Title: DEEP LEARNING PREDICTING POWER COSTS FOR POWER PLANNING
<br>Cover Date: 2025-05-01
<br>Cover Display Date: May 2025
<br>DOI: 10.24507/icicelb.16.05.549
<br>Description: 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.
<br>Citations: 0
<br>Aggregation Type: Journal
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<br><br><br>Title: Categorize Level of Crystal Sugar Making with Recurrent Neural Network
<br>Cover Date: 2022-01-01
<br>Cover Display Date: 2022
<br>DOI: 10.1109/JCSSE54890.2022.9836272
<br>Description: This research presents the study of recurrent neural networks to predict industrial crystal sugar making. The recurrent neural network trains on six parameters consisting of liquid in the pan, Brix levels, vacuum in the pan, liquor temperatures, water steam supplier, and current for mix-motor agitator. The input variables were the trained model to predict by categorizing data in three levels high, middle, and low which the data came from human control the sugar boiler machine. The trained model for the future can be extended to make an experience meter to indicate the ability of workers to control the machine.
<br>Citations: 0
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Smart collar design to predict cow behavior
<br>Cover Date: 2020-11-04
<br>Cover Display Date: 4 November 2020
<br>DOI: 10.1109/JCSSE49651.2020.9268342
<br>Description: Productivity from agriculture and farming is one major to drive Thailand's economy. Low price sensors and easy to make the Internet of Thing devices for making the data collector applied in various fields such as industry, communication, and transportation. However, smart application in agriculture does not apply widespread usage. This research proposed the detail design of the smart cow collar to use monitoring cow healthy. Data of walking and scraping of the cow sent to collect on the server and report to the user known the cow healthy by relating to the behavior. The purpose of the device helps the cow keeper reducing the monitoring of cow behaviors and it can predict illness and vital of the cow occurring in the future. For predicting part, design by the data from the gyroscopes after preparing send to Hidden Markov Models to predict cow behavior, however, this paper mainly focuses on the detail building the cow collar and creating the prototype device, then testing on the real environment.
<br>Citations: 4
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Classifying Vehicle Traffic Messages from Twitter to Organize Traffic Services
<br>Cover Date: 2019-05-14
<br>Cover Display Date: 14 May 2019
<br>DOI: 10.1109/IEA.2019.8714777
<br>Description: Communication in social networks as Twitter, Facebook, and Line application is a channel telling a traffic status by a text message. Traffic services organization by peoples monitors messages from the social network in order to take actions when having the accident. This research focuses on creating a system that classify the text-message from social networks, to get the status of traffic, type of accident, level of accident and GPS location in order to consider and take action by the traffic services organization. The designed system in this phase uses text analysis by considering structure and message styles and the fully design applies hidden Markov models to classify the type of the message. Short message from the social network came from Twitter, JS100-radio and FM91.
<br>Citations: 0
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Reinforcement learning applied to scrum team towards large-scale global optimization
<br>Cover Date: 2019-02-02
<br>Cover Display Date: 2 February 2019
<br>DOI: 10.1109/ICSP.2018.8652286
<br>Description: 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.
<br>Citations: 0
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Case study psychological questionnaire evaluated by hidden Markov models
<br>Cover Date: 2017-12-19
<br>Cover Display Date: 19 December 2017
<br>DOI: 10.1109/TENCON.2017.8228312
<br>Description: Psychological testing is a process to know a human feeling or thought in a normal situation or a pressure situation, to know personal behavior. A psychological evaluation technique that generally uses questionnaires wrote by a specialist investigating personal behavior. In this research, we motivate on applying hidden markov models to recognize the personal behavior of human from doing the psychological questionnaire. This paper presented system designs, converting questionnaire to be input for hidden markov models training. The experiment is a limited test on a group of students from a class in the university. The system can split the student by the personal behavior into 7 groups.
<br>Citations: 2
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Introducing scrum process optimization
<br>Cover Date: 2017-11-14
<br>Cover Display Date: 14 November 2017
<br>DOI: 10.1109/ICMLC.2017.8107761
<br>Description: Scrum is a well-known methodology in software development describing as organization in a developer team. A scrum team is a small size with expert members, rich communication, sharing knowledge, self-organization, and self-planning. Scrum process in this version developed by an attempt to create a self-organization team, which an action-reward function integrated in the proposed algorithm. This paper proposed a scrum process creating an optimization algorithm in the class of evolutionary computation. The proposed algorithm experimented on 30 numerical functions by the benchmark coding from the CEC2017 competition problems. The experiment results indicate that uses action-reward function to organize operation planning can help the proposed algorithm finding the best result.
<br>Citations: 2
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Roulette wheel selection applied to PSO on numerical function in discrete and continuous space
<br>Cover Date: 2016-07-22
<br>Cover Display Date: 22 July 2016
<br>DOI: 10.1109/TENCONSpring.2016.7519433
<br>Description: 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.
<br>Citations: 1
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Optimization algorithm using scrum process
<br>Cover Date: 2016-07-02
<br>Cover Display Date: 2 July 2016
<br>DOI: 10.1109/ICMLC.2016.7860908
<br>Description: Scrum process is methodology for software development. Members in a scrum team have self-organizing team by planning and sharing knowledge. This paper introduces optimization algorithm using the population as scrum team doing the scrum process to find an optimum solution. The proposed algorithm maintains the level of exploration and the exploitation search by specific of the scrum-team. The experiment has compared the proposed approach with GA and PSO by finding an optimal solution of five numerical functions. The experiment result indicates that the proposed algorithm provides the best solution and finds the result quickly.
<br>Citations: 4
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Roulette wheel selection to encourage discrete Particle Swarm Optimization solving toll-keeper scheduling problem
<br>Cover Date: 2015-12-30
<br>Cover Display Date: 30 December 2015
<br>DOI: 10.1109/TICST.2015.7369392
<br>Description: Particle Swarm Optimization (PSO) has been proven to solve various applications by most applications using the real problem-space. In some specific problem, the discrete problem-space is selected on PSO that getting the solution result slowly by cause of sticky on local solution, and also the researcher cannot modify or difficult to understand how to improve the performance finding solution. The researcher many be tried to adjust velocity value by giving a new c1, c2 and weight to be a smaller or a larger value. This research proposed how to apply roulette wheel select to improve PSO on the discrete problem-space. Experiment tested the idea by a toll-keeper scheduling and a numerical function. Both problems created parameters inform discrete problem-space. The experiment result showed that PSO with roulette wheel selection taking the solution quickly.
<br>Citations: 2
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Mutual information rough sets feature selection and classification for microarray data analysis
<br>Cover Date: 2014-01-01
<br>Cover Display Date: 2014
<br>DOI: N/A
<br>Description: The feature selection (FS) techniques aim to reduce the subset size of an original data set, which are retained in the most useful information by selecting the most informative feature instead of irrelevant or redundant features. The benefits of FS for classification analysis can reduce the input data, improved predictive accuracy, learned knowledge is that easily understood, and reduced execution time. Many approaches based on rough set theory up to now, have operated the dependency function for measuring the goodness of the feature. However, there is not tolerance to noisy or inconsistency data, especially on high dimensional data microarray data sets. Moreover, mostly relevant information could be invisible by using only information from a positive region but neglecting a boundary region, mostly relevant may be invisible. Therefore, this paper proposes the maximal positive region and minimal boundary region criterion, based on rough set and mutual information, which use the different values among the information contained in the positive region, and the information contained in the boundary region. The experimental results indicate that our proposed method can increase the classification accuracy. © 2014 Pushpa Publishing House, Allahabad, India.
<br>Citations: 0
<br>Aggregation Type: Journal
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<br><br><br>Title: The proposed algorithm for feature selection based on rough set and mutual information
<br>Cover Date: 2014-01-01
<br>Cover Display Date: May 2014
<br>DOI: N/A
<br>Description: The feature selection approaches based on rough set theory aim to reduce the input data for improvement classification accuracy. Most existing approaches have concerned the discernibility relation to find the features, and have employed the dependency function for measuring the goodness of feature. The most relevant information cannot be visible by using information from discernibility relation only, so that neglecting indiscernibility relation, mostly relevant may be invisible. Moreover, their results are not tolerant to noisy or inconsistency data. Therefore, this paper proposes new algorithm based on rough set theory, which concerned both the discernibility and indiscernibility relations. The experimental results show that our approach gives higher classification accuracy than existing approaches. © 2014 Pushpa Publishing House, Allahabad, India.
<br>Citations: 0
<br>Aggregation Type: Journal
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