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Mr. nhat truong
National Taiwan University of Science and Technology, Department of Chemical Engineering, 43 Sec.4, Keelung Rd., Taipei, 106, Taiwan, R.O.C

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0 Civil Engineering
0 Construction Management
0 Machine Learning
0 Project Management
0 MetaHeuristic Algorigthm

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Journal article
Published: 23 March 2021 in Mathematics
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Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.

ACS Style

Jui-Sheng Chou; Dinh-Nhat Truong; Chih-Fong Tsai. Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics. Mathematics 2021, 9, 686 .

AMA Style

Jui-Sheng Chou, Dinh-Nhat Truong, Chih-Fong Tsai. Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics. Mathematics. 2021; 9 (6):686.

Chicago/Turabian Style

Jui-Sheng Chou; Dinh-Nhat Truong; Chih-Fong Tsai. 2021. "Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics." Mathematics 9, no. 6: 686.

Journal article
Published: 13 February 2021 in Energy
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To increase the efficiency of energy use, ensure the stability of the power supply, and achieve balance in the energy supply, power management units have proposed plans that integrate energy-saving with intelligent systems, in which smart grids are used to distribute power and to manage power consumption. Imagery deep learning technology is proposed to address the knowledge gap, and highly accurate energy consumption predictions can be made by converting the 1-D time-series and features to 2-D images for visual recognition. Models based on machine learning and convolutional neural networks (CNNs) were used to predict future power consumption. Performance indicators were evaluated to determine the prediction accuracy and identify the best model for predicting power consumption. A metaheuristic—Jellyfish Search (JS)—is incorporated into the best model to optimize its hyperparameters to ensure model accuracy and stability. After the hybrid JS-CNNs model was constructed, validation was carried out. The analytical results provide insights into the formulation of energy policy for management units and can help power supply agencies to distribute regional power in a way that minimizes unnecessary energy loss. This study contributes to the prediction of future energy consumption trends, reveals power consumption patterns in cities and counties across a nation.

ACS Style

Jui-Sheng Chou; Dinh-Nhat Truong; Ching-Chiun Kuo. Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning. Energy 2021, 224, 120100 .

AMA Style

Jui-Sheng Chou, Dinh-Nhat Truong, Ching-Chiun Kuo. Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning. Energy. 2021; 224 ():120100.

Chicago/Turabian Style

Jui-Sheng Chou; Dinh-Nhat Truong; Ching-Chiun Kuo. 2021. "Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning." Energy 224, no. : 120100.

Research article
Published: 03 November 2020 in International Journal of Energy Research
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This study aims to develop a novel forecasting system that optimizes linear time‐series with nonlinear machine learning models to identify the historical pattern of regional energy consumption. The linear time‐series model, Seasonal AutoRegressive Integrated Moving Average (SARIMA), was applied to simulate the linear component, while the least squares support vector regression (LSSVR) model was used to capture the nonlinear component of time series energy data and combine the linear and nonlinear components. Several optimization algorithms were investigated using high‐dimension mathematical benchmark functions to compare their outcomes for applying in the proposed forecasting system. Wilcoxon rank‐sum test and convergence graphs for Jellyfish Search (JS) and other parameter‐less algorithms (ie, Teaching‐Learning‐Based Optimization (TLBO) and Symbiotic Organisms Search, SOS algorithms) indicate that the JS optimizer finds the best global solutions in mathematical tests. Then, three real energy time‐series datasets from a regional transmission organization that coordinates the movement of wholesale electricity were used to evaluate various forecasting methods. The analytical results confirm that the proposed system, JS‐LSSVR(SARIMA, LSSVR), can predict multi‐step ahead time series energy consumption with higher accuracy than the linear model (ie, SARIMA), nonlinear model (ie, LSSVR), hybrid model (ie, JS‐LSSVR), hybrid systems (ie, TLBO‐LSSVR(SARIMA, LSSVR) and TLBO‐LSSVR(SARIMA, LSSVR)), and prior studies. Numerical experiments show that the JS‐LSSVR(SARIMA, LSSVR) system can forecast energy consumption 1 week ahead efficiently (from 9.8 to 21.4 seconds on average). Notably, the proposed technique only requires 4 inputs vs 7 to 32 inputs of other models in the literature. Thus, a power company can apply the proposed system to forecast energy consumption to efficiently dispatch regional energy capacity and keep the electricity supply and demand in balance for residential buildings in sustainable cities.

ACS Style

Jui‐Sheng Chou; Dinh‐Nhat Truong. Multistep energy consumption forecasting by metaheuristic optimization of time‐series analysis and machine learning. International Journal of Energy Research 2020, 45, 4581 -4612.

AMA Style

Jui‐Sheng Chou, Dinh‐Nhat Truong. Multistep energy consumption forecasting by metaheuristic optimization of time‐series analysis and machine learning. International Journal of Energy Research. 2020; 45 (3):4581-4612.

Chicago/Turabian Style

Jui‐Sheng Chou; Dinh‐Nhat Truong. 2020. "Multistep energy consumption forecasting by metaheuristic optimization of time‐series analysis and machine learning." International Journal of Energy Research 45, no. 3: 4581-4612.

Journal article
Published: 07 August 2020 in Applied Mathematics and Computation
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This study develops a novel metaheuristic algorithm that is motivated by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer. The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom. JS optimizer is tested using a comprehensive set of mathematical benchmark functions and applied to a series of structural engineering problems. Fifty small/average-scale and twenty-five large-scale functions involving various dimensions were used to validate JS optimizer, which was compared with ten well-known metaheuristic algorithms. JS optimizer was found to outperform those algorithms in solving mathematical benchmark functions. The JS algorithm was then used to solve structural optimization problems, including 25-bar tower design, 52-bar tower design and 582-bar tower design problems. In those cases, JS not only performed best but also required the fewest evaluations of objective functions. Therefore, JS is potentially an excellent metaheuristic algorithm for solving optimization problems.

ACS Style

Jui-Sheng Chou; Dinh-Nhat Truong. A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation 2020, 389, 125535 .

AMA Style

Jui-Sheng Chou, Dinh-Nhat Truong. A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation. 2020; 389 ():125535.

Chicago/Turabian Style

Jui-Sheng Chou; Dinh-Nhat Truong. 2020. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean." Applied Mathematics and Computation 389, no. : 125535.

Journal article
Published: 30 April 2020 in Chaos, Solitons & Fractals
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This study develops a Multi-Objective Jellyfish Search (MOJS) algorithm to solve engineering problems optimally with multiple objectives. Lévy flight, elite population, fixed-size archive, chaotic map, and the opposition-based jumping method are integrated into the MOJS to obtain the Pareto optimal solutions. These techniques are employed to define the motions of jellyfish in an ocean current or a swarm in multi-objective search spaces. The proposed algorithm is tested on 20 multi-objective mathematical benchmark problems, and compared with six well-known metaheuristic optimization algorithms (MOALO, MODA, MOEA/D, MOGWO, MOPSO, and NSGA-II). The results thus obtained indicate that the MOJS finds highly accurate approximations to Pareto-optimal fronts with a uniform distribution of solutions for the test functions. Three constrained structural problems (25-bar tower design, 160-bar tower design, and 942-bar tower design) of minimizing structural weight and maximum nodal deflection were solved using MOJS. The visual analytics demonstrates the merits of MOJS in solving real engineering problems with best Pareto-optimal fronts. Accordingly, the MOJS is an effective and efficient algorithm for solving multi-objective optimization problems.

ACS Style

Jui-Sheng Chou; Dinh-Nhat Truong. Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems. Chaos, Solitons & Fractals 2020, 135, 109738 .

AMA Style

Jui-Sheng Chou, Dinh-Nhat Truong. Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems. Chaos, Solitons & Fractals. 2020; 135 ():109738.

Chicago/Turabian Style

Jui-Sheng Chou; Dinh-Nhat Truong. 2020. "Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems." Chaos, Solitons & Fractals 135, no. : 109738.

Original paper
Published: 21 March 2020 in Natural Hazards
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This work develops a novel metaheuristic optimization-based least squares support vector regression (LSSVR) model with a multi-output (MO) algorithm for assessing natural hazards. The MO algorithm is more efficient than the single-output algorithm because the relations among outputs can be estimated simultaneously by the proposed prediction model. Furthermore, the hyperparameters in MOLSSVR are optimized using an accelerated particle swarm optimization (APSO) algorithm combined with a self-tuning method to generate the best predictions and the fastest convergence. The APSO algorithm is validated by solving benchmark functions with unimodal and multimodal characteristics. The performance of APSO-MOLSSVR is compared with those of hybrid and single models yielded from standard multi-input single-output algorithms. A graphical user interface was designed as a stand-alone application to provide a user-friendly system for executing advanced data mining techniques. In real-world engineering cases, APSO-MOLSSVR achieved an error rate that was up to 63.55% better than those achieved using prediction models that are proposed in the single-output scheme. The system much more quickly and efficiently identified the optimal parameters and effectively solved multiple-output problems.

ACS Style

Jui-Sheng Chou; Dinh-Nhat Truong; Yonatan Che. Optimized multi-output machine learning system for engineering informatics in assessing natural hazards. Natural Hazards 2020, 101, 727 -754.

AMA Style

Jui-Sheng Chou, Dinh-Nhat Truong, Yonatan Che. Optimized multi-output machine learning system for engineering informatics in assessing natural hazards. Natural Hazards. 2020; 101 (3):727-754.

Chicago/Turabian Style

Jui-Sheng Chou; Dinh-Nhat Truong; Yonatan Che. 2020. "Optimized multi-output machine learning system for engineering informatics in assessing natural hazards." Natural Hazards 101, no. 3: 727-754.

Journal article
Published: 10 January 2020 in IEEE Access
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By providing a range of values rather than a point estimate, accurate interval forecasting is critical to the success of investment decisions in exchange rate markets. This work proposes a sliding-window metaheuristic optimization for interval-valued time series forecasting using multi-output least squares support vector regression (MLSSVR). The hyperparameters in MLSSVR are optimized using an accelerated particle swarm optimization algorithm to yield the best predictions and fastest convergence. The proposed system has a graphical user interface that is developed in a computing environment and functions as a stand-alone application. The system is validated using stock prices and exchange rates and outputs are compared with published results. Finally, the proposed interval time series prediction method is tested in two case studies; one involves the daily Australian dollar and Japanese yen rates (AUD/JPY) and the other involves US dollar and Canadian dollar rates (USD/CAD). The proposed model is promising for interval time series forecasting.

ACS Style

Jui-Sheng Chou; Nhat Truong; Thuy-Linh Le. Interval Forecasting of Financial Time Series by Accelerated Particle Swarm-Optimized Multi-Output Machine Learning System. IEEE Access 2020, 8, 14798 -14808.

AMA Style

Jui-Sheng Chou, Nhat Truong, Thuy-Linh Le. Interval Forecasting of Financial Time Series by Accelerated Particle Swarm-Optimized Multi-Output Machine Learning System. IEEE Access. 2020; 8 (99):14798-14808.

Chicago/Turabian Style

Jui-Sheng Chou; Nhat Truong; Thuy-Linh Le. 2020. "Interval Forecasting of Financial Time Series by Accelerated Particle Swarm-Optimized Multi-Output Machine Learning System." IEEE Access 8, no. 99: 14798-14808.

Journal article
Published: 04 December 2019 in Sustainability
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The main goal of the analysis of microbial ecology is to understand the relationship between Earth’s microbial community and their functions in the environment. This paper presents a proof-of-concept research to develop a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the microbial species in a river as a function of physicochemical parameters. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of physicochemical attributes from a river in Southeast China. A bio-inspired metaheuristic optimized machine learner, which supports the adjustment to the multiple-output prediction form, is used in bioclimatic modeling. The accuracy of prediction and applicability of the model can help microbiologists and ecologists in quantifying the predicted microbial species for further experimental planning with minimal expenditure, which is become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming. This work demonstrates a neoteric approach for potential use in predicting preliminary microbial structures in the environment.

ACS Style

Jui-Sheng Chou; Chang-Ping Yu; Dinh-Nhat Truong; Billy Susilo; Anyi Hu; Qian Sun. Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning. Sustainability 2019, 11, 6889 .

AMA Style

Jui-Sheng Chou, Chang-Ping Yu, Dinh-Nhat Truong, Billy Susilo, Anyi Hu, Qian Sun. Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning. Sustainability. 2019; 11 (24):6889.

Chicago/Turabian Style

Jui-Sheng Chou; Chang-Ping Yu; Dinh-Nhat Truong; Billy Susilo; Anyi Hu; Qian Sun. 2019. "Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning." Sustainability 11, no. 24: 6889.

Journal article
Published: 06 July 2018 in Transport Policy
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The Taiwan high-speed rail (HSR) markedly reduces the travel time from the north of Taiwan to the south, or vice versa, relative to other modes of public overland transportation. The HSR is faster than those modes, but also more expensive to ride. The pricing of HSR tickets has gained limited public acceptance because it lacks justification, indicating the need for a theoretical method for objectively justifying the ticket prices. With continuing improvements in data analytics, the computational capacity of computers, and visualization techniques, constructing a time–space model of a long-distance transportation system has become increasingly feasible, and such a model can be used to examine the time–space compression of the HSR. The first part of this investigation uses multidimensional scaling to obtain fitting coordinates based on travel times for various combinations of departure/destination HSR stations, and a geographic information system to generate time–space maps of the relative locations of those stations. Through these maps, we can directly estimate the traveling time between pairs of stations. The second part constructs a floating ticket-pricing model that accounts for the riding costs of the HSR. The model's power to explain the prices of HSR tickets is evaluated. Based on the analytical results, suggestions to the current HSR ticket fare were proposed to set the feasible rate concerning the operating, passenger-perceived, and time-space compression costs. Recommendations for future research are made.

ACS Style

Jui-Sheng Chou; Ya-Ling Chien; Ngoc-Mai Nguyen; Dinh-Nhat Truong. Pricing policy of floating ticket fare for riding high speed rail based on time-space compression. Transport Policy 2018, 69, 179 -192.

AMA Style

Jui-Sheng Chou, Ya-Ling Chien, Ngoc-Mai Nguyen, Dinh-Nhat Truong. Pricing policy of floating ticket fare for riding high speed rail based on time-space compression. Transport Policy. 2018; 69 ():179-192.

Chicago/Turabian Style

Jui-Sheng Chou; Ya-Ling Chien; Ngoc-Mai Nguyen; Dinh-Nhat Truong. 2018. "Pricing policy of floating ticket fare for riding high speed rail based on time-space compression." Transport Policy 69, no. : 179-192.