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ORCIDFarshideh Einsele
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2020 – today
- 2022
[c7]Patrick Blatter, Farshideh Einsele:
Mining Association Rules in Commuter Feedback Comments from Facebook of Swiss National Railways (SBB) Using Apriori Algorithm. DIONE 2022: 230-241- 2021
- [c6]Ilona Rahel Mewes, Helena Jenzer, Farshideh Einsele:
A Study about Discovery of Critical Food Consumption Patterns Linked with Lifestyle Diseases for Swiss Population using Data Mining Methods. HEALTHINF 2021: 30-38 - [c5]Timo Lustenberger, Helena Jenzer, Farshideh Einsele:
Building an Integrated Relational Database from Swiss Nutrition's (menuCH) and Multiple Swiss Health Datasets Acquired from 1992 to 2012 for Data Mining Purposes. DATA 2021: 150-156
2010 – 2019
- 2015
- [c4]Farshideh Einsele, Leila Sadeghi, Rolf Ingold, Helena Jenzer:
A Study about Discovery of Critical Food Consumption Patterns Linked with Lifestyle Diseases using Data Mining Methods. HEALTHINF 2015: 239-245
2000 – 2009
- 2008
- [j1]Farshideh Einsele, Rolf Ingold, Jean Hennebert:
A Language-Independent, Open-Vocabulary System Based on HMMs for Recognition of Ultra Low Resolution Words. J. Univers. Comput. Sci. 14(18): 2982-2997 (2008) - [c3]Farshideh Einsele, Rolf Ingold
, Jean Hennebert:
A language-independent, open-vocabulary system based on HMMs for recognition of ultra low resolution words. SAC 2008: 429-433 - [p1]Farshideh Einsele, Rolf Ingold, Jean Hennebert:
Recognition of Ultra Low Resolution Word Images Using HMMs. Computer Recognition Systems 2 2008: 429-436 - 2007
- [c2]Farshideh Einsele, Jean Hennebert, Rolf Ingold:
Towards identification of very low resolution, anti-alaised characters. ISSPA 2007: 1-4 - [c1]Farshideh Einsele, Rolf Ingold, Jean Hennebert:
A HMM-Based Approach to Recognize Ultra Low Resolution Anti-Aliased Words. PReMI 2007: 511-518
Coauthor Index
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