Sökning: "Multivariate Time Series Classification"
Visar resultat 1 - 5 av 16 uppsatser innehållade orden Multivariate Time Series Classification.
1. Preserving Privacy in Cloud Services by Using an Explainable Deep-Learning Model for Anomaly Detection
Master-uppsats, Linköpings universitet/Institutionen för datavetenskapSammanfattning : 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. LÄS MER
2. A Transformer-Based Scoring Approach for Startup Success Prediction : Utilizing Deep Learning Architectures and Multivariate Time Series Classification to Predict Successful Companies
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 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). LÄS MER
3. Machine learning for detecting financial crime from transactional behaviour
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Signaler och systemSammanfattning : 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. LÄS MER
4. AUGMENTATION AND CLASSIFICATION OF TIME SERIES FOR FINDING ACL INJURIES
Kandidat-uppsats, Umeå universitet/Institutionen för datavetenskapSammanfattning : 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. LÄS MER
5. Banger for the Buck : Predicting Growth of Music Tracks using Machine Learning
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 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. LÄS MER