Sökning: "LSTM"

Visar resultat 1 - 5 av 255 uppsatser innehållade ordet LSTM.

  1. 1. Sequential Anomaly Detection for Log Data Using Deep Learning

    Master-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaper

    Författare :Lina Hammargren; Wei Wu; [2021-06-14]
    Nyckelord :anomaly detection; recurrent neural network; long short-term memory; semi-supervised learning; seq2seq; transformer; unsupervised learning; log analysis;

    Sammanfattning : AbstractSoftware development with continuous integration changes needs frequent testing forassessment. Analyzing the test output manually is time-consuming and automatingthis process could be beneficial to an organization. LÄS MER

  2. 2. Training Autoencoders for feature extraction of EEG signals for motor imagery

    Kandidat-uppsats, Mälardalens högskola/Akademin för innovation, design och teknik

    Författare :Casper Wahl; [2021]
    Nyckelord :;

    Sammanfattning : Electroencephalography (EEG) is a common technique used to read brain activity from an individual, and can be used for a wide range of applications, one example is during the rehab process of stroke victims. Loss of motor function is a common side effect of strokes, and the EEG signals can show if sufficient activation of the part of the brain related to the motor function that the patient is training has been achieved. LÄS MER

  3. 3. Interpretable serious event forecasting using machine learning and SHAP

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Sebastian Gustafsson; [2021]
    Nyckelord :LSTM; GBDT; SHAP; ML; AI;

    Sammanfattning : Accurate forecasts are vital in multiple areas of economic, scientific, commercial, and industrial activity. There are few previous studies on using forecasting methods for predicting serious events. LÄS MER

  4. 4. Predictive vertical CPU autoscaling in Kubernetes based on time-series forecasting with Holt-Winters exponential smoothing and long short-term memory

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

    Författare :Thomas Wang; [2021]
    Nyckelord :Kubernetes; Docker; Container; Cloud Native; Cloud Computing; Resource Provisioning; Autoscaling; Predictive scaling; CPU Usage; Seasonality; Exponential Smoothing; Long short-term memory; Time-series Analysis; Kubernetes; Docker; Container; Cloud Native; Cloud Computing; Resursförsörjning; Autoskalning; prediktiv skalning; CPU Användning; Säsongsmässighet; Exponentiell utjämning; långt korttidsminne; tidsserieanalys;

    Sammanfattning : Private and public clouds require users to specify requests for resources such as CPU and memory (RAM) to be provisioned for their applications. The values of these requests do not necessarily relate to the application’s run-time requirements, but only help the cloud infrastructure resource manager to map requested virtual resources to physical resources. LÄS MER

  5. 5. Investigation of Machine Learning Methods for Anomaly Detection and Characterisation of Cable Shoe Pressing Processes

    Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Författare :Elliot Härenby Deak; [2021]
    Nyckelord :Machine Learning; LSTM; kNN; Time-series Classification; Anomaly Detection;

    Sammanfattning : The ability to reliably connect electrical cables is important in many applications. A poor connection can become a fire hazard, so it is important that cables are always appropriately connected. This thesis investigates methods for monitoring of a machine that presses cable connectors onto cables. LÄS MER