What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His primary scientific interests are in Artificial intelligence, Eye tracking, Information retrieval, Computer vision and Reading.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Set and Pattern recognition.
His Eye tracking study combines topics from a wide range of disciplines, such as Personalization, Inertial measurement unit, Gaze and Relevance feedback.
His Information retrieval research is multidisciplinary, incorporating elements of Document layout analysis, Image, Text mining, Key and Document clustering.
His study in Computer vision is interdisciplinary in nature, drawing from both Classifier, Linear classifier and Test set.
Andreas Dengel interconnects Experiential learning, Mathematics education and Computer graphics, Scrolling in the investigation of issues within Reading.
His most cited work include:
- ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images (338 citations)
- From paper to office document standard representation (151 citations)
- EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (136 citations)
What are the main themes of his work throughout his whole career to date?
Andreas Dengel focuses on Artificial intelligence, Information retrieval, Machine learning, Deep learning and Natural language processing.
The study incorporates disciplines such as Computer vision and Pattern recognition in addition to Artificial intelligence.
Particularly relevant to Feature extraction is his body of work in Pattern recognition.
His Information retrieval research integrates issues from Task and World Wide Web.
His research in Machine learning is mostly concerned with Support vector machine.
His studies in Eye tracking integrate themes in fields like Human–computer interaction and Eye movement.
He most often published in these fields:
- Artificial intelligence (59.08%)
- Information retrieval (19.35%)
- Machine learning (16.95%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (59.08%)
- Deep learning (16.78%)
- Machine learning (16.95%)
In recent papers he was focusing on the following fields of study:
Andreas Dengel mainly investigates Artificial intelligence, Deep learning, Machine learning, Pattern recognition and Convolutional neural network.
His research on Artificial intelligence frequently links to adjacent areas such as Natural language processing.
His work deals with themes such as Margin, Glaucoma, Optic disc and Domain, which intersect with Deep learning.
His Machine learning course of study focuses on Modality and Class.
His work in Pattern recognition addresses subjects such as Image, which are connected to disciplines such as Table and Task.
His Convolutional neural network study combines topics in areas such as Computer-aided diagnosis and Data mining.
Between 2018 and 2021, his most popular works were:
- EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (136 citations)
- Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks (91 citations)
- DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Artificial intelligence, Deep learning, Machine learning, Convolutional neural network and Data mining are his primary areas of study.
His Artificial intelligence research incorporates themes from Natural language processing, Time series and Pattern recognition.
Andreas Dengel combines subjects such as Image processing, Feature selection and Glaucoma, Optic disc with his study of Deep learning.
The various areas that Andreas Dengel examines in his Machine learning study include Classifier and Pose.
His study looks at the relationship between Convolutional neural network and topics such as Ground truth, which overlap with Pixel and Intersection.
His work carried out in the field of Data mining brings together such families of science as Domain, Domain model, Statistical model and Spectrogram.
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