scholarly journals Automatic analysis of malware behavior using machine learning

2011 ◽  
Vol 19 (4) ◽  
pp. 639-668 ◽  
Author(s):  
Konrad Rieck ◽  
Philipp Trinius ◽  
Carsten Willems ◽  
Thorsten Holz
Keyword(s):  
Machine Learning ◽  
Automatic Analysis

scholarly journals Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jorunn M. Andersen ◽  
Oliwia Witczak ◽  
Vajira Thambawita ◽  
Pål Halvorsen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sperm Motility ◽  
Semen Analysis ◽  
Biological Data ◽  
Automatic Analysis ◽  
Semen Sample ◽  
Human Reproduction ◽  
Machine Learning Techniques ◽  
Simple Linear Regression ◽  
Learning Techniques

AbstractMethods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology. We use modern and classical machine learning techniques together with a dataset consisting of 85 videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. Adding participant data did not improve the algorithms performance. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.

Clinical Validation of Machine Learning for Automatic Analysis of Multichannel Magnetocardiography

2005 ◽  
pp. 143-152 ◽  
Author(s):  
Riccardo Fenici ◽  
Donatella Brisinda ◽  
Anna Maria Meloni ◽  
Karsten Sternickel ◽  
Peter Fenici
Keyword(s):  
Machine Learning ◽  
Automatic Analysis ◽  
Clinical Validation
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