What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Project management
Data mining, Support vector machine, Artificial neural network, Artificial intelligence and Project management are his primary areas of study.
Big data and Anomaly detection is closely connected to Autoregressive integrated moving average in his research, which is encompassed under the umbrella topic of Data mining.
The various areas that Jui-Sheng Chou examines in his Support vector machine study include Ensemble forecasting, Firefly algorithm and Metaheuristic.
His studies deal with areas such as Predictive modelling and Linear regression as well as Artificial neural network.
His work on Case-based reasoning as part of his general Artificial intelligence study is frequently connected to Budget allocation, thereby bridging the divide between different branches of science.
His Project management study integrates concerns from other disciplines, such as Infrastructure asset management, Cost estimate and Operations research.
His most cited work include:
- Modeling heating and cooling loads by artificial intelligence for energy-efficient building design (170 citations)
- Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques (134 citations)
- Cross-country comparisons of key drivers, critical success factors and risk allocation for public-private partnership projects (120 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Support vector machine, Project management, Data mining and Machine learning.
He specializes in Artificial intelligence, namely Ensemble forecasting.
His study in Support vector machine is interdisciplinary in nature, drawing from both Artificial neural network, Mean absolute percentage error, Metaheuristic, Hyperparameter and Least squares.
His research in Artificial neural network intersects with topics in Decision tree and Linear regression.
His Metaheuristic course of study focuses on Firefly algorithm and Chaotic and Global optimization.
His Project management research incorporates themes from Knowledge management, Cost estimate, Process and Operations research.
He most often published in these fields:
- Artificial intelligence (22.92%)
- Support vector machine (21.53%)
- Project management (19.44%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (22.92%)
- Metaheuristic (15.97%)
- Support vector machine (21.53%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Metaheuristic, Support vector machine, Machine learning and Dredging.
Jui-Sheng Chou integrates Artificial intelligence with Energy policy in his study.
Jui-Sheng Chou combines subjects such as Optimization problem, Swarm behaviour and Benchmark with his study of Metaheuristic.
The study incorporates disciplines such as Convergence, Data mining and Word error rate in addition to Benchmark.
His Support vector machine research includes themes of Empirical modelling, Shear strength, Hyperparameter, Test data and Least squares.
Jui-Sheng Chou works on Machine learning which deals in particular with Ensemble forecasting.
Between 2019 and 2021, his most popular works were:
- Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings (16 citations)
- A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean (13 citations)
- FBI inspired meta-optimization (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Project management
Jui-Sheng Chou focuses on Support vector machine, Time series, Artificial intelligence, Benchmark and Swarm behaviour.
In his research on the topic of Support vector machine, Firefly algorithm, Mean absolute percentage error, Beam, Structural engineering and Shear strength is strongly related with Hyperparameter.
His Time series research integrates issues from Exchange rate, Liberian dollar and Metaheuristic optimization.
His work in the fields of Meta-optimization, Robustness and IEEE Congress on Evolutionary Computation overlaps with other areas such as Job shop scheduling and Energy policy.
His Benchmark study incorporates themes from Optimization problem, Metaheuristic and Engineering design process.
His Metaheuristic research is multidisciplinary, incorporating perspectives in Supervised learning, Ensemble forecasting, Machine learning and Energy management.
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