What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Artificial immune system, Artificial intelligence, Anomaly detection, Immune system and Intrusion detection system.
His Artificial immune system study combines topics from a wide range of disciplines, such as Algorithm, Key, Cognitive science and Antigen.
His Artificial intelligence study incorporates themes from Machine learning, Scheduling and Pattern recognition.
His Anomaly detection research integrates issues from Data stream, False positive paradox, Sensor fusion and Antigen-presenting cell.
To a larger extent, Uwe Aickelin studies Computer security with the aim of understanding Intrusion detection system.
As part of one scientific family, Uwe Aickelin deals mainly with the area of Evolutionary algorithm, narrowing it down to issues related to the Robustness, and often Mathematical optimization.
His most cited work include:
- An indirect genetic algorithm for a nurse-scheduling problem (293 citations)
- Danger theory: The link between AIS and IDS? (292 citations)
- Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection (285 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Artificial immune system, Machine learning, Immune system and Data mining.
The Artificial intelligence study combines topics in areas such as Genetic algorithm, Set and Pattern recognition.
While the research belongs to areas of Genetic algorithm, Uwe Aickelin spends his time largely on the problem of Nurse scheduling problem, intersecting his research to questions surrounding Joint probability distribution.
His Artificial immune system study integrates concerns from other disciplines, such as Field, Anomaly detection, Intrusion detection system and Recommender system.
His Anomaly detection research incorporates themes from Algorithm and Antigen-presenting cell.
His work in Innate immune system and Acquired immune system are all subfields of Immune system research.
He most often published in these fields:
- Artificial intelligence (32.69%)
- Artificial immune system (19.09%)
- Machine learning (16.35%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (32.69%)
- Machine learning (16.35%)
- Data mining (10.71%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Data mining, Cluster analysis and Fuzzy set.
His work in the fields of Artificial intelligence, such as Artificial neural network, overlaps with other areas such as Regular expression.
His work is dedicated to discovering how Machine learning, Classifier are connected with Supervised learning, Novelty, Rule-based system and Dunn index and other disciplines.
Many of his research projects under Data mining are closely connected to Weighting with Weighting, tying the diverse disciplines of science together.
Uwe Aickelin combines subjects such as Jaccard index, Mathematical optimization and Quality of life with his study of Fuzzy set.
His studies in Pattern recognition integrate themes in fields like Data stream and Anomaly detection.
Between 2014 and 2021, his most popular works were:
- Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams (49 citations)
- Modelling cyber-security experts' decision making processes using aggregation operators (15 citations)
- Novel similarity measure for interval-valued data based on overlapping ratio (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Uwe Aickelin focuses on Artificial intelligence, Data mining, Machine learning, Pattern recognition and Pattern recognition.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Missing data and Imputation.
His work on Data stream mining as part of general Data mining study is frequently linked to Consumer debt, Impulsivity and Work, therefore connecting diverse disciplines of science.
His studies deal with areas such as Classifier and Diversity as well as Machine learning.
His Pattern recognition study combines topics in areas such as Dice and Interval valued.
The Pattern recognition study combines topics in areas such as Biometrics, World Wide Web, eHealth and Medical record.
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