral reef bleaching under climate change was compared between these three models. Unsupervised machine learning based on a clustering technique was used to group similar characteristics of coral reef bleaching. Then, the correlation between bleaching conditions and characteristics was examined. We used a 5-year dataset obtained from the Department of Marine and Coastal Resources, Thailand, during 2013–2018. The results showed that SVM was the most effective classification model with 88.85% accuracy, followed by decision tree and Naïve Bayes that achieved 80.25% and 71.34% accuracy, respectively. In unsupervised machine learning, coral reef characteristics were clustered into six groups, and we found that seawater pH and sea surface temperature correlated with coral reef bleaching.
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Development of Elderly Life Quality Database in Thailand with a Correlation Feature Analysis
(2022-04-01)
Nillaor, Pichetwut
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Sriwichian, Anirut
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Wanichsombat, Apirat
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Kajornkasirat, Siriwan
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Boonjing, Veera
Understanding the context of the elderly is very important for determining guidelines that improve their quality of life. One problem in Thailand, in this context, is that each organization involved in caring for the elderly has its own separate data collection, resulting in mismatches that negatively affect government agencies in their monitoring. This study proposes the development of a central database for elderly care and includes a study of factors affecting their quality of life. The proposed system can be used to collect data, manage data, perform data analysis with multiple linear regression, and display results via a web application in visualizations of many forms, such as graphs, charts, and spatial data. In addition, our system would replace paper forms and increase efficiency in work, as well as in storage and processing. In an observational case study, we include 240 elderly in village areas 5, 6, 7, and 8, in the Makham Tia subdistrict, Muang district, Surat Thani province, Thailand. Data were analyzed with multiple linear regression to predict the level of quality of life by using other indicators in the data gathered. This model uses only 14 factors of the available 39. Moreover, this model has an accuracy of 86.55%, R-squared = 69.11%, p-Value < 2.2 × 10<sup>−16</sup>, and Kappa = 0.7994 at 95% confidence. These results can make subsequent data collection more comfortable and faster as the number of questions is reduced, while revealing with good confidence the level of quality of life of the elderly. In addition, the system has a central database that is useful for elderly care organizations in the community, in support of planning and policy setting for elderly care.
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A novel elderly tracking system using machine learning to classify signals from mobile and wearable sensors
(2021-12-01)
Muangprathub, Jirapond
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Sriwichian, Anirut
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Wanichsombat, Apirat
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Kajornkasirat, Siriwan
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Nillaor, Pichetwut
A health or activity monitoring system is the most promising approach to assisting the elderly in their daily lives. The increase in the elderly population has increased the demand for health services so that the existing monitoring system is no longer able to meet the needs of sufficient care for the elderly. This paper proposes the development of an elderly tracking system using the integration of multiple technologies combined with machine learning to obtain a new elderly tracking system that covers aspects of activity tracking, geolocation, and personal information in an indoor and an outdoor environment. It also includes information and results from the collaboration of local agencies during the planning and development of the system. The results from testing devices and systems in a case study show that the k-nearest neighbor (k-NN) model with k = 5 was the most effective in classifying the nine activities of the elderly, with 96.40% accuracy. The developed system can monitor the elderly in real-time and can provide alerts. Furthermore, the system can display information of the elderly in a spatial format, and the elderly can use a messaging device to request help in an emergency. Our system supports elderly care with data collection, tracking and monitoring, and notification, as well as by providing supporting information to agencies relevant in elderly care.
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Learning recommendation with formal concept analysis for intelligent tutoring system
(2020-10-01)
Muangprathub, Jirapond
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Boonjing, Veera
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Chamnongthai, Kosin
Computer Science; Learning recommendation; Formal concept analysis; Intelligent tutoring system; Adaptive learning
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Efficient breadth-first reduct search
(2020-05-01)
Boonjing, Veera
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Chanvarasuth, Pisit
This paper formulates the problem of determining all reducts of an information system as a graph search problem. The search space is represented in the form of a rooted graph. The proposed algorithm uses a breadth-first search strategy to search for all reducts starting from the graph root. It expands nodes in breadth-first order and uses a pruning rule to decrease the search space. It is mathematically shown that the proposed algorithm is both time and space efficient.
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Web-based Elderly Monitoring System with GIS
(2019-07-01)
Sriwichian, Anirut
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Boonjing, Veera
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Nillaor, Pichetwut
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Muangprathub, Jirapond
Many countries have become elderly society due to the increase ratio in the elderly population. This increase causes an impact on the elderly and society in many ways. If we are to solve problems for the elderly, it is important to have elderly information that helps understand their being and quality of life. This research aims to develop the web-based elderly monitoring system with GIS that is used to store data and assess the level of quality of life of the elderly. Afterward, the proposed system provided in a spatial data format using GIS technology. We use this system to collect all the elderly in four villages (Moo 5, 6, 7, and 8). From the using of the system in 4 villages, there were 240 elderly people who participated in providing information. The result is the quality of life information and general information, both overview and personal, which the agencies related to elderly care can be used to promote the development of the quality of life for the elderly.
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A Knowledge Integrated Case-Based Classifier
(2019-06-01)
Muangprathub, Jirapond
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Kajornkasirat, Siriwan
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Wanichsombat, Apirat
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Boonjing, Veera
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Saelee, Jarunee
This paper proposes a case-based classifier using a new approach that integrates rule-based and case-based reasoning approaches for enhanced accuracy. The rule-based reasoning component uses rules generated from a concept lattice of training data, binarized using fuzzy sets. These binarized data are stored as cases in the case-based classification component. The case-based component complements the rule-based component to enhance classification accuracy. Moreover, we designed the case-based component with an embedded similarity measure that uses a vector model for concept approximations. Thus, this design makes it possible to generate high quality rules and classify unseen new cases. In addition, the ability to build a knowledge base in lattice form is important for discovering hierarchical patterns, incrementing or updating the existing knowledge base, and inducing rules with our rule learning algorithm. The novel methodology was implemented and evaluated with benchmark datasets from the UCI repository and historic rubber prices in Thailand, demonstrating improvements in accuracy of classification calls. The results from the fact their several hierarchical datasets are very promising, with improved classification performance over prior reported methods.

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