Sökning: "Linear discriminant analysis"

Visar resultat 6 - 10 av 44 uppsatser innehållade orden Linear discriminant analysis.

1. 6. Sentiment Classification of Amazon Product Reviews

   Master-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Jakob Leth; [2022]
   Nyckelord :;

   Sammanfattning : Opinions and attitudes expressed online contain information useful to companies and organizations. By using machine learning and statistical models automated ways of understanding online behavior can be implemented. LÄS MER

3. 7. Sleep apnea prediction in a Swedish cohort : Can the STOP-Bang questionnaire be improved?

   Magister-uppsats, Umeå universitet/Statistik

   Författare :Miriam Gladh; [2022]
   Nyckelord :;

   Sammanfattning : Obstructive sleep apnea (OSA) is defined as more than five breathing pauses per hour of sleep, an apnea-hypopnea index (AHI) > 5. STOP-Bang is a questionnaire that predicts the risk of sleep apnea based on risk factors, like snoring, hypertension, and throat circumference greater than 40 cm. LÄS MER

4. 8. Classification of Repeated Measurement Data Using Growth Curves and Neural Networks

   Master-uppsats, Linköpings universitet/Tillämpad matematik; Linköpings universitet/Tekniska fakulteten

   Författare :Kasper Andersson; [2022]
   Nyckelord :Repeated Measurement Data; Sequential Data; Growth Curve Model; Linear Discriminant Analysis; Neural Network; Recurrent Neural Network; LSTM; Upprepade Mätningar; Sekventiell Data; Tillväxtkurvor; Linjär Diskriminantanalys; Neurala Nätverk; Recurrent Neurala Nätverk; LSTM;

   Sammanfattning : This thesis focuses on statistical and machine learning methods designed for sequential and repeated measurement data. We start off by considering the classic general linear model (MANOVA) followed by its generalization, the growth curve model (GMANOVA), designed for analysis of repeated measurement data. LÄS MER

5. 9. 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

7. 10. Classification of a Sensor Signal Attained By Exposure to a Complex Gas Mixture

   Master-uppsats, Linköpings universitet/Statistik och maskininlärning

   Författare :Rabnawaz Jan Sher; [2021]
   Nyckelord :Classification; Random Forest; Linear Discriminant Analysis; Naive Bayes; Principal Component Analysis; Drift; Baseline Compensation; Normalization; Sensor; Signal Preprocessing.;

   Sammanfattning : This thesis is carried out in collaboration with a private company, DANSiC AB This study is an extension of a research work started by DANSiC AB in 2019 to classify a source. This study is about classifying a source into two classes with the sensitivity of one source higher than the other as one source has greater importance. LÄS MER