Sökning: "long term memory"

Visar resultat 16 - 20 av 440 uppsatser innehållade orden long term memory.

  1. 16. Predicting Cryptocurrency Prices with Machine Learning Algorithms: A Comparative Analysis

    Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Författare :Harsha Nanda Gudavalli; Khetan Venkata Ratnam Kancherla; [2023]
    Nyckelord :Bitcoin; Cryptocurrency; Machine Learning;

    Sammanfattning : Background: Due to its decentralized nature and opportunity for substantial gains, cryptocurrency has become a popular investment opportunity. However, the highly unpredictable and volatile nature of the cryptocurrency market poses a challenge for investors looking to predict price movements and make profitable investments. LÄS MER

  3. 17. 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

  4. 18. 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. 19. 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

  7. 20. Plant yield prediction in indoor farming using machine learning

    Magister-uppsats, Högskolan i Skövde/Institutionen för informationsteknologi

    Författare :Anjali Ashok; Mary Adesoba; [2023]
    Nyckelord :Yield prediction; Machine Learning; Hyperparameter tweaking; Support Vector Regression; Long Short-Term Memory; Artificial Neural Network;

    Sammanfattning : Agricultural industry has started to rely more on data driven approaches to improve productivity and utilize their resources effectively. This thesis project was carried out in collaboration with Ljusgårda AB, it explores plant yield prediction using machine learning models and hyperparameter tweaking. LÄS MER