His primary areas of study are Artificial intelligence, Pattern recognition, Parallel computing, CAD and Population. His research ties Computer vision and Artificial intelligence together. His work carried out in the field of Pattern recognition brings together such families of science as Image and BitTorrent tracker.
His research integrates issues of Algorithm, Optimization problem, Ant colony optimization algorithms and General-purpose computing on graphics processing units in his study of Parallel computing. In his study, which falls under the umbrella issue of CAD, User interface and Collaborative software is strongly linked to Human–computer interaction. His Mixture model research includes themes of Data mining and Feature.
His primary areas of investigation include Artificial intelligence, CAD, Computer vision, Consistency and Feature. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition. The concepts of his Pattern recognition study are interwoven with issues in Video tracking, BitTorrent tracker and Computer graphics.
His study in CAD is interdisciplinary in nature, drawing from both Software engineering, Computer Aided Design, Cloud computing and Distributed computing. His Consistency research also works with subjects such as
Fazhi He mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Feature. His research on Artificial intelligence frequently links to adjacent areas such as Machine learning. His work in Image segmentation and Pixel are all subfields of Computer vision research.
His research investigates the connection with Pattern recognition and areas like Computer graphics which intersect with concerns in Residual and Process. He combines subjects such as Software, Hardware software, Cluster analysis and Benchmark with his study of Algorithm. As a member of one scientific family, Fazhi He mostly works in the field of Feature, focusing on Differential evolution and, on occasion, Optimization algorithm, Data mining and Dual.
Fazhi He mostly deals with Artificial intelligence, Computer vision, Machine learning, Recommender system and Autoencoder. Fazhi He has researched Artificial intelligence in several fields, including Residual and Pattern recognition. He interconnects Process, Subnetwork, Task, Computer graphics and Image in the investigation of issues within Pattern recognition.
His Image segmentation, Segmentation, Pixel and Feature study are his primary interests in Computer vision. In general Machine learning study, his work on Artificial neural network often relates to the realm of Matrix decomposition, Social influence, Social media and Learning to rank, thereby connecting several areas of interest. His Recommender system research is multidisciplinary, incorporating perspectives in Structure, Layer, Noise and Set.
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Service-Oriented Feature-Based Data Exchange for Cloud-Based Design and Manufacturing
Yiqi Wu;Fazhi He;Dejun Zhang;Xiaoxia Li.
IEEE Transactions on Services Computing (2018)
Dynamic strategy based parallel ant colony optimization on GPUs for TSPs
Yi Zhou;Yi Zhou;Fazhi He;Yimin Qiu.
Science in China Series F: Information Sciences (2017)
DRCDN: learning deep residual convolutional dehazing networks
Shengdong Zhang;Fazhi He.
The Visual Computer (2020)
A novel region-based active contour model via local patch similarity measure for image segmentation
Haiping Yu;Fazhi He;Yiteng Pan.
Multimedia Tools and Applications (2018)
A method and tool for human-human interaction and instant collaboration in CSCW-based CAD
Fazhi He;Soonhung Han.
Computers in Industry (2006)
3D mesh simplification with feature preservation based on Whale Optimization Algorithm and Differential Evolution
Yaqian Liang;Fazhi He;Xiantao Zeng.
Integrated Computer-aided Engineering (2020)
Learning social representations with deep autoencoder for recommender system
Yiteng Pan;Fazhi He;Haiping Yu.
World Wide Web (2020)
A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation
Haiping Yu;Haiping Yu;Fazhi He;Yiteng Pan.
Multimedia Tools and Applications (2020)
A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation
Haiping Yu;Fazhi He;Yiteng Pan.
Multimedia Tools and Applications (2019)
A full migration BBO algorithm with enhanced population quality bounds for multimodal biomedical image registration
Yilin Chen;Fazhi He;Haoran Li;Dejun Zhang.
Applied Soft Computing (2020)
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