What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Algorithm
- Operating system
Hanan Samet mainly investigates Algorithm, Quadtree, Theoretical computer science, Artificial intelligence and Data structure.
Her studies deal with areas such as Pixel and Data mining as well as Algorithm.
The study incorporates disciplines such as Discrete mathematics, Image processing, Computer graphics, Tree and Product in addition to Quadtree.
Her Theoretical computer science research is multidisciplinary, relying on both Polygon, Hierarchical database model, Representation, Vertex and Line segment.
Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Line, Computer vision and Pattern recognition.
The various areas that Hanan Samet examines in her Data structure study include Time complexity, Path and Polyhedron.
Her most cited work include:
- The design and analysis of spatial data structures (2274 citations)
- The Quadtree and Related Hierarchical Data Structures (1862 citations)
- Foundations of Multidimensional and Metric Data Structures (1147 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Algorithm, Quadtree, Data mining, Information retrieval and Artificial intelligence.
Hanan Samet works mostly in the field of Algorithm, limiting it down to topics relating to Theoretical computer science and, in certain cases, Spatial network and Vertex, as a part of the same area of interest.
The Quadtree study combines topics in areas such as Image processing, Hierarchical database model, Representation, Computer graphics and Tree.
Her Data mining research is multidisciplinary, incorporating elements of Spatial query, Spatial database, Spatial analysis and Geographic information system.
Her Information retrieval study integrates concerns from other disciplines, such as World Wide Web and Interface.
Hanan Samet interconnects Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence.
She most often published in these fields:
- Algorithm (21.33%)
- Quadtree (20.00%)
- Data mining (17.87%)
What were the highlights of her more recent work (between 2011-2020)?
- Information retrieval (16.80%)
- Data mining (17.87%)
- World Wide Web (9.60%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Information retrieval, Data mining, World Wide Web, Geotagging and Artificial intelligence.
Her Data mining research integrates issues from Spatial query, Spatial database, Index and Trajectory.
Her Spatial database research includes themes of Quadtree and Computer graphics.
Her research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Inference.
Her Spatial analysis study frequently links to other fields, such as Theoretical computer science.
Her studies in Filter integrate themes in fields like Algorithm and Computation.
Between 2011 and 2020, her most popular works were:
- Pruning Filters for Efficient ConvNets (833 citations)
- Pruning Filters for Efficient ConvNets (522 citations)
- Schema extraction for tabular data on the web (99 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Operating system
Hanan Samet mostly deals with Information retrieval, World Wide Web, Interface, Artificial intelligence and Geotagging.
Her work carried out in the field of Information retrieval brings together such families of science as Process, Data mining, Database, Window and Set.
Her study in the field of Mobile device also crosses realms of Data quality.
As a part of the same scientific study, Hanan Samet usually deals with the Artificial intelligence, concentrating on Machine learning and frequently concerns with Inference.
Her work investigates the relationship between Inference and topics such as Filter that intersect with problems in Algorithm.
Her work in the fields of Algorithm, such as Computation, intersects with other areas such as Sparse matrix.
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.
