Sökning: "Feature Elimination"

Visar resultat 1 - 5 av 31 uppsatser innehållade orden Feature Elimination.

  1. 1. RNN-based Graph Neural Network for Credit Load Application leveraging Rejected Customer Cases

    Uppsats för yrkesexamina på avancerad nivå, Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Oskar Nilsson; Benjamin Lilje; [2023]
    Nyckelord :Machine Learning; Deep Learning; Reject Inference; GNN; GCN; Graph Neural Networks; RNN; Recursive Neural Network; LSTM; Semi-Supervised Learning; Encoding; Decoding; Feature Elimination;

    Sammanfattning : Machine learning plays a vital role in preventing financial losses within the banking industry, and still, a lot of state of the art and industry-standard approaches within the field neglect rejected customer information and the potential information that they hold to detect similar risk behavior.This thesis explores the possibility of including this information during training and utilizing transactional history through an LSTM to improve the detection of defaults. LÄS MER

  3. 2. Data-Driven Success in Infrastructure Megaprojects. : Leveraging Machine Learning and Expert Insights for Enhanced Prediction and Efficiency

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

    Författare :David E.G. Nordmark; [2023]
    Nyckelord :Megaproject; Small sample size; Project management; Random forest; Critical success factors; Feature selection; Recursive feature elimination; Megaprojekt; Små dataurval; Projektledning; Random forest; Kritiska framgångsfaktorer; Variabel urval; Rekursiv variabel eliminering;

    Sammanfattning : This Master's thesis utilizes random forest and leave-one-out cross-validation to predict the success of megaprojects involving infrastructure. The goal was to enhance the efficiency of the design and engineering phase of the infrastructure and construction industries. LÄS MER

  4. 3. Time synchronization error detection in a radio access network

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

    Författare :Moulika Madana; [2023]
    Nyckelord :GNSS - Global Navigation Satellite System; OAS - Over-the air-synchronization; PRTC - primary reference time clock; PTP - precision time protocol; Gauss Jordan elimination; GNN- Graph Neural Network; GNSS -Globalt navigationssatellitsystem; OAS - Över-the-air tidssynkronisering; PRTC - Primär referenstidklocka; PTP - Precisionstidprotokoll; Gauss Jordan eliminering; GNN- Graf neurala nätverk;

    Sammanfattning : Time synchronization is a process of ensuring all the time difference between the clocks of network components(like base stations, boundary clocks, grandmasters, etc.) in the mobile network is zero or negligible. It is one of the important factors responsible for ensuring effective communication between two user-equipments in a mobile network. LÄS MER

  5. 4. Parkinson’s disease tremor assessment: Leveragingsmartphones for symptom measurement

    M1-uppsats, Malmö universitet/Institutionen för datavetenskap och medieteknik (DVMT)

    Författare :Malek Abdul Sater; Reem Mohamed; [2023]
    Nyckelord :;

    Sammanfattning : Parkinson's disease (PD) is a progressive, chronic neurodegenerative disorder that impacts patients' quality of life. Hand tremor is a hallmark motor symptom of PD. However, current clinical tremor assessment methods are time-consuming and expensive and may not capture the full extent of tremor fluctuations. LÄS MER

  7. 5. Predicting Workforce in Healthcare : Using Machine Learning Algorithms, Statistical Methods and Swedish Healthcare Data

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

    Författare :Gabriel Diskay; Carl Joelsson; [2023]
    Nyckelord :Machine Learning ML ; Linear Regression Model LRM ; Gradient Boosting Regressor GBR ; Exponential Smoothing Model ESM ; Workforce Prediction WP ; Healthcare Sector HS ; Labor Policy LP ; Beveridge Curve BC ; Economic Forecasting EF ; Recursive Feature Elimination RFE ; Human Resource Management HRM ;

    Sammanfattning : Denna studie undersöker användningen av maskininlärningsmodeller för att predicera arbetskraftstrender inom hälso- och sjukvården i Sverige. Med hjälp av en linjär regressionmodell, en Gradient Boosting Regressor-modell och en Exponential Smoothing-modell syftar forskningen för detta arbete till att ge viktiga insikter för underlaget till makroekonomiska överväganden och att ge en djupare förståelse av Beveridge-kurvan i ett sammanhang relaterat till hälso- och sjukvårdssektorn. LÄS MER