Essays about: "Meddelande klassificering"

Found 3 essays containing the words Meddelande klassificering.

  1. 1. Message Classification Based Continuous Data Transmission for an E-health Embedded System

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

    Author : Jiuwu Sun; [2019]
    Keywords : Bluetooth Low Energy BLE ; ARM micro-controller; E-health embedded system; Message classification; Real-time operating system RTOS .; Bluetooth Low Energy BLE ; ARM-mikrokontroller; E-hälsa inbäddat system; Meddelande klassificering; Realtid operativsystem RTOS .;

    Abstract : This thesis aims to develop an e-health embedded system with a real-time operating system (RTOS), which allows users to monitor their body condition, including heart rate and breath, through Bluetooth Low Energy (BLE). Meanwhile, the device is also able to provide guidance for breathing by simulating breathing according to given parameters. READ MORE

  3. 2. LSTM vs Random Forest for Binary Classification of Insurance Related Text

    University essay from KTH/Matematisk statistik

    Author : Hannes Kindbom; [2019]
    Keywords : Random Forest; Classification; Natural Language Processing; Machine Learning; Neural Networks; Bag of Words; Bachelor Thesis; Diffusion of Innovation; Adoption Rate; User Experience; Random Forest; Klassificering; Språkteknologi; Maskininlärning; Neurala nätverk; Bag of Words; Kandidatexamensarbete; Användarupplevelse;

    Abstract : The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. READ MORE

  4. 3. Classification of Hate Tweets and Their Reasons using SVM

    University essay from Uppsala universitet/Avdelningen för datalogi

    Author : Natalya Tarasova; [2016]
    Keywords : Support Vector Machines; classification; Akaike Information Criteria; machine learning; Twitter; hate tweets;

    Abstract : Denna studie fokuserar på att klassificera hat-meddelanden riktade mot mobiloperatörerna Verizon,  AT&T and Sprint. Huvudsyftet är att med hjälp av maskininlärningsalgoritmen Support Vector Machines (SVM) klassificera meddelanden i fyra kategorier - Hat, Orsak, Explicit och Övrigt - för att kunna identifiera ett hat-meddelande och dess orsak. READ MORE