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Showing result 36 - 40 of 899 essays matching the above criteria.

1. 36. Quality enhancement of time-resolved computed tomography scans with cycleGAN

   University essay from Lunds universitet/Synkrotronljusfysik; Lunds universitet/Fysiska institutionen

   Author : Johannes Stubbe; [2023]
   Keywords : carbon fibers; carbon fibres; microfibers; tomography; deep learning; cycleGAN; time-resolved tomography; Physics and Astronomy;

   Abstract : Time-resolved x-ray tomography enables us to dynamically and non-destructively study the interior of a specimen. The obtainable temporal resolution is limited by the x-ray flux and the desired spatial resolution. READ MORE

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

   University essay from KTH/Mekatronik och inbyggda styrsystem

   Author : Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Keywords : 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;

   Abstract : 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. READ MORE

4. 38. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

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

   Author : Luca Colasanti; [2023]
   Keywords : Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

   Abstract : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. READ MORE

5. 39. Development of a Complete Minuscule Microscope: Embedding Data Pipeline and Machine Learning Segmentation

   University essay from KTH/Tillämpad fysik

   Author : Kenan Zec; [2023]
   Keywords : Incubation-microscope; Machine Learning Segmentation; ESP32-Cam; Deep Learning; Inkubationsmikroskop; Maskininlärnings-segmentering; ESP32-Cam; Deep Learning;

   Abstract : Cell culture is a fundamental procedure in many laboratories and precedes much research performed under the microscope. Despite the significance of this procedural stage, the monitoring of cells throughout growth is impossible due to the absence of equipment and methodological approaches. READ MORE

6. 40. 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