What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Bojan Cukic mainly focuses on Artificial intelligence, Software quality, Data mining, Machine learning and Reliability engineering.
His research integrates issues of Code, Computer vision and Pattern recognition in his study of Artificial intelligence.
His Software quality study is associated with Software.
His work on False alarm expands to the thematically related Data mining.
He has included themes like Software fault tolerance, Linear discriminant analysis, Software quality assurance and Fault prone in his Machine learning study.
His research in Reliability engineering intersects with topics in Software verification and validation, Reliability, Software system and Predictive modelling.
His most cited work include:
- Software Engineering for Self-Adaptive Systems: A Research Roadmap (864 citations)
- Software Engineering for Self-Adaptive Systems : A Second Research Roadmap (523 citations)
- Defect prediction from static code features: current results, limitations, new approaches (278 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Data mining, Machine learning, Reliability engineering and Software quality are his primary areas of study.
His work carried out in the field of Artificial intelligence brings together such families of science as Computer vision and Identification.
The study incorporates disciplines such as Set, Software quality assurance, Software metric, Fingerprint and Software development process in addition to Data mining.
His biological study spans a wide range of topics, including Software verification and validation, Reliability, Software system and Software architecture.
His Software quality study introduces a deeper knowledge of Software.
His Software engineering research is multidisciplinary, incorporating perspectives in Resource, Software development and Social software engineering.
He most often published in these fields:
- Artificial intelligence (33.91%)
- Data mining (25.29%)
- Machine learning (17.82%)
What were the highlights of his more recent work (between 2012-2020)?
- Data mining (25.29%)
- Biometrics (13.79%)
- Fingerprint recognition (6.32%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Data mining, Biometrics, Fingerprint recognition, Artificial intelligence and Identification.
His Data mining research is multidisciplinary, incorporating elements of Fingerprint, Software, Software verification and validation and Data set.
His Software verification and validation study combines topics in areas such as Verification and validation, Development testing, Software Engineering Process Group, Software development process and Social software engineering.
His Social software engineering research includes elements of Computer programming and Software engineering.
His Biometrics research includes themes of Image quality, Mobile device and Authentication.
His Artificial intelligence research incorporates elements of Computer vision and Pattern recognition.
Between 2012 and 2020, his most popular works were:
- Software Engineering for Self-Adaptive Systems : A Second Research Roadmap (523 citations)
- Fingerprint liveness detection based on histograms of invariant gradients (64 citations)
- Defect Prediction between Software Versions with Active Learning and Dimensionality Reduction (35 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Bojan Cukic mainly investigates Data mining, Fingerprint recognition, Artificial intelligence, Computer vision and Software development.
His Data mining research is multidisciplinary, incorporating perspectives in Test data, Software, Fingerprint and Quantization.
His Fingerprint recognition study integrates concerns from other disciplines, such as Image quality, Fingerprint, Interoperability, Robustness and Pattern recognition.
His research in the fields of IRIS, Noise and Pixel overlaps with other disciplines such as Transform coding.
His Software development research is multidisciplinary, relying on both Active learning and Machine learning.
His Software verification and validation research integrates issues from Computer programming, Software engineering and Systems development life cycle.
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