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Visar resultat 1 - 5 av 12 uppsatser som matchar ovanstående sökkriterier.

1. 1. Neural Network-Based Residential Water End-Use Disaggregation

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

   Författare :Cajsa Pierrou; [2023]
   Nyckelord :Residential water end-use; Flow disaggregation; Time series classification; Artificial neural network; Smart water meter; Slutanvändning av vatten i hushåll; Flödesdisaggregering; Tidsserieklassificering; Artificiella neurala nätverk; Smart vattenmätare;

   Sammanfattning : Sustainable management of finite resources is vital for ensuring livable conditions for both current and future generations. Measuring the total water consumption of residential households at high temporal resolutions and automatically disaggregating the sole signal into classified end usages (e.g. LÄS MER

3. 2. Attention based Knowledge Tracing in a language learning setting

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

   Författare :Sebastiaan Vergunst; [2022]
   Nyckelord :Knowledge Tracing; Exercise Recommendation; Personalised Learning; Recurrent Neural Network; Attention; Self-Attention; Exercise Embedding; Kunskapsspårning; Övningsrekommendation; Personligt Anpassad Inlärning; Rekurrenta Neurala Nätverk; Uppmärksamhet; Självuppmärksamhet; Övningsembedding;

   Sammanfattning : Knowledge Tracing aims to predict future performance of users of learning platforms based on historical data, by modeling their knowledge state. In this task, the target is a binary variable representing the correctness of the exercise, where an exercise is a word uttered by the user. LÄS MER

4. 3. Non-Contractual Churn Prediction with Limited User Information

   Master-uppsats, KTH/Matematisk statistik

   Författare :Andreas Brynolfsson Borg; [2019]
   Nyckelord :;

   Sammanfattning : This report compares the effectiveness of three statistical methods for predicting defecting viewers in SVT's video on demand (VOD) services: logistic regression, random forests, and long short-term memory recurrent neural networks (LSTMs). In particular, the report investigates whether or not sequential data consisting of users' weekly watch histories can be used with LSTMs to achieve better predictive performance than the two other methods. LÄS MER

5. 4. Arrival Time Predictions for Buses using Recurrent Neural Networks

   Master-uppsats, Linköpings universitet/Artificiell intelligens och integrerade datorsystem

   Författare :Christoffer Fors Johansson; [2019]
   Nyckelord :Machine Learning; Recurrent Neural Networks; RNN; Long short-term memory; LSTM; Regression; GTFS;

   Sammanfattning : In this thesis, two different types of bus passengers are identified. These two types, namely current passengers and passengers-to-be have different needs in terms of arrival time predictions. A set of machine learning models based on recurrent neural networks and long short-term memory units were developed to meet these needs. LÄS MER

7. 5. Explainable AI - Visualization of Neuron Functionality in Recurrent Neural Networks for Text Prediction

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

   Författare :John Dahlberg; [2019]
   Nyckelord :Explainability; Visualization; Recurrent Neural Networks; Neuron Functionality; Text Prediction; Förklaringsbarhet; Visualisering; Rekurrenta Neurala Nätverk; Neu- ronfunktionalitet; Textprediktering;

   Sammanfattning : Artificial Neural Networks are successfully solving a wide range of problems with impressive performance. Nevertheless, often very little or nothing is understood in the workings behind these black-box solutions as they are hard to interpret, let alone to explain. LÄS MER