1. Alain Goossens & Jean-Pierre Van Loo Data scientists – SII Belgium
Applying neural networks to anomaly detection
Alain Goossens & Jean-Pierre Van Loo Data scientists – SII Belgium
Data Innovation Summit
27th June 2018
#DISUMMIT

2. Underground cabling, infrastructure and pipelines
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

3. Current anomaly detection process
Desk top analysis
Visual analysis of pictures
Risk analysis
Verification of permits
When necessary send an intervention team
Intervention team
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

4. Artificial intelligence
Concept of the POC
YES there is an anomaly
NO there is no anomaly
Artificial intelligence
INPUT
pictures
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

5. Overview Advanced analytics pattern in Azure
MODEL TRAINING
SERVING STORAGE
AZURE ML
AZURE ML STUDIO
ML SERVER
AZURE DATABRICKS (Spark ML)
SQL Server (In-database ML)
DATA SCIENCE VM
BATCH AI
SENSORS AND IOT
(UNSTRUCTURED)
COSMOS DB
APPLICATIONS
LONG TERM STORAGE
DATA PROCESSING
SQL DB r
LOGS, FILES AND MEDIA (UNSTRUCTURED)
SQL DB
DATA LAKE STORE
AZURE STORAGE
COSMOS DB
DATA LAKE ANALYTICS
AZURE DATABRICKS
HDINSIGHT
SQL DW
TRAINED MODEL HOSTING
ORCHESTRATION
AZURE ANALYSIS SERVICES
BUSINESS / CUSTOM APPS
(STRUCTURED)
DASHBOARDS
SQL Server (In-database ML)
AZURE CONTAINER SERVICE
DATA FACTORY
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

6. Data preparation Categorization of the images
Rural - Industrial
523 with bulldozers
567 with no anomalies
Enrichment to have more images
Cropping, flipping or translating images
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

7. The models used
A simple neural network with Microsoft Azure Machine Learning Studio 
Convolutional neural network (CNN) and R-CNN on a deep learning Virtual machine
Custom Vision Service
Vision API + Microsoft Azure Machine Learning Studio based on output of the Vision API
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

8. The models used
A simple neural network with Microsoft Azure Machine Learning Studio 
Convolutional neural network (CNN) and R-CNN on a deep learning Virtual machine
Custom Vision Service
Vision API + Microsoft Azure Machine Learning Studio based on output of the Vision API
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

9. Two-class Neural network – AUC optimized

10. Principle of convolutional neural network (CNN)
anomaly
no anomaly
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

11. Deep learning virtual machine
Labelled data set, anomaly
Labelled data set, normal condition
Two class classification/prediction:
YES anomaly
No anomaly
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

12. Convolutional neural network (CNN)
Results
50 epochs
Accuracy:
Training = 98,5 %
Validation = 92,9 %
Test = 93 %
True positives : 94,4 % False negatives : 5,6 %
False positives : 8,4 % True negatives : 91,6 %
Bulldozer detected when there is one
No bulldozer detected when there is no
No bulldozer detected but there is one
Bulldozer detected but there is no
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

13. Regions of interest on big images
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

14. Custom Vision Service
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

15. Computer Vision API 2000 known tags
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

16. Machine learning studio on vision API tags
Find a correlation between tags identified and the presence of a bulldozer
Tested models:
Two-class boosted decision tree
Two-class logistic regression
Two-class support vector machine
Two-class decision jungle
Two-class neural network
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

17. Business conclusions
Fairly good results with the main models used but with a dataset:
trivial
limited in variability
Customer learned about AI, ML and NN in a real world context
Further steps:
Extend dataset variability
Apply other models
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

18. Business conclusions
Fairly good results with the main models used but with a dataset:
trivial
limited in variability
Customer learned about AI, ML and NN in a real world context
Further steps:
Extend dataset variability
Apply other models
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

19. Scientific conclusions
Cropping images is absolutely required
Learning speed of the neural network is quite satisfactory (for a relatively simple dataset)
Ad hoc trained models perform better but pre- trained models are not to be excluded
Using classifiers on metatags from a generic Vision API gives a fairly good result
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT

20. Scientific conclusions
Cropping images is absolutely required
Learning speed of the neural network is quite satisfactory (for a relatively simple dataset)
Ad hoc trained models perform better but pre- trained models are not to be excluded
Using classifiers on metatags from a generic Vision API gives a fairly good result
Applying neural networks to anomaly detection, Alain Goossens & Jean-Pierre #DISUMMIT