What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
Yicong Zhou focuses on Encryption, Artificial intelligence, Algorithm, Theoretical computer science and Pattern recognition.
His Encryption study integrates concerns from other disciplines, such as Image and Scrambling.
The study incorporates disciplines such as Machine learning, Noise measurement and Computer vision in addition to Artificial intelligence.
His Algorithm study combines topics from a wide range of disciplines, such as Chaotic, Entropy, Transfer entropy, Joint entropy and Robustness.
Yicong Zhou has researched Theoretical computer science in several fields, including Information theory and measure theory, Rényi entropy and Permutation.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Salt-and-pepper noise, Median filter, Non-local means and Gaussian noise.
His most cited work include:
- A new 1D chaotic system for image encryption (417 citations)
- 2D Sine Logistic modulation map for image encryption (332 citations)
- Image encryption using 2D Logistic-adjusted-Sine map (297 citations)
What are the main themes of his work throughout his whole career to date?
Yicong Zhou mainly focuses on Artificial intelligence, Pattern recognition, Algorithm, Computer vision and Encryption.
His research brings together the fields of Machine learning and Artificial intelligence.
His work in Pattern recognition tackles topics such as Quaternion which are related to areas like Channel.
His Algorithm research incorporates themes from Chaotic, Digital image and Discrete cosine transform.
His Chaotic research integrates issues from Security analysis, CHAOS, Pseudorandom number generator and Sine.
His research investigates the link between Encryption and topics such as Theoretical computer science that cross with problems in Permutation.
He most often published in these fields:
- Artificial intelligence (62.22%)
- Pattern recognition (39.11%)
- Algorithm (29.33%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (62.22%)
- Pattern recognition (39.11%)
- Algorithm (29.33%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Algorithm, Robustness and Encryption.
His work deals with themes such as Sparse matrix and Computer vision, which intersect with Artificial intelligence.
His work carried out in the field of Pattern recognition brings together such families of science as Quaternion, Outlier and Tensor.
His work in the fields of Computation and JPEG overlaps with other areas such as Domain adaptation.
The concepts of his Encryption study are interwoven with issues in Image, Scrambling and Permutation.
The various areas that Yicong Zhou examines in his Permutation study include Cryptosystem, Theoretical computer science and Digital image.
Between 2019 and 2021, his most popular works were:
- Prior Knowledge-Based Probabilistic Collaborative Representation for Visual Recognition (42 citations)
- Two-Dimensional Sine Chaotification System With Hardware Implementation (31 citations)
- Low-Rank Quaternion Approximation for Color Image Processing (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Chaotic, Algorithm and Encryption.
His Artificial intelligence research includes themes of Iterative method, Sparse matrix and Tensor.
His Pattern recognition study incorporates themes from Pixel, Quaternion, Kernel and Color image.
The Chaotic study combines topics in areas such as Collapse, Topology, Noise and Bifurcation.
His studies in Algorithm integrate themes in fields like Manifold, Convolution and Graph embedding, Graph.
His research integrates issues of Control system, Image and Scrambling in his study of Encryption.
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