Sökning: "data use"

Visar resultat 1 - 5 av 8756 avhandlingar innehållade orden data use.

1. 1. Data Stream Mining and Analysis : Clustering Evolving Data

   Författare :Christian Nordahl; Håkan Grahn; Veselka Boeva; Marie Netz; Plamen Angelov; Blekinge Tekniska Högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Data Stream Mining; Clustering; Data Streams; Data Mining; Computer Science; Datavetenskap;

   Sammanfattning : Streaming data is becoming more prevalent in our society every day. With the increasing use of technologies such as the Internet of Things (IoT) and 5G networks, the number of possible data sources steadily increases. Therefore, there is a need to develop algorithms that can handle the massive amount of data we now generate. LÄS MER

3. 2. Data Privacy for Big Automotive Data

   Författare :Boel Nelson; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; data privacy; differential privacy; big data; vehicular data; privacy;

   Sammanfattning : In an age where data is becoming increasingly more valuable as itallows for data analysis and machine learning, big data has become ahot topic. With big data processing, analyses can be carried out onhuge amounts of user data. LÄS MER

4. 3. Flood Hazard Assessment in Data-Scarce Basins : Use of alternative data and modelling techniques

   Författare :Diana Fuentes-Andino; Sven Halldin; Chong-Yu Xu; Keith Beven; Giuliano Di Baldassarre; Wouter Buytaert; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Central America; floods; data scarcity; data quality; uncertainty analysis; regionalisation; flood frequency analysis; GLUE; hydraulic modelling; rainfall-runoff modeling; TOPMODEL; LISFLOOD-FP; GRADEX; index-flood; Muskingum-Cunge-Todini flow routing; Mellanamerika; högflöde; datakvalitet; osäkerhetsanalys; regionalisering; frekvensanalys av högflöden; GLUE; hydraulisk modellering; nederbörds-avrinningsmodeller; TOPMODEL; LISFLOOD-FP; GRADEX; indexflöde; Muskingum-Cunge-Todini flödessvarstid; Central América; inundaciones; escasez de datos; calidad de los datos; análisis de incertidumbre; regionalización; análisis de frequencia de inundación; GLUE; modelación hidraulica; modelo de lluvia-escorrentía; TOPMODEL; LISFLOOD-FP; GRADEX; índice de inundación; Muskingum-Cunge-Todini rutina de propagación de flujo;

   Sammanfattning : Flooding is of great concern world-wide, causing damage to infrastructure, property and loss of life. Low-income countries, in particular, can be negatively affected by flood events due to their inherent vulnerabilities. Moreover, data to perform studies for flood risk management in low-income regions are often scarce or lacking sufficient quality. LÄS MER

5. 4. Algorithmically Guided Information Visualization : Explorative Approaches for High Dimensional, Mixed and Categorical Data

   Författare :Sara Johansson Fernstad; Mikael Jern; Jimmy Johansson; Jane Shaw; Matthew O. Ward; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Information visualization; data mining; high dimensional data; categorical data; mixed data;

   Sammanfattning : Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. LÄS MER

7. 5. Order in the random forest

   Författare :Isak Karlsson; Henrik Boström; Lars Asker; Pierre Geurts; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine learning; random forest; ensemble; time series; data series; sequential data; sparse data; high-dimensional data; Computer and Systems Sciences; data- och systemvetenskap;

   Sammanfattning : In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered. LÄS MER