Essays about: "Weka"

Showing result 6 - 10 of 31 essays containing the word Weka.

  1. 6. How can machine learning help identify cheating behaviours in physical activity-based mobile applications?

    University essay from Malmö universitet/Fakulteten för teknik och samhälle (TS)

    Author : Elina Kock; Yamma Sarwari; [2020]
    Keywords : machine learning; human activity recognition; HAR; smartphone; mobile games; cheating;

    Abstract : Den här studien undersöker möjligheten att använda sig utav Human Activity Recognition (HAR) i ett mobilspel, Bamblup, som använder sig utav fysiska rörelser för att upptäcka om en spelare fuskar eller om denne verkligen utför den verkliga aktiviteten. Sensordata från en accelerometer och ett gyroskop i en iPhone 7 användes för att samla data från olika människor som utförde ett antal aktiviteter utav intresse. READ MORE

  3. 7. Classification Performance Between Machine Learning and Traditional Programming in Java

    University essay from Högskolan Kristianstad/Fakulteten för naturvetenskap

    Author : Abdulrahman Alassadi; Tadas Ivanauskas; [2019]
    Keywords : Classification performance; algorithms; Java; benchmarking; machine learning; naive bayes; heart disease; supervised learning; WEKA;

    Abstract : This study proposes a performance comparison between two Java applications with two different programming approaches, machine learning, and traditional programming. A case where both machine learning and traditional programming can be applied is a classification problem with numeric values. READ MORE

  4. 8. Textural and Mineralogical Characterization of Li-pegmatite Deposit: Using Microanalytical and Image Analysis to Link Micro and Macro Properties of Spodumene in Drill Cores. : Keliber Lithium Project, Finland.

    University essay from Luleå tekniska universitet/Mineralteknik och metallurgi

    Author : Juan Sebastian Guiral Vega; [2018]
    Keywords : Lithium; Image Analysis; Spodumene; Pegmatite; Hyperspectral; Geometallurgy; Machine learning; Mineralogy; characterization; Mineral mapping; Texture; Scanning electron microscopy; SEM; Mineral Processing; Kaustinen; Keliber; Lithium deposit; Geometalurgia; procesamiento mineral; litio; espodumena; pegmatita; machine learning; procesamiento mineral;

    Abstract : Lithium represents one of the strategic elements for the rest of the 21st century due to its increasing demand in technological applications. Therefore, new efforts should be focused on the optimization of mineral characterization processes, which link the ore properties with its behaviour during downstream processes. READ MORE

  5. 9. Resource efficient travel mode recognition

    University essay from KTH/Skolan för datavetenskap och kommunikation (CSC)

    Author : Lovisa Runhem; [2017]
    Keywords : transportation mode recognition; hierarchical classification; smartphone sensors;

    Abstract : In this report we attempt to provide insights to how a resource efficient solution for transportation mode recognition can be implemented on a smartphone using the accelerometer and magnetometer as sensors for data collection. The proposed system uses a hierarchical classification process where instances are first classified as vehicles or non-vehicles, then as wheel or rail vehicles, and lastly as belonging to one of the transportation modes: bus, car, motorcycle, subway, or train. READ MORE

  6. 10. Adaptive Learning

    University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Author : Per Grundtman; [2017]
    Keywords : adaptive learning; machine learning; e-learning; biosyncing; biometric sensors; Empatica E4; Intelligent Tutoring Systems; WEKA;

    Abstract : The purpose of this project is to develop a novel proof-of-concept system in attempt to measure affective states during learning-tasks and investigate whether machine learning models trained with this data has the potential to enhance the learning experience for an individual. By considering biometric signals from a user during a learning session, the affective states anxiety, engagement and boredom will be classified using different signal transformation methods and finally using machine-learning models from the Weka Java API. READ MORE