Boongasame, Laor

Preferred name

Boongasame, Laor

Alternative Name

Boongasame, Labor

Main Affiliation

School of Science

laor.bo@kmitl.ac.th

Now showing 1 - 10 of 26

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An ELECTRE III Based CBR Approach to Combinatorial Portfolio Selection
(2019-06-01)
Chanvarasuth, Pisit
;
Boongasame, Laor
;
Boonjing, Veera
Investors generally learn from historical data and use it to improve future investment decisions. However, existing portfolio selection research rarely considers such a concept. This paper proposes a novel approach to the combinatorial portfolio selection problem. Our solution employs the Case-Based Reasoning (CBR) approach with ELECTRE III, based on the Industry Classification Benchmark standards. The target stocks are ranked based on their similarities to a reference case, allowing specified selection criteria. Similarities are calculated based on selected ELECTRE III metrics. Experimental results with the Stock Exchange of Thailand data show that our CBR approach with ELECTRE III outperforms the traditional method.
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An Extension of Laor Weight Initialization for Deep Time-Series Forecasting: Evidence from Thai Equity Risk Prediction
(2025-09-01)
Petchpol, Katsamapol
;
Boongasame, Laor
This study presents a gradient-informed proxy initialization framework designed to improve training efficiency and predictive performance in deep learning models for time-series forecasting. The method extends the Laor Initialization approach by introducing backward gradient norm clustering as a selection criterion for input-layer weights, evaluated through a lightweight, architecture-agnostic proxy model. Only the numerical input layer adopts the selected initialization, while internal components retain standard schemes such as Xavier, Kaiming, or Orthogonal, maintaining compatibility and reducing overhead. The framework is evaluated on a real-world financial forecasting task: identifying high-risk equities from the Thai Market Surveillance Measure List, a domain characterized by label imbalance, non-stationarity, and limited data volume. Experiments across five architectures, including Transformer, ConvTran, and MMAGRU-FCN, show that the proposed strategy improves convergence speed and classification accuracy, particularly in deeper and hybrid models. Results in recurrent-based models are competitive but less pronounced. These findings support the method’s practical utility and generalizability for forecasting tasks under real-world constraints.
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Combinatorial portfolio selection with the ELECTRE III method: Case study of the Stock Exchange of Thailand (SET)
(2016-11-03)
Boonjing, Veera
;
Boongasame, Laor
Various techniques of portfolio selection are applied to interpret the status of the market and predict the market's future trend, but they are not beneficial to small investors because these techniques should be administered by an expert. In addition, these techniques desire accumulation of data about the market and complicated calculations, which is too much effort for individual small investors. Therefore, portfolio selection with two significant financial ratios using the ELECTRE III method is proposed for these investors to make trading decisions. In order to demonstrate the effectiveness of this new method, it is compared to the situation where a fix percentage allocation existed and data was collected from the Stock Exchange of Thailand (SET).
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Factors affecting gold price prediction and the use of deep learning techniques for gold price prediction
(2022-11-11)
Boongasame, Laor
"Gold" is a term that refers to a collection of precious metals. Gold's bright, shiny yellow color is its defining characteristic. Due to the availability of sophisticated computational techniques such as generalized auto-regressive conditional heteroskedasticity (GARCH), it is possible to perform a more accurate analysis of gold price expression data using deep learning. In this chapter, techniques for predicting the gold price using deep learning are presented. Also talked about were problems that have come up in the field and possible directions for future work.
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A new approach to Multi-criteria Decision Making (MCDM) using the fuzzy binary relation of the ELECTRE III method and the principles of the AHP method
(2010-03-31)
Boongasame, Laor
;
Boonjing, Veera
There are several methods for Multi Criteria Decision Making (MCDM) such as multiple attribute utility theory (MAUT), the analytical hierarchy process (AHP), and Fuzzy AHP. However, these methods are compensatory optimization approaches for which bad score on some criteria can be compensated by excellent scores on other criteria. So, the Elimination and Choice Translating Reality III (ELECTRE III) was proposed to solve such problem. Nevertheless, thresholds determined by identified experts and used in this method may be inconsistent. Therefore, this paper proposes an integrated approach which employs ELECTRE III and partial concepts of AHP together, called the Consistency ELECTRE III. In this method, ELECTRE III is used in ranks the alternatives and AHP is used in determining the consistency of the criteria thresholds within ELECTRE III. In the simulation, it is found that threshold values of criteria within ELECTRE III affect the ranking of the alternatives. Specially, the ranking of the Consistency ELECTRE III and that of the ELECTRE III are different. © 2010 Springer-Verlag Berlin Heidelberg.
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Classification of Thai Rice Varieties Using Image Processing and Deep Learning Techniques
(2025-01-01)
Kongmanee, Panpatsorn
;
Puengpradith, Sorapojana
;
Boongasame, Laor
The methods for identifying Thai rice varieties are complex, time-consuming, and require high expertise to achieve accurate results. This research explores different deep learning techniques to efficiently classify the strains of Thai rice that optimize accuracy and speed. The focus rice varieties are Khao Hom-Mali Thai and Thai Hom Pathum Thani 1 fragrant rice; both have similar shapes and characteristics but differ in price, market value, and recognition. The proposed model is based on an instance segmentation model of YOLOv8, which is compared against popular instance segmentation models such as YOLACT, SOLOv2, and Mask R-CNN. Additionally, hyperparameter tuning is performed to ascertain the most optimal values. The evaluation of the model performance reports in the form of mean average precision (mAP), inference time, and model stability. Experimental results indicate that YOLOv8n-seg, with the fewest parameters, achieves the highest accuracy comparable to other YOLOv8-based models with more parameters. The proposed model demonstrates superior accuracy and processing speed performance compared to other state-of-the-art models.
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A new coalition formation based on reservation prices and locations of buyers
(2013-01-01)
Narabin, Santit
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Boonjing, Veera
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Boongasame, Laor
This paper defines a coalition as a cluster of buyers in dimensional space of reservation price and location. It defines a new buyer utility accounting for discount and traveling costs. A new coalition with nonnegative utility is proposed to maximize a number of successful buyers. This nonnegative utility coalition also assures that a winner does not have to pay more to subsidize others. Simulation results confirm that performance of the new scheme is significantly better than of a random one and is as good as a reservation price based coalition. © 2013 S. Narabin et al.
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Forming buyer coalitions with bundles of items
(2009-09-10)
Boongasame, Laor
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Leung, Ho Fung
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Boonjing, Veera
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Chiu, Dickson K.W.
There are several existing buyer coalition schemes, but they do not consider bundles of items. This research presents an algorithm for forming a buyer coalition with bundles of items, called the GroupBuyPackage scheme, in order to maximize the total discount. Our simulation results show that the total discount of the coalitions in this scheme are close to that in the optimal scheme. © 2009 Springer Berlin Heidelberg.
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Three fakes with deep learning techniques fake news, fake reviewers, and deepfakes: A survey
(2024-10-23)
Boongasame, Laor
The internet has brought convenience to the world. Many people communicate with each other through social media. Truth and lies are among the conveniences that people enjoy. Fake news and fake reviews cause problems for many people, both physically and mentally. For example, mentally, it may cause misunderstandings. Physically, it may lead to incorrect behavior towards the body, such as eating the wrong food or medicine. As a result, this paper presents a survey of research on various false stories that exist on social media. This paper will focus on three distinct topics: 1) fake news; 2) fake reviews; and 3) deepfakes. All three will be surveys of false stories used in deep learning. Some of the three tasks may contain the same information. At the same time, the theory may consist of multiple parts. Next, this chapter will present it in separate parts, hoping that readers will gain an understanding of fake social media and find it useful for future research.
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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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Boongasame, Laor

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