What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Programming language
- Machine learning
Artificial intelligence, Fuzzy set, Fuzzy logic, Recommender system and Distrust are her primary areas of study.
Her Artificial intelligence research incorporates themes from Social media, Machine learning, Satisfiability and Natural language processing.
In her study, Theoretical computer science, Argumentation framework and Degree is strongly linked to Algorithm, which falls under the umbrella field of Fuzzy set.
Her work carried out in the field of Fuzzy logic brings together such families of science as Logical consequence, Answer set programming, Region connection calculus, Relation and Deductive reasoning.
Her Type-2 fuzzy sets and systems research is multidisciplinary, incorporating perspectives in Dominance-based rough set approach, Rough set and Fuzzy mathematics.
In Information retrieval, Martine De Cock works on issues like Microblogging, which are connected to Data mining.
Her most cited work include:
- Gradual trust and distrust in recommender systems (137 citations)
- Intuitionistic fuzzy rough sets: at the crossroads of imperfect knowledge (134 citations)
- Ranking Approaches for Microblog Search (106 citations)
What are the main themes of her work throughout her whole career to date?
Martine De Cock mostly deals with Artificial intelligence, Fuzzy logic, Theoretical computer science, Answer set programming and Machine learning.
Her Artificial intelligence research incorporates themes from Information retrieval, Malware and Natural language processing.
Martine De Cock regularly ties together related areas like Data mining in her Fuzzy logic studies.
Her Theoretical computer science study integrates concerns from other disciplines, such as Vagueness, Algorithm, Boolean network and Solver.
Martine De Cock combines subjects such as Semantics and Extension with her study of Answer set programming.
Martine De Cock has included themes like Spatial intelligence and Rough set in her Fuzzy set study.
She most often published in these fields:
- Artificial intelligence (41.39%)
- Fuzzy logic (32.97%)
- Theoretical computer science (27.47%)
What were the highlights of her more recent work (between 2016-2021)?
- Artificial intelligence (41.39%)
- Machine learning (15.38%)
- Secure multi-party computation (4.76%)
In recent papers she was focusing on the following fields of study:
Her scientific interests lie mostly in Artificial intelligence, Machine learning, Secure multi-party computation, Deep learning and Malware.
Her Artificial intelligence research integrates issues from Domain and Computation.
The Decision tree and Support vector machine research she does as part of her general Machine learning study is frequently linked to other disciplines of science, such as User information, therefore creating a link between diverse domains of science.
While the research belongs to areas of Malware, Martine De Cock spends her time largely on the problem of Network security, intersecting her research to questions surrounding Algorithm.
Her Cryptographic engineering study incorporates themes from Gradient descent and Theoretical computer science.
Her Theoretical computer science research includes elements of Boolean circuit and Fuzzy logic.
Between 2016 and 2021, her most popular works were:
- Efficient and Private Scoring of Decision Trees, Support Vector Machines and Logistic Regression Models Based on Pre-Computation (57 citations)
- Character Level based Detection of DGA Domain Names (53 citations)
- Inline DGA Detection with Deep Networks (48 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Martine De Cock mainly focuses on Artificial intelligence, Deep learning, Machine learning, Secure multi-party computation and Malware.
Her work in the fields of Artificial intelligence, such as Domain knowledge, intersects with other areas such as Social trust.
Her study focuses on the intersection of Deep learning and fields such as Domain with connections in the field of Feature extraction, Domain generation algorithm, Domain Name System, Data mining and The Internet.
Her work on Profiling as part of general Machine learning study is frequently connected to Decision level, Law enforcement and User modeling, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Her study on Secure multi-party computation also encompasses disciplines like
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Tree that connect with fields like Privacy preserving and Information retrieval,
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Cryptographic protocol together with Encryption, Personally identifiable information, Data science and Password.
Her research on Malware also deals with topics like
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Random forest which is related to area like Supervised learning,
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Network security which is related to area like Algorithm, Artificial neural network and Training set.
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