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Machine learning approach to predict the strength of concrete confined with sustainable natural FRP composites
(2024-07-01)
Ali Talpur, Shabbir
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Thansirichaisree, Phromphat
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Poovarodom, Nakhorn
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Mohamad, Hisham
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Zhou, Mingliang
Recent earthquakes have highlighted the need to strengthen existing structures with substandard designs. NFRPs provide a sustainable, cost-effective alternative for strengthening, but accurately predicting their performance remains a challenge. This study investigates the use of machine learning algorithms for predicting the compressive strength concrete specimens confined with various NFRPs. Four algorithms were employed: decision tree, random forest, neural network, and gradient boosting regressor. A diverse dataset encompassing various geometries, material properties, and confinement configurations was used to train and evaluate the models. Gradient boosting regressor (GBR) achieved the highest performance, with an average R-squared value of 0.94 and low mean absolute error (MAE) and root mean squared error (RMSE) during training and k-fold cross-validation. Neural network and random forest also demonstrated satisfactory performance, with average R-squared values of 0.88 and 0.86, respectively, during cross-validation. These results suggest that machine learning holds promise for predicting the compressive strength of concrete confined with NFRPs. GBR offers the most accurate predictions, making it a valuable tool for engineers seeking to optimize the design and performance of strengthened structures using sustainable materials.
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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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Improving the ID3 Algorithm By Filtering Out Attributes With Values Of 0 or 1
(2022-01-01)
Chuenprasertsuk, Pinyarat
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Jearanaitanakij, Kietikul
The iterative Dichotomiser 3 (ID3) algorithm is a classification algorithm that generates a decision tree, and it is one of the most simple and well-known tools. However, there is still room for improvement. This paper aims to reduce the running time of generating a decision tree by ignoring attributes that have an information gain value of 0 or 1 and the experiment result shows that the improved ID3 algorithm can significantly reduce the running time by more than 10 percent when compared with the traditional ID3 algorithm.
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Effectiveness of Six Text Classifiers for Predicting SET Stock Price Direction
(2020-01-01)
Netisopakul, Ponrudee
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Saewong, Woranun
Six text classification methods were compared to find the best model for predicting Stock Exchange of Thailand stock prices. News headlines, on individual stocks, were classified as causing “change” and “no-change” based on a preset change threshold, 2.5%. The training dataset was collected by matching stock news in 2018 with stock names and filling in stock price changes. 258 news were associated with a “change” and 636 news with “no-change”. The Thai text news items were preprocessed and converted to TF-IDF vector representation. Six machine learning text classification methods are applied to create six text classifier models and create a confusion matrix, then compared with actual changes to obtain accuracy scores. We found that a deep learning classifier (with 85.6% accuracy) scored better than other classifiers for one day price movement to assist short-term investments.
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Predicting sheet and rill erosion of Shihmen reservoir watershed in Taiwan using machine learning
(2019-07-01)
Nguyen, Kieu Anh
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Chen, Walter
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Lin, Bor Shiun
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Seeboonruang, Uma
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Thomas, Kent
Shihmen Reservoir watershed is vital to the water supply in Northern Taiwan but the reservoir has been heavily impacted by sedimentation and soil erosion since 1964. The purpose of this study was to explore the capability of machine learning algorithms, such as decision tree and random forest, to predict soil erosion (sheet and rill erosion) depths in the Shihmen reservoir watershed. The accuracy of the models was evaluated using the RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R<sup>2</sup>. Moreover, the models were verified against the multiple regression analysis, which is commonly used in statistical analysis. The predictors of these models were 14 environmental factors which influence soil erosion, whereas the target was 550 erosion pins installed at 55 locations (on 55 slopes) and monitored over a period of approximately three years. The data sets for the models were separated into 70% for the training data and 30% for the testing data, using the simple random sampling and stratified random sampling methods. The results show that the random forest algorithm performed the best of the three methods. Moreover, the stratified random sampling method had better results among the two sampling methods, as anticipated. The average error (RMSE relative to 1:1 line) of the stratified random sampling method of the random forest algorithm is 0.93 mm/yr in the training data and 1.75 mm/yr in the testing data, respectively. Finally, the random forest algorithm predicted that type of slope, slope direction, and sub-watershed are the three most important factors of the 14 environmental factors collected and used in this study for splits in the trees and thus they are the three most important factors affecting the depth of sheet and rill erosion in the Shihmen Reservoir watershed. The results of this study can be employed by decision-makers to improve soil conservation planning and watershed remediation.
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A deep learning model for predicting buy and sell recommendations in stock exchange of Thailand using long short-term memory
(2019-02-01)
Sanboon, Thaloengpattarakoon
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Keatruangkamala, Kamol
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Jaiyen, Saichon
Nowadays, the stock price prediction has been one of the most challenging problem to the AI research community. Most prediction techniques concentrate on forecasting the future prices of stocks based on conventional Machine learning techniques. However, these techniques cannot capture long term dependencies in stock price data. Therefore, they cannot consider the relation between the current predicted data and the previous data in stock data. This research adopts deep learning techniques for predicting buy and sell recommendations in Stock Exchange of Thailand using Long Short-Term Memory. The proposed model can capture long term dependencies in stock price data in order to enhance the prediction accuracy. The accuracy of the proposed model is evaluated on five Stock Exchange of Thailand (SET) stocks, between 5 January 2015 and 29 December 2017, and compared the results with support vector Machine, multilayer perceptron, decision tree, random forest, logistic regression and k-nearest neighbors. The experimental results signify that the proposed model can outperform all comparative models.
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Application of neural networks for vehicle classifiers: Extreme learning machine approach
(2019-01-18)
Jiaramaneepinit, Boonnithi
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Nuthong, Chaiwat
Machine learning has been a popular topic in research field for many applications. One of the applications is traffic surveillance system. In many areas, traffic surveillance system is installed in order to gather and estimate important traffic information. Nowadays, there are several systems used for information's extracting, and classifying. One of the well-known approaches is decision tree. It uses a tree-like model that decides consequences outcomes from events. However, in some application, decision tree does not perform well. Another widely used approach is neural network, which has promising performance. It has been developed and become one of the most popular computing systems in research field. The traditional approach in training neural network is backpropagation. However, it has several drawbacks. One of them is the training time. In recent decades, Extreme learning machine (ELM) was proposed for training single hidden layer feed-forward neural network (SLFN) in the extremely fast way. It minimizes training error by utilizing dataset in one-shot calculation. This paper focuses on classifiers in traffic surveillance system. The classification divides into two main tasks. One is vehicle types' classification. Another is vehicle colors' classification. Neural networks trained with ELM are applied to the dataset. The performance are then compared to decision tree based approaches with ensemble methods. The experimental results show that ELM achieves better accuracy than of decision tree based approaches in both tasks.
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Improving ID3 Algorithm by Combining Values from Equally Important Attributes
(2018-08-21)
Kraidech, Suratchanan
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Jearanaitanakij, Kietikul
ID3 is a well-known algorithm which is used in the classification task of the decision tree learning. Although a lot of research provides improvements on the traditional ID3 algorithm with various strategies, no attempt was made on ID3 to address the problem when there are more than one attribute that can be placed at a particular node, i.e. those attributes are equally important. This paper proposes a new variation of ID3 to combine equally important attributes into a single node of the decision tree classification. The Connect-4 dataset from UCI is used in our experiment since the dataset contains many attributes and instances which can easily encounter the equally important attribute problem. The experimental results show that our proposed method significantly reduces the average depth of the decision tree generated by ID3 algorithm while the average accuracy rate is still preserved.
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Improving ID3 Algorithm by Using A∗ Search
(2018-08-21)
Kaewrod, Nicha
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Jearanaitanakij, Kietikul
ID3 is one of the most widely used algorithms for creating a classification decision tree. However, the traditional ID3 algorithm has a difficulty when there are equally important attributes during the decision node construction. It randomly selects one of the most important attributes to serve as the current decision node. This behavior may lead to the resulting decision tree which contains unnecessary depths and suboptimal accuracy. The purpose of this paper is to find the near optimal depth decision tree of the dataset, which contains an equally important attributes problem, by using the A-Star (A∗) search. The experiment results on four standard datasets from UCI indicate that the proposed algorithm can significantly reduce the decision tree's depth and still maintain the classification accuracy.
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Reducing the depth of ID3 algorithm by combining values from neighboring important attributes
(2018-07-02)
Kraidech, Suratchanan
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Jearanaitanakij, Kietikul
The ID3 algorithm is one of the most popular decision tree algorithms which is mainly used in the classification task. There are many pieces of research about improving the ID3 algorithm by using various strategies. We improved the ID3 algorithm by addressing the equally important attributes problem in our previous work. In this paper, we extend our previous algorithm by changing the attribute selection to allow the neighboring second-place important attributes to be combined with the most important attributes. The proposed algorithm is tested on four standard benchmarks from the UCI repository. The experimental results indicate the significant reduction in the maximum depth and the classification depth of the decision tree. In addition, the testing time is also reduced while the classification accuracy is satisfying stable.

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