Essays about: "Risk model evaluation"

Showing result 41 - 45 of 257 essays containing the words Risk model evaluation.

  1. 41. A Smart Patent Monitoring Assistant : Using Natural Language Processing

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

    Author : Selemawit Fsha Nguse; [2022]
    Keywords : Patent monitoring; natural language processing; bidirectional encoder representations from transformers; machine learning; patent classification; relevance ranking; Patentövervakning; natural language processing; bidirectional encoder representations from transformers; maskininlärning; klassificering av patent; relevansranking;

    Abstract : Patent monitoring is about tracking the upcoming inventions in a particular field, predicting future trends, and specific intellectual property rights of interest. It is the process of finding relevant patents on a particular topic based on a specific query. With patent monitoring, one can keep them updated on the new technology in the market. READ MORE

  3. 42. Risk Evaluation in a ML-Approximated Portfolio Environment

    University essay from KTH/Matematik (Avd.)

    Author : Filip Franzén; Karl Axel Nord; [2022]
    Keywords : Financial risk management; forecasting; machine learning; FMCG; Finansiell riskhantering; prognostisering; maskininlärning; konsumtionsvaror;

    Abstract : This thesis explores and evaluates the forecasting application of the machine learning method Gradient Boosting Decision Trees. This method is used to forecast the demand of the online grocery market with a 7-day time horizon. The thesis was conducted in collaboration with the online grocery company Mathem. READ MORE

  4. 43. Analysis of Brain Signals from Patients with Parkinson’s Disease using Self-Supervised Learning

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

    Author : Emma Lind; [2022]
    Keywords : Machine Learning; Self-supervised learning; Feature extraction; Parkinson’s Disease; Magnetoencephalography; Electroencephalogram; Maskininlärning; Självlärande inlärning; Särdragsextraktion; Parkinsons sjukdom; Magnetoencefalografi; Elektroencefalografi;

    Abstract : Parkinson’s disease (PD) is one of the most common neurodegenerative brain disorders, commonly diagnosed and monitored via clinical examinations, which can be imprecise and lead to a delayed or inaccurate diagnosis. Therefore, recent research has focused on finding biomarkers by analyzing brain networks’ neural activity to find abnormalities associated with PD pathology. READ MORE

  5. 44. Risk Stratification of Acute Coronary Syndrome using Machine Learning : An analysis of CLEOS-CPDS data

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

    Author : Glacier Ali; Rebecka Gustavsson; [2022]
    Keywords : Chest pain; Acute coronary syndrome; Computerized history taking; Machine learning; Risk stratification; Bröstsmärtor; Akuta koronara syndrom; Digitaliserad insamling av medicinsk bakgrund; Maskininlärning; Riskbedömning;

    Abstract : Chest pain is one of the most common complaints amongst patients seeking urgent medical care at hospitals. Chest pain can be a symptom of serious cardiovascular disease such as acute coronary syndrome (ACS), however, most underlying causes are benign. Risk stratification in early stages of medical evaluation is difficult. READ MORE

  6. 45. An evaluation of deep learning models for urban floods forecasting

    University essay from KTH/Geoinformatik

    Author : Yang Mu; [2022]
    Keywords : Urban flooding forecasting; Convolutional neural networks; Deep learning; Physically-based simulation; Recurrent neural network; Stadsöversvämningsprognoser; konvolutionella neurala nätverk; djupinlärning; fysiskt baserad simulering; återkommande neurala nätverk;

    Abstract : Flood forecasting maps are essential for rapid disaster response and risk management, yet the computational complexity of physically-based simulations hinders their application for efficient high-resolution spatial flood forecasting. To address the problems of high computational cost and long prediction time, this thesis proposes to develop deep learning neural networks based on a flood simulation dataset, and explore their potential use for flood prediction without learning hydrological modelling knowledge from scratch. READ MORE