Sökning: "händelse i skolan"

Visar resultat 1 - 5 av 62 uppsatser innehållade orden händelse i skolan.

1. 1. Changes in Organization Design Produced by Covid-19 from a Path-Dependence Perspective : A Case Study of a Medical Manufacturing Company

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Carolina Artavia Muñoz; [2023]
   Nyckelord :Organization Design; Black Swan Events; Path Dependence Theory; Jay Galbraith s Star Model; Covid-19; Medical Manufacturing Industry.; Organisationsdesign; svarta svan-händelser; stigberoende; Jay Galbraits Star Model; Covid-19; medicinsk tillverkningsindustri.;

   Sammanfattning : Covid-19 brought various challenges that the world that was not fully prepared to face since 2019. The pandemic came with both health and economic repercussions, and various organizations had to adapt their Organization Design to confront these new challenges posed by this Black Swan event. LÄS MER

3. 2. Using eye tracking to measure visual responses

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Sebaztian Johansson; Zebastian Kensert Forsman; [2023]
   Nyckelord :;

   Sammanfattning : Eye tracking technology is becoming more accessible in general, presenting new opportunities for applications. Said technology is also a relatively new and unexplored tool that can be used in research to enable insights not previously available. LÄS MER

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

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Luca Colasanti; [2023]
   Nyckelord :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;

   Sammanfattning : 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. LÄS MER

5. 4. On Linear Mode Connectivity up to Permutation of Hidden Neurons in Neural Network : When does Weight Averaging work?

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Adhithyan Kalaivanan; [2023]
   Nyckelord :Mode Connectivity; Representation Learning; Loss Landscape; Network Symmetry; Lägesanslutning; representationsinlärning; förlustlandskap; nätverkssymmetri;

   Sammanfattning : Neural networks trained using gradient-based optimization methods exhibit a surprising phenomenon known as mode connectivity, where two independently trained network weights are not isolated low loss minima in the parameter space. Instead, they can be connected by simple curves along which the loss remains low. LÄS MER

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

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

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

   Sammanfattning : 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. LÄS MER