What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Computer vision
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Computer network and Wireless sensor network.
In the field of Artificial intelligence, his study on Image segmentation, Feature extraction and Support vector machine overlaps with subjects such as License.
His study in Feature extraction is interdisciplinary in nature, drawing from both Denial-of-service attack, Data mining and Network security.
His work carried out in the field of Computer vision brings together such families of science as Discriminative model and Benchmark.
The concepts of his Pattern recognition study are interwoven with issues in Wireless, Algorithm, Robustness and Character recognition.
His Computer network research is multidisciplinary, incorporating perspectives in Computer security and Cloud computing security.
His most cited work include:
- Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (242 citations)
- Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges (222 citations)
- A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis (161 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Pixel and Feature extraction are his primary areas of study.
His study in Machine learning extends to Artificial intelligence with its themes.
His study in the field of Histogram, Edge detection, Feature detection and Image texture is also linked to topics like Spiral.
The study incorporates disciplines such as Contextual image classification, Robustness and Benchmark in addition to Pattern recognition.
His Pixel research integrates issues from Image resolution and Object detection.
His Feature study typically links adjacent topics like Data mining.
He most often published in these fields:
- Artificial intelligence (62.07%)
- Computer vision (34.24%)
- Pattern recognition (32.27%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (62.07%)
- Pattern recognition (32.27%)
- Benchmark (5.67%)
In recent papers he was focusing on the following fields of study:
Xiangjian He mostly deals with Artificial intelligence, Pattern recognition, Benchmark, Deep learning and The Internet.
His study in the field of Feature, Feature extraction and Image segmentation also crosses realms of Encoder.
In his work, Channel is strongly intertwined with Image, which is a subfield of Feature extraction.
Xiangjian He works mostly in the field of Pattern recognition, limiting it down to topics relating to Pixel and, in certain cases, Noisy text and Convolution, as a part of the same area of interest.
As part of one scientific family, he deals mainly with the area of Benchmark, narrowing it down to issues related to the Feature, and often Structure.
His The Internet study also includes
- Machine learning, which have a strong connection to Hacker,
- Upload and related Computer network.
Between 2018 and 2021, his most popular works were:
- Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges (222 citations)
- A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting (31 citations)
- SAMS: A Seamless and Authorized Multimedia Streaming Framework for WMSN-Based IoMT (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- The Internet
Xiangjian He mainly investigates Artificial intelligence, Pattern recognition, Benchmark, Computer network and Authentication.
In his study, he carries out multidisciplinary Artificial intelligence and Context research.
Xiangjian He combines subjects such as Tracking, Convolution and Outlier with his study of Pattern recognition.
His Benchmark research is multidisciplinary, relying on both Image resolution, Feature, Pruning, Pixel and Eye tracking.
He has researched Computer network in several fields, including Information privacy and Upload.
His Authentication study integrates concerns from other disciplines, such as Quality of service, Multimedia, Wireless ad hoc network, Wireless sensor network and Node.
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.
