What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
Artificial intelligence, Computer vision, Algorithm, Deep learning and Pixel are his primary areas of study.
His Artificial intelligence research incorporates themes from Machine learning, Data mining, Secure multi-party computation and Secret sharing.
His Computer vision research is multidisciplinary, relying on both Homomorphic secret sharing and Pattern recognition.
The various areas that he examines in his Algorithm study include Scheme and Collision.
His study in Pixel is interdisciplinary in nature, drawing from both Time complexity, Hypercube, Distance transform, Binary image and Encryption.
Shi-Jinn Horng combines subjects such as Image processing, Biclustering, Correlation clustering, Cluster analysis and Euclidean distance with his study of Parallel algorithm.
His most cited work include:
- Editorial : Trust, security, and privacy in next-generation wireless sensor networks (90 citations)
- Copyright Protection for E-Government Document Images (68 citations)
- A Rough Set-Based Method for Updating Decision Rules on Attribute Values’ Coarsening and Refining (57 citations)
What are the main themes of his work throughout his whole career to date?
Shi-Jinn Horng mainly focuses on Algorithm, Parallel algorithm, Artificial intelligence, Parallel computing and Image processing.
His Algorithm research focuses on subjects like Embedding, which are linked to Combinatorics and Hypercube.
His Parallel algorithm research incorporates elements of Time complexity, Computational complexity theory, Distance transform, Binary image and Computation.
His work deals with themes such as Machine learning, Computer vision and Pattern recognition, which intersect with Artificial intelligence.
His study in the field of Parallel processing is also linked to topics like Bus network.
The concepts of his Image processing study are interwoven with issues in Pixel, Histogram and Euclidean distance.
He most often published in these fields:
- Algorithm (27.10%)
- Parallel algorithm (27.10%)
- Artificial intelligence (23.36%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (23.36%)
- Data mining (11.68%)
- Deep learning (3.27%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Data mining, Deep learning, Algorithm and Rough set.
His Artificial intelligence research includes elements of Machine learning, Computer vision and Pattern recognition.
His research integrates issues of Algorithm design, Missing data, Imputation and Weighted least squares method in his study of Data mining.
Shi-Jinn Horng focuses mostly in the field of Deep learning, narrowing it down to topics relating to Time series and, in certain cases, Multivariate statistics and Intrusion detection system.
Shi-Jinn Horng is involved in the study of Algorithm that focuses on Secret sharing in particular.
His Rough set research includes themes of Incremental learning, Variation, Granularity and Reduction.
Between 2013 and 2021, his most popular works were:
- A Rough Set-Based Method for Updating Decision Rules on Attribute Values’ Coarsening and Refining (57 citations)
- An incremental approach for attribute reduction based on knowledge granularity (33 citations)
- A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Machine learning, Data mining and Traffic flow.
His biological study spans a wide range of topics, including Computer vision and Pattern recognition.
His Pattern recognition study combines topics from a wide range of disciplines, such as Measure, Inference, Cluster analysis and Curse of dimensionality.
The study incorporates disciplines such as Time series, Temporal database, Intrusion detection system, Feature learning and Multivariate statistics in addition to Deep learning.
His Machine learning research is multidisciplinary, relying on both Entropy, Data set and Decision rule.
His Data mining research incorporates themes from Algorithm design and Missing data.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
