Essays about: "Multivariate Time Series Classification"
Showing result 1 - 5 of 16 essays containing the words Multivariate Time Series Classification.
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1. Preserving Privacy in Cloud Services by Using an Explainable Deep-Learning Model for Anomaly Detection
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : As cloud services become increasingly popular, ensuring their privacy and security has become a significant concern for users. Cloud computing involves Data Service Outsourcing and Computation Outsourcing, which require additional security considerations compared to traditional computing. READ MORE
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2. A Transformer-Based Scoring Approach for Startup Success Prediction : Utilizing Deep Learning Architectures and Multivariate Time Series Classification to Predict Successful Companies
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The Transformer, an attention-based deep learning architecture, has shown promising capabilities in both Natural Language Processing and Computer Vision. Recently, it has also been applied to time series classification, which has traditionally used statistical methods or the Gated Recurrent Unit (GRU). READ MORE
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3. Machine learning for detecting financial crime from transactional behaviour
University essay from Uppsala universitet/Signaler och systemAbstract : Banks and other financial institutions are to a certain extent obligated to ensure that their services are not utilized for any type of financial crime. This thesis investigates the possibility of analyzing bank customers' transactional behaviour with machine learning to detect if they are involved in financial crime. READ MORE
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4. AUGMENTATION AND CLASSIFICATION OF TIME SERIES FOR FINDING ACL INJURIES
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : This thesis addresses the problem where we want to apply machine learning over a small data set of multivariate time series. A challenge when classifying data is when the data set is small and overfitting is at risk. Augmentation of small data sets might avoid overfitting. READ MORE
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5. Banger for the Buck : Predicting Growth of Music Tracks using Machine Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The advent of music streaming has made it increasingly important for actors in the music industry to understand if tracks are going to succeed or not. This study investigates if it is possible to accurately classify the growth of the listener base of a music track based on multivariate time series with listener behavior data. READ MORE