Nootyasakool, Supakit

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Alternative Name

Nootyaskool, Supakit

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School of Information Technology

supakit.no@kmitl.ac.th

Now showing 1 - 10 of 16

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Hybrid Genetic Algorithm with Baum-Welch Algorithm by using diversity population technique
(2006-12-01)
Nootyaskool, Supakit
;
Kruatrachue, Boontee
Baum-Welch Algorithm (BWA) have used in recognition systems, many researchers have improved BWA performances by using Hybrid Genetic Algorithm (HGA). This paper presents a new HGA technique by using diversity population structure. We surveyed HGA techniques and divided into four types. There were separate processes, population types, fitness determiners, and diversity population structure. A technique of diversity population structure protected applying BWA to similar population. Different population structures make available GA to find optimum point quickly. This paper compared all of HGA techniques, which there trained on Hidden Markov Models (HMM), in an application Thai off-line handwritten recognition, we used database from NECTEC. An experiment of HGA, HMM probability of diversity population techniques get better than techniques of population types 52.65% improvement and there better than techniques of separately process 37.91% improvement. Moreover, HGA experimented five times repeatedly, standard derivation value of diversity population techniques showed closely results. © 2006 IEEE.
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Reinforcement learning applied to scrum team towards large-scale global optimization
(2019-02-02)
Nootyaskool, Supakit
;
Ounsrimuang, Pimolrat
Large-scale problems have size of problem over a thousand dimensions in finding a best solution that uses long computation times. In this work, we use an idea of scrum methodology that is a well-known in software development companies, to create an optimization algorithm. The scrum methodology describing about the team organization likes as rugby team management that player have expert in game. The proposed algorithm is developed based on concept of the evolutionary computation by this work added agent specifics in leaning environment of the problem. The specific of the agent is reinforcement learning by taking an action and getting reward. The proposed algorithm was experimented on a large-scale global optimization finding optimum point of numerical function, comparing between with and without reinforcement learning. The experiment result showed that the usage of reinforcement learning has good results.
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HMM Parameters Optimization using Combine Genetic Algorithm and Iterative Training
(2003-12-01)
Kruatrachue, Boontee
;
Siriboon, Kritawan
;
Nootyaskool, Supakit
HMM have been used extensively for recognizing observation sequence especially in speech recognition. Iterative training procedure such as Baum-Weltch, or gradient techniques are normally used to find locally optimize HMM parameters. This paper presents genetic algorithm (GA) to perform global search for Hidden Markov Model (HMM) parameters that maximize probability of observation sequence given the model. In order to increase the convergence rate and parameters optimization, we combine iterative procedure with GA. The probability of observation sequence of the train model using iterative procedure, GA, and GA with iterative procedure will be compared along with their convergence rates. The test patterns are chain code sequences generated from 38 isolated on-line Thai handwritten characters. The recognition rate and the probability of the train observation sequences of GA were better than the iterative training. The recognition rate of HMM with iterative training 95.05%, GA 97.50% and GA with iterative training 98.41% on 3839 patterns.
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Predicting Fuel Burn with Neural Network to Adjust Contingency Fuel of Airplane
(2023-01-01)
Ounsrimoung, Pimolrat
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Nootyaskool, Supakit
;
Atchariyachanvanich, Kanokwan
;
Yooyen, Soemsak
The amount of fuel in an airplane tank is very important for flying. however, flying a short distance by adding a fuel-full tank is not energy efficient because spending a lot of tons for holding fuel weight. The flight planners who consider the amount of fuel to add to the tank by using historical data, use fuel burn calculating and adjust contingency fuel. This research presents the neural networks to predict fuel burn, which learn from historical airplane data. The experiment applied to local and international flight data and used both Airbus and Boeing. The predicted model was swapped and tested on the outbound and inbound replacements for confirmation capable of the predicted mode.
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Gold Investment Model on RNN and Finding Best Investment Strategy on PSO
(2022-01-01)
Kanchanakantikul, Pakamas
;
Nootyaskool, Supakit
Nowadays, Algorithm trading in community and stock is interesting research, while gold is also an investment option. This research presents two steps. Three inputs sequence consists of the gold price(sell), gold spot and crude oil. Output has an order sequence indicating buy, sell, and wait for the signal. Firstly, finding the best strategy from historical data by particle swarm optimization (PSO) compared with random search (RS). That will get buying, selling, or waiting signals in the gold trading market Secondly, creating gold investment by recurrent neural network (RNN) model. The experiment result showed RNN trading model based on PSO is better than RS, which has a profit of 79.667 percent.
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Roulette wheel selection applied to PSO on numerical function in discrete and continuous space
(2016-07-22)
Ounsrimuang, Pimolrat
;
Nootyaskool, Supakit
Particle Swarm Optimization (PSO) successfully finds a solution as shown in various literatures. In some problems creating on discrete space, adjustment control-parameter may be difficult to modify a reach of optimum solution. The paper proposes an approach applying roulette wheel selection to PSO, which can help PSO escape from a local solution. This approach tested on both continuous and discrete space by finding solution of 12-numerical functions and an engineering-problem. The experiment result showed that the proposed technique can help PSO getting the best result both problem spaces, the performance improvement but also maintain easily to implementation.
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Improved Naive RAG by Integrated Advanced Techniques: A Comprehensive Framework Using Parent-Child Architecture, Hybrid Retrieval, and Contextual Compression
(2026-07-01)
Aromsuk, Tinnarat
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Netisopakul, Ponrudee
;
Nootyaskool, Supakit
This paper presents a novel approach to enhance Retrieval-Augmented Generation (RAG) systems through the integration of three advanced techniques: Parent-Child Architecture, Hybrid Retrieval, and Contextual Compression with cross-encoder re-ranking. We implement this framework using Langchain and FAISS vector search, with Anthropic’s Claude as the foundation model. Our comprehensive evaluation across 14 diverse Wikipedia-based knowledge domains employs the RAGAS framework to measure multiple performance dimensions. Results demonstrate that our advanced framework yields significant improvements in key metrics: Context Precision 10.11%, Context Recall 2.25%,and BLEU scores 1.14% compared to Naive RAG implementations. Domain analysis reveals particularly strong performance in Medicine 8.0% BLEU, Science 4.9%, and specialized Technology domains 4.9%. While, some technical domains such as Cybersecurity (−2.4%) and Biology (−6.6%) show performance degradation. Our framework achieves these improvements with minimal computational overhead by 1.89%,offering a practical approach to implementing domain-adaptive RAG systems that optimize context quality for improved generation performance.
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DEEP LEARNING PREDICTING POWER COSTS FOR POWER PLANNING
(2025-05-01)
Ounsrimung, Pimolrat
;
Phuanphannid, Walaiphan
;
Nootyaskool, Supakit
The current global situation, especially in Thailand, is showing recovery following the easing of COVID-19 restrictions. With a growing population and industrial expansion, there is an increased demand for electricity. This rise in demand, along with factors like fuel costs and fluctuating currency rates, has influenced global power prices. The authors are keen on studying power’s variable price prediction using machine learning techniques. The objective is to analyze factors associated with power costs and their interrelationships. It is essential to have accurate data for strategic power planning. Research has been conducted on theories related to variable power prices, time series, traditional statistical forecasting, machine learning, and deep learning. It was found that three main factors affecting variable power prices were identified: natural gas prices, exchange rates, and inflation rates, with natural gas prices and inflation rates having a strong correlation. Traditional statistical forecasting is less efficient for highly volatile power’s variable price. Deep learning models outperform conventional machine learning for this dataset.
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Hidden Markov Models predict foreign exchange rate
(2015-01-15)
Nootyaskool, Supakit
;
Choengtong, Wuttichow
This paper presents the prediction Thai baht by using Hidden Markov Models (HMM) with which the prediction model uses four factors, dollar index, interest rate, inflation rate and economic growth. The main idea of this work is a technique of encoding four factors into one observation sequence to train HMM. One result of prediction data will present four factors after decoding. The experiment is done using the data-by-day from 2002 to 2013 and showed that the technique has the mean percentage error of 0.167% to predict Thai currency exchange.
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The hybrid implementation genetic algorithm with particle swarm optimization to solve the unconstrained optimization problems
(2012-10-26)
Nootyaskool, Supakit
Genetic algorithm (GA) has an advantage in exploration search. Particle swarm optimization (PSO) has an advantage in sharing movement information between particles. The combining between GA and PSO is proposed in this research. We design hybrid-GA with PSO, and compare the performance with simple GA and simple PSO, which their models will find the solution of five-difference complexity of numerical functions. The experiment result showed that hybrid GA with PSO can find the solution of a multimodal problem and unimodal with noise signal quickly. © 2012 IEEE.

Nootyasakool, Supakit

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