What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Structural engineering
Min-Yuan Cheng mostly deals with Artificial intelligence, Genetic algorithm, Support vector machine, Fuzzy logic and Construction management.
His Artificial intelligence study incorporates themes from Algorithm and Machine learning.
Min-Yuan Cheng has included themes like Cost estimate and Project management in his Genetic algorithm study.
His studies link Inference with Support vector machine.
His Fuzzy logic research is multidisciplinary, relying on both Case-based reasoning and Euclidean distance.
His Construction management research is multidisciplinary, incorporating elements of Database, Process, Systems engineering and Geographic information system.
His most cited work include:
- Symbiotic Organisms Search: A new metaheuristic optimization algorithm (646 citations)
- ArcSite: enhanced GIS for construction site layout (108 citations)
- Integrating barcode and GIS for monitoring construction progress (94 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Inference, Fuzzy logic, Support vector machine and Machine learning.
His Artificial intelligence research includes elements of Genetic algorithm, Mathematical optimization, Data mining and Construction management.
His studies deal with areas such as Differential evolution, Intelligent decision support system, Project management, Process and Process management as well as Construction management.
His Operations research research extends to Fuzzy logic, which is thematically connected.
His research in Support vector machine intersects with topics in Curve fitting, Algorithm and Time series.
When carried out as part of a general Artificial neural network research project, his work on Hybrid neural network is frequently linked to work in Multivariate adaptive regression splines, therefore connecting diverse disciplines of study.
He most often published in these fields:
- Artificial intelligence (32.69%)
- Inference (22.60%)
- Fuzzy logic (19.71%)
What were the highlights of his more recent work (between 2016-2021)?
- Metaheuristic (6.25%)
- Mathematical optimization (14.42%)
- Structural engineering (7.69%)
In recent papers he was focusing on the following fields of study:
Min-Yuan Cheng spends much of his time researching Metaheuristic, Mathematical optimization, Structural engineering, Artificial intelligence and Operations research.
Min-Yuan Cheng interconnects Optimization problem, Benchmark and Engineering design process in the investigation of issues within Metaheuristic.
Min-Yuan Cheng has researched Mathematical optimization in several fields, including Field and Support vector regression model.
His work deals with themes such as Machine learning, Continuous optimization, Engineering optimization and Key, which intersect with Artificial intelligence.
The concepts of his Operations research study are interwoven with issues in Chaotic search and Fuzzy logic.
His work focuses on many connections between Artificial neural network and other disciplines, such as Hyperparameter, that overlap with his field of interest in Inference.
Between 2016 and 2021, his most popular works were:
- BIM integrated smart monitoring technique for building fire prevention and disaster relief (58 citations)
- Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression (31 citations)
- Optimal path planning in real time for dynamic building fire rescue operations using wireless sensors and visual guidance (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Composite material
Min-Yuan Cheng mainly focuses on Mathematical optimization, Benchmark, Support vector machine, Metaheuristic and Emergency management.
The various areas that Min-Yuan Cheng examines in his Mathematical optimization study include Least squares support vector machine and Artificial intelligence.
His Artificial intelligence research integrates issues from Multi-objective optimization, Multi-swarm optimization and Probabilistic-based design optimization.
His Benchmark research incorporates elements of Nature inspired, Constrained optimization and Civil engineering.
His study in Support vector machine is interdisciplinary in nature, drawing from both Supervised learning, Coefficient of determination, Mean absolute percentage error and Algorithm.
His Engineering optimization research incorporates themes from Derivative-free optimization, Global optimization and Fuzzy logic.
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