What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Cryptography
His primary areas of investigation include Encryption, Theoretical computer science, Cryptosystem, Algorithm and Image.
His work carried out in the field of Encryption brings together such families of science as Permutation, Cryptography and Compressed sensing.
His Theoretical computer science research includes elements of Ciphertext-only attack, Pixel and Probabilistic encryption.
His studies deal with areas such as Stream cipher, Communication channel, Chaotic map and Confusion and diffusion as well as Cryptosystem.
His Algorithm research is multidisciplinary, incorporating perspectives in Chaotic, Encoding and Robustness.
His study in Image is interdisciplinary in nature, drawing from both Homomorphic encryption, Probabilistic logic and Homomorphism.
His most cited work include:
- An image encryption scheme based on rotation matrix bit-level permutation and block diffusion (154 citations)
- Double optical image encryption using discrete Chirikov standard map and chaos-based fractional random transform (141 citations)
- Exploiting self-adaptive permutation–diffusion and DNA random encoding for secure and efficient image encryption (138 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Encryption, Algorithm, Image, Compressed sensing and Theoretical computer science.
His Encryption study integrates concerns from other disciplines, such as Chaotic, Artificial intelligence and Key.
In his work, Phase is strongly intertwined with Encoding, which is a subfield of Algorithm.
The Image study combines topics in areas such as CHAOS and Data mining.
His Compressed sensing course of study focuses on Decoding methods and Distributed computing.
His research integrates issues of Plaintext, Ciphertext-only attack, Probabilistic encryption, Key schedule and Robustness in his study of Theoretical computer science.
He most often published in these fields:
- Encryption (86.79%)
- Algorithm (50.94%)
- Image (41.51%)
What were the highlights of his more recent work (between 2019-2021)?
- Encryption (86.79%)
- Artificial intelligence (32.70%)
- Cipher (30.82%)
In recent papers he was focusing on the following fields of study:
Yushu Zhang mainly investigates Encryption, Artificial intelligence, Cipher, Image and Computer vision.
His Encryption research is multidisciplinary, incorporating elements of Algorithm, Compressed sensing, Hash function and Chaotic.
He combines subjects such as Permutation and Confusion and diffusion with his study of Chaotic.
In Cipher, he works on issues like Color image, which are connected to Local binary patterns, Grayscale, Genetic algorithm, Pattern recognition and Crossover.
When carried out as part of a general Image research project, his work on Thumbnail is frequently linked to work in Usability, therefore connecting diverse disciplines of study.
He works mostly in the field of Computer vision, limiting it down to concerns involving Embedding and, occasionally, Pixel.
Between 2019 and 2021, his most popular works were:
- An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding (43 citations)
- An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding (43 citations)
- An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding (43 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Cryptography
His scientific interests lie mostly in Encryption, Artificial intelligence, Hash function, Computer vision and Cipher.
His research in Encryption intersects with topics in Algorithm, Compressed sensing, Image and Chaotic.
His Compressed sensing research integrates issues from Image compression and Feature.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Threat model.
His research brings together the fields of Embedding and Computer vision.
His Cipher research incorporates themes from Pixel, Grayscale, Kronecker product, Color image and Local binary patterns.
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