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Laor Initialization: A New Weight Initialization Method for the Backpropagation of Deep Learning
(2025-07-01)
Boongasame, Laor
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Muangprathub, Jirapond
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Thammarak, Karanrat
This paper presents Laor Initialization, an innovative weight initialization technique for deep neural networks that utilizes forward-pass error feedback in conjunction with k-means clustering to optimize the initial weights. In contrast to traditional methods, Laor adopts a data-driven approach that enhances convergence’s stability and efficiency. The method was assessed using various datasets, including a gold price time series, MNIST, and CIFAR-10 across the CNN and LSTM architectures. The results indicate that the Laor Initialization achieved the lowest K-fold cross-validation RMSE (0.00686), surpassing Xavier, He, and Random. Laor demonstrated a high convergence success (final RMSE = 0.00822) and the narrowest interquartile range (IQR), indicating superior stability. Gradient analysis confirmed Laor’s robustness, achieving the lowest coefficients of variation (CV = 0.2230 for MNIST, 0.3448 for CIFAR-10, and 0.5997 for gold price) with zero vanishing layers in the CNNs. Laor achieved a 24% reduction in CPU training time for the Gold price data and the fastest runtime on MNIST (340.69 s), while maintaining efficiency on CIFAR-10 (317.30 s). It performed optimally with a batch size of 32 and a learning rate between 0.001 and 0.01. These findings establish Laor as a robust alternative to conventional methods, suitable for moderately deep architectures. Future research should focus on dynamic variance scaling and adaptive clustering.
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Enhanced Feature Selection via Hierarchical Concept Modeling
(2024-12-01)
Saelee, Jarunee
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Wetchapram, Patsita
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Wanichsombat, Apirat
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Intarasit, Arthit
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Muangprathub, Jirapond
The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. With big data, it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. In this study, we present a new feature selection approach based on hierarchical concept models using formal concept analysis (FCA) and a decision tree (DT) for selecting a subset of attributes. The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). The hierarchical concept model is built from a dataset, and it is selected by top-down considering features (attributes) node for each level of structure. Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on a paired-samples t-test. To compare the identified models in order to evaluate feature selection effects, the indicators used were information gain (IG) and chi-squared (CS), while both forward selection (FS) and backward elimination (BS) were tested with the datasets to assess whether the presented model was effective in reducing the number of features used. The results show clearly that the proposed models when using DT or using FCA, needed fewer features than the other methods for similar classification performance.
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Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory
(2024-01-01)
Boongasame, Laor
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Boonpluk, Jindaphon
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Soponmanee, Sunisa
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Muangprathub, Jirapond
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Thammarak, Karanrat
This study aims to design and implement deepfake video detection using VGG-16 in combination with long short-term memory (LSTM). In contrast to other studies, this study compares VGG-16, VGG-19, and the newest model, ResNet-101, including LSTM. All the models were tested using Celeb-DF video dataset. The result showed that the VGG-16 model with 15 epochs and 32 batch sizes had the highest performance. The results showed that the VGG-16 model with 15 epochs and 32 batch sizes exhibited the highest performance, with 96.25% accuracy, 93.04% recall, 99.20% specificity, and 99.07% precision. In conclusion, this model can be implemented practically.
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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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Portfolio Risk and Return with a New Simple Moving Average of Price Change Ratio
(2020-12-01)
Muangprathub, Jirapond
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Intarasit, Arthit
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Boongasame, Laor
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Phaphoom, Nattakarn
Cluster analysis is a commonly used technique by investors to create a diversified portfolio. The approach aims at maximizing returns for a tolerable degree of risks. To diversify effectively, investors use similarity measures to enable clustering. Traditional price indexes, such as Return on Asset and Return on Equity, are known to perform inconsistently in identifying acceptable clusters. Our study proposes a novel indicator, Simple Moving Average of Price Change Ratios (SMA-PCR-N), for use as a similarity measure. It is an adjusted version of the traditional Simple Moving Average (SMA) calculated based on stocks’ closing price over a number of time periods, to observe price trend and potential changes. Instead, the SMA-PCR-N considers the daily opening prices, the closing prices and the average price of stocks. We demonstrate the use of k-means clustering with SMA-PCR-N to create a diversified stock portfolio. Data on approximately three hundred stocks were retrieved from the Stock Exchange of Thailand for the fiscal years 2015–2017, and were used experiments to evaluate the effectiveness of our SMA-PCR-N diversification approach. The results show that SMA-PCR-N based portfolios gave higher returns than portfolios created based on SMA clusters, in most cases.
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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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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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