Essays about: "Anomali Detection"

Showing result 1 - 5 of 24 essays containing the words Anomali Detection.

  1. 1. AI/ML Development for RAN Applications : Deep Learning in Log Event Prediction

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Yuxin Sun; [2023]
    Keywords : LSTM; Anomaly Detection; Failure Prediction; Log Mining; Deep Learning; LSTM; Anomali Detection; Failure Prediction; Log Mining; Deep Learning;

    Abstract : Since many log tracing application and diagnostic commands are now available on nodes at base station, event log can easily be collected, parsed and structured for network performance analysis. In order to improve In Service Performance of customer network, a sequential machine learning model can be trained, test, and deployed on each node to learn from the past events to predict future crashes or a failure. READ MORE

  3. 2. Finding Causal Relationships Among Metrics In A Cloud-Native Environment

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Suresh Rishi Nandan; [2023]
    Keywords : Causality; Causal Discovery; Bayesian Network; Conditional Independence; Partial Correlation; Ensemble Causal Discovery; Anomaly Detection; Causal Graphs; Causality; Causal Discovery; Bayesian Network; Conditional Indeberoende; partiell korrelation; Ensemble Causal Discovery; Anomali Detektion; kausala grafer;

    Abstract : Automatic Root Cause Analysis (RCA) systems aim to streamline the process of identifying the underlying cause of software failures in complex cloud-native environments. These systems employ graph-like structures to represent causal relationships between different components of a software application. READ MORE

  4. 3. Anomaly detection for prediction of failures in manufacturing environments : Machine learning based semi-supervised anomaly detection for multivariate time series to predict failures in a CNC-machine

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Felix Boltshauser; [2023]
    Keywords : Machine learning; Anomaly Detection; DeepAnT; ROCKET; OCSVM; manufacturing; predictive maintenance; Maskin inlärning; Anomali Detektion; DeepAnT; ROCKET; OCSVM; tillverkning; prediktivt underhåll;

    Abstract : For manufacturing enterprises, the potential of collecting large amounts of data from production processes has enabled the usage of machine learning for prediction-based monitoring and maintenance of machines. Yet common maintenance strategies still include reactive handling of machine failures or schedule-based maintenance conducted by experienced personnel. READ MORE

  5. 4. Credit Card Transaction Fraud Detection Using Neural Network Classifiers

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Ehsan Nazeriha; [2023]
    Keywords : GAN; Deep Learning; Variational Autoencoder; Anomaly Detection; SMOTE; GAN; Djupinlärning; Variational Autoencoder; Anomali detektering; SMOTE;

    Abstract : With increasing usage of credit card payments, credit card fraud has also been increasing. Therefore a fast and accurate fraud detection system is vital for the banks. To solve the problem of fraud detection, different machine learning classifiers have been designed and trained on a credit card transaction dataset. READ MORE

  6. 5. Unsupervised Machine Learning Based Anomaly Detection in Stockholm Road Traffic

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Vilma Hellström; [2023]
    Keywords : Anomaly detection; DBSCAN; LSTM; Machine learning; Synthetic anomalies; Unsupervised learning; Anomalidetektering; DBSCAN; LSTM; maskininlärning; syntetiska anomalier; oövervakad inlärning;

    Abstract : This thesis is a study of anomaly detection in vehicle traffic data in central Stockholm. Anomaly detection is an important tool in the analysis of traffic data for improved urban planing. Two unsupervised machine learning models are used, the DBSCAN clustering model and the LSTM deep learning neural network. READ MORE