Sökning: "Time-series Classification"

Visar resultat 1 - 5 av 121 uppsatser innehållade orden Time-series Classification.

1. 1. Land cover classification using machine-learning techniques applied to fused multi-modal satellite imagery and time series data

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Anastasia Sarelli; [2024]
   Nyckelord :Geography; GIS; Land Cover Classification; Landsat; Machine Learning; Earth and Environmental Sciences;

   Sammanfattning : Land cover classification is one of the most studied topics in the field of remote sensing, involving the use of data from satellite sensors to analyze and categorize different land surface types. There are numerous satellite products available, each offering different spatial, spectral, and temporal resolutions. LÄS MER

3. 2. Preserving Privacy in Cloud Services by Using an Explainable Deep-Learning Model for Anomaly Detection

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Shiwei Dong; [2023]
   Nyckelord :;

   Sammanfattning : 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

4. 3. Monitoring deforestation in the Serranía de Chiribiquete in northern Colombian Amazon using time series analysis of satellite data

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Yuyang Qian; [2023]
   Nyckelord :Physical Geography and Ecosystem analysis; CCDC; Forest loss; Landsat; Sentinel-1; Change detection; NNP; FARC; Earth and Environmental Sciences;

   Sammanfattning : Deforestation monitoring is of significant importance for the ecosystem, climate change,and policy-making. The availability of optical and synthetic aperture radar (SAR) satellite remote sensing images, along with the development of time series change detection methods, has contributed to the increasing popularity of time series analysis in forest disturbance monitoring. LÄS MER

5. 4. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

   Master-uppsats, KTH/Mekatronik och inbyggda styrsystem

   Författare :Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Nyckelord :Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

   Sammanfattning : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. LÄS MER

7. 5. A Deep Learning Approach To Vehicle Fault Detection Based On Vehicle Behavior

   Master-uppsats, Malmö universitet/Institutionen för datavetenskap och medieteknik (DVMT)

   Författare :Rafi Khaliqi; Cozma Iulian; [2023]
   Nyckelord :Deep learning; attention mechanism; vehicle fault detection; CNN; Bi-LSTM; Bi-GRU; Supervised classification;

   Sammanfattning : Vehicles and machinery play a crucial role in our daily lives, contributing to our transportationneeds and supporting various industries. As society strives for sustainability, the advancementof technology and efficient resource allocation become paramount. LÄS MER