Sökning: "linear feature extraction"

Visar resultat 1 - 5 av 32 uppsatser innehållade orden linear feature extraction.

1. 1. 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

3. 2. Intelligence Extraction Using Machine Learning for Threat Identification Purposes : An Overview

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

   Författare :Jonatan Lindgren; [2022]
   Nyckelord :Machine learning; Radar threat identification; Clustering; Performance metrics for unsupervised learning; Feature scaling; Electronic warfare; Maskininlärning; Identifikation av radarhot; Klustring; Prestandamått för oövervakad inlärning; Skalning av dataparametrar; Elektronisk krigsföring;

   Sammanfattning : Radar is an invaluable tool for detecting and assessing threats on land, on the seas and in the air. To properly evaluate threats, radar operators construct threat libraries where the signal characteristics of emitters are stored and mapped to specific types of platforms. LÄS MER

4. 3. Deep learning, LSTM and Representation Learning in Empirical Asset Pricing

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

   Författare :Benjamin von Essen; [2022]
   Nyckelord :LSTM; empirical asset pricing; deep learning; representation learning; neural networks; LSTM; empirisk tillgångsvärdering; djupinlärning; representationsinlärning; neurala nätverk;

   Sammanfattning : In recent years, machine learning models have gained traction in the field of empirical asset pricing for their risk premium prediction performance. In this thesis, we build upon the work of [1] by first evaluating models similar to their best performing model in a similar fashion, by using the same dataset and measures, and then expanding upon that. LÄS MER

5. 4. Sequential Deep Learning Models for Neonatal Sepsis Detection : A suitability assessment of deep learning models for event detection in physiological data

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

   Författare :Henrik Alex Siren; [2022]
   Nyckelord :Neonatal sepsis; Deep learning; Recurrent models; Convolutional models; Physiological data; Neonatal sepsis; Djupinlärning; RNN-modeller; CNN-modeller; Fysiologisk data;

   Sammanfattning : Sepsis is a life-threatening condition that neonatal patients are especially susceptible to. Fortunately, improved bedside monitoring has enabled the collection and use of continuous vital signs data for the purpose of detecting conditions such as sepsis. LÄS MER

7. 5. E-noses equipped with Artificial Intelligence Technology for diagnosis of dairy cattle disease in veterinary

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

   Författare :Farbod Haselzadeh; [2021]
   Nyckelord :Artificial intelligence; Electronic nose; Gas sensor arrays; Principal component analysis; Autoencoder; Veterinary diagnose; Feature extraction; Dimentionality reduction; Normalization; Maskin intelligence; Artificial intelligence; Elektronisk näsa; Gas sensore array; Normalisering; dimensionalitetsminskning; Autoencoder; Klassificering AI; E-nose; Feature Extraction; Normalization; PCA; Autoencoder; Encoder; Decoder; MLP; Classifier; LDA; Support Vector Machine; Logistic Regression; Cross Validation; Signal segmentation;

   Sammanfattning : The main goal of this project, running at Neurofy AB, was that developing an AI recognition algorithm also known as, gas sensing algorithm or simply recognition algorithm, based on Artificial Intelligence (AI) technology, which would have the ability to detect or predict diary cattle diseases using odor signal data gathered, measured and provided by Gas Sensor Array (GSA) also known as, Electronic Nose or simply E-nose developed by the company. Two major challenges in this project were to first overcome the noises and errors in the odor signal data, as the E-nose is supposed to be used in an environment with difference conditions than laboratory, for instance, in a bail (A stall for milking cows) with varying humidity and temperatures, and second to find a proper feature extraction method appropriate for GSA. LÄS MER