What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
Baowen Xu focuses on Data mining, Artificial intelligence, Machine learning, Software and Object-oriented programming.
His Data mining research is multidisciplinary, relying on both Context, Class size, Test case, Range and Regression testing.
The concepts of his Artificial intelligence study are interwoven with issues in Software quality, Set, Metric and Pattern recognition.
His Machine learning research is multidisciplinary, incorporating perspectives in Computational linguistics, Metamorphic testing and Oracle.
The study incorporates disciplines such as Statistics, Cohesion, Cyclomatic complexity and Slicing in addition to Object-oriented programming.
His study looks at the relationship between Test Management Approach and fields such as Task, as well as how they intersect with chemical problems.
His most cited work include:
- An Efficient Identity-Based Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks (315 citations)
- Link prediction in social networks: the state-of-the-art (308 citations)
- Link prediction in social networks: the state-of-the-art (308 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Data mining, Artificial intelligence, Programming language, Software and Machine learning.
The Data mining study combines topics in areas such as Context, Modified condition/decision coverage, Test case, Code coverage and Empirical research.
His research in Test case intersects with topics in Algorithm, Regression testing and Test Management Approach.
His work deals with themes such as Metric and Pattern recognition, which intersect with Artificial intelligence.
His study ties his expertise on Slicing together with the subject of Programming language.
His Software research incorporates themes from Debugging, Software engineering and Source code.
He most often published in these fields:
- Data mining (25.50%)
- Artificial intelligence (25.00%)
- Programming language (22.00%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (25.00%)
- Machine learning (13.75%)
- Software (18.50%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Software, Software bug and Data mining.
His Artificial intelligence research incorporates elements of Baseline and Code.
His studies in Machine learning integrate themes in fields like Test data, Context, Task and Data modeling.
His biological study spans a wide range of topics, including Python, Function and Upstream, Downstream.
His Software bug research is multidisciplinary, incorporating elements of Cross project, Software metric, Maintenance engineering, Workaround and Empirical research.
His Data mining study which covers Metric that intersects with Sparse approximation and Obfuscation.
Between 2016 and 2021, his most popular works were:
- An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems (69 citations)
- How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction (57 citations)
- On the Multiple Sources and Privacy Preservation Issues for Heterogeneous Defect Prediction (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
His scientific interests lie mostly in Software, Data mining, Artificial intelligence, Machine learning and Software bug.
His work in the fields of Analysis effort method overlaps with other areas such as Effort management.
His Data mining study combines topics from a wide range of disciplines, such as Estimation, Metric, Estimator, Empirical research and AdaBoost.
In general Artificial intelligence study, his work on Kernel, Ensemble learning and Kernel often relates to the realm of Multiple kernel learning, thereby connecting several areas of interest.
His study in the fields of Feature vector under the domain of Machine learning overlaps with other disciplines such as Stage.
His Python study also includes
- Maintainability which connect with Software development,
- World Wide Web together with Data science.
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.
