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480 results

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Configuration Component Flow variable Education Topic detection
+5
  1. Go to item
    Workflow
    Fraud Detection: Model Training
    Fraud Fraud detection Random forest
    +5
    This workflow reads in the creditcard.csv file and trains and evaluates a Random Forest model to classify transactions as either …
    knime > Examples > 50_Applications > 39_Fraud_Detection > 01_Fraud_Detection_Model_Training
    0
  2. Go to item
    Workflow
    Fraud Detection Techniques Comparison
    Credit card Fraud DBSCAN
    +6
    This workflow shows an overview of outlier detection techniques for credit card fraud detection. The performance of the technique…
    knime > Finance, Accounting, and Audit > Fraud Detection Techniques Comparison
    3
  3. Go to item
    Workflow
    Fraud Detection: Model Deployment
    Fraud Fraud detection Random forest
    +5
    This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 02_Fraud_Detection_Deployment
    1
  4. Go to item
    Workflow
    Fraud Detection by Unsupervised Learning
    Fraud Fraud detection Banking
    +8
    This workflow reads in the creditcard.csv file and trains and evaluates an Isolation Forest model that detects fraudulent transac…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Solutions > 02_Fraud_Detection_by_Unsupervised_Learning
    0
  5. Go to item
    Workflow
    Fraud Detection by Supervised Learning
    Fraud Fraud detection Random forest
    +9
    This workflow reads in the creditcard.csv file and trains and evaluates a Logistic Regression and a Random Forest model to classi…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Exercises > 01_Fraud_Detection_by_Supervised_Learning
    0
  6. Go to item
    Workflow
    Fraud Detection by Unsupervised Learning
    Fraud Fraud detection Banking
    +8
    This workflow reads in the creditcard.csv file and trains and evaluates an Isolation Forest model that detects fraudulent transac…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Exercises > 02_Fraud_Detection_by_Unsupervised_Learning
    0
  7. Go to item
    Workflow
    Fraud Detection by Supervised Learning
    Fraud Fraud detection Random forest
    +9
    This workflow reads in the creditcard.csv file and trains and evaluates a Logistic Regression and a Random Forest model to classi…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Solutions > 01_Fraud_Detection_by_Supervised_Learning
    0
  8. Go to item
    Workflow
    Keras Autoencoder for Fraud Detection Deployment
    Autoencoder Keras Neural network
    +16
    Read Keras model. Read deployment data, which are normalized into range [0,1]. Apply the Keras model to the deployment data, calc…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 04_Keras_Autoencoder_for_Fraud_Detection_Deployment
    1
  9. Go to item
    Workflow
    Keras Autoencoder for Fraud Detection Training
    Autoencoder Keras Neural network
    +16
    Partition numeric input data into a training, test, and validation set. Normalize the data into range [0,1]. Build a Keras autoen…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 03_Keras_Autoencoder_for_Fraud_Detection_Training
    1
  10. Go to item
    Workflow
    Isolation Forest for Fraud Detection: Model Training
    Fraud H2o Isolation forest
    +3
    This workflow uses the KNIME H2O Machine Learning Integration to train an isolation forest model for fraud detection.
    knime > Examples > 50_Applications > 39_Fraud_Detection > 05_Isolation_Forest_for_Fraud_Detection_Training
    0
  11. Go to item
    Workflow
    Isolation Forest for Fraud Detection: Model Deployment
    Fraud detection Deployment Isolation forest
    +5
    This workflow reads the trained isolation forest model, as well as the new transaction and applies the model to it. Based on the …
    knime > Examples > 50_Applications > 39_Fraud_Detection > 06_Isolation_Forest_for_Fraud_Detection_Deployment
    0
  12. Go to item
    Workflow
    Overview of Credit Card Fraud Detection Techniques
    Credit card Fraud DBSCAN
    +8
    This workflow shows an overview of credit card fraud detection techniques. The performances of the techniques are evaluated on th…
    knime > Finance, Accounting, and Audit > Overview of Credit Card Fraud Detection Techniques
    1
  13. Go to item
    Workflow
    Fraud Detection: Model Deployment
    Fraud Fraud detection Random forest
    +1
    This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify…
    kathrin > Public > Faud_Detection_Autoencoder > Isolation_Forest_for_Fraud_Detection_Deployment
    0
  14. Go to item
    Workflow
    Fraud Detection Model
    tosinlitics > Public > Fraud Detection Model
    0
  15. Go to item
    Workflow
    Outlier Dection / Fraud Detection in Contracts
    Fraud detection Anomaly detection Text processing
    +4
    Discover anomalies / irregularities / Frauds(?) in contracts payment amounts via: - data visualization - basic stats - clustering…
    rs1 > Public > Contracts_Fraud_Detection_Usecase_example
    1
  16. Go to item
    Workflow
    Fraud Detection Techniques Comparison
    Credit card Fraud DBSCAN
    +5
    This workflow shows an overview of outlier detection techniques for credit card fraud detection. The performance of the technique…
    marcelo_linero > Public > Fraud Detection Techniques Comparison
    0
  17. Go to item
    Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +16
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Solutions > 02_Fraud_Detection_Deployment_Solution
    0
  18. Go to item
    Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +16
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Exercises > 02_Fraud_Detection_Deployment
    0
  19. Go to item
    Workflow
    Fraud Detection: JSON Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (JSON) and Container Output (JSON) nodes can be used to create a REST API for a w…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 04_Fraud_Detection_JSON_Input
    0
  20. Go to item
    Workflow
    Fraud Detection: Table Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (Table) and Container Output (Table) nodes can be used to create a REST API for a…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 02_Fraud_Detection_Table_Input
    0

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