His primary areas of investigation include Support vector machine, Data mining, Artificial intelligence, Machine learning and Least squares. Nhat-Duc Hoang has included themes like Differential evolution and Metaheuristic in his Support vector machine study. Nhat-Duc Hoang combines subjects such as C4.5 algorithm, Soft computing, Spatial prediction and Least squares support vector machine with his study of Data mining.
His study in Artificial intelligence concentrates on Artificial neural network, Digital image, Image processing and Thresholding. Nhat-Duc Hoang interconnects Feature extraction and Random forest in the investigation of issues within Image processing. His research in Least squares intersects with topics in Firefly algorithm and Receiver operating characteristic.
Nhat-Duc Hoang mostly deals with Artificial intelligence, Support vector machine, Data mining, Machine learning and Artificial neural network. His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with issues in Image texture, Image and Moment. His Support vector machine research is multidisciplinary, incorporating perspectives in Random forest, Structural engineering, Differential evolution and Least squares.
The Data mining study combines topics in areas such as Inference, C4.5 algorithm, Soft computing, Supervised learning and Receiver operating characteristic. His work on Relevance vector machine and Firefly algorithm as part of general Machine learning research is frequently linked to Process, bridging the gap between disciplines. His study in the field of Backpropagation also crosses realms of Topographic Wetness Index, Flash flood and Geographic information system.
His primary areas of investigation include Artificial intelligence, Artificial neural network, Support vector machine, Metaheuristic and Algorithm. The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence. His study in Artificial neural network is interdisciplinary in nature, drawing from both Statistical hypothesis testing, Robustness and Nonlinear system.
His Support vector machine research is multidisciplinary, incorporating elements of Relevance, Data mining, Differential evolution and Hazard. In his works, Nhat-Duc Hoang conducts interdisciplinary research on Data mining and Precipitation. His work deals with themes such as Swarm intelligence and Particle swarm optimization, which intersect with Metaheuristic.
Nhat-Duc Hoang mainly focuses on Algorithm, Flash flood, Geographic information system, Random forest and Atterberg limits. His Flash flood study spans across into subjects like Tree, Firefly protocol, Subspace topology, Data mining and Elevation. As part of his studies on Data mining, Nhat-Duc Hoang often connects relevant areas like Artificial neural network.
The Random forest study which covers F1 score that intersects with Support vector machine. His Support vector machine study introduces a deeper knowledge of Artificial intelligence. His Mean squared error study incorporates themes from Particle swarm optimization and Coefficient of determination.
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Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization
Dieu Tien Bui;Dieu Tien Bui;Tran Anh Tuan;Nhat Duc Hoang;Nguyen Quoc Thanh.
A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area.
Dieu Tien Bui;Nhat-Duc Hoang;Francisco Martínez-Álvarez;Phuong-Thao Thi Ngo.
Science of The Total Environment (2020)
Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam
Dieu Tien Bui;Binh Thai Pham;Quoc Phi Nguyen;Nhat-Duc Hoang.
International Journal of Digital Earth (2016)
A novel fuzzy K -nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS
Dieu Tien Bui;Dieu Tien Bui;Quoc Phi Nguyen;Nhat-Duc Hoang;Harald Klempe.
Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and Multi-layer Perceptron Neural Network
Dieu Tien Bui;Viet-Ha Nhu;Nhat-Duc Hoang.
Advanced Engineering Informatics (2018)
A novel method for asphalt pavement crack classification based on image processing and machine learning
Nhat-Duc Hoang;Quoc-Lam Nguyen.
Engineering With Computers (2019)
Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding
Nhat Duc Hoang.
Advances in Civil Engineering (2018)
Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study
Nhat-Duc Hoang;Dieu Tien Bui.
Bulletin of Engineering Geology and the Environment (2018)
Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment
Nhat-Duc Hoang;Anh-Duc Pham.
Expert Systems With Applications (2016)
A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides
Dieu Tien Bui;Himan Shahabi;Ataollah Shirzadi;Kamran Chapi.
Remote Sensing (2018)
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