Essays about: "state classification"

Showing result 1 - 5 of 213 essays containing the words state classification.

  1. 1. Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison

    University essay from Linköpings universitet/Datorseende

    Author : Rolf Sievert; [2021]
    Keywords : Machine learning; Multiclass; Deep learning; Instance segmentation; Object segmentation; Iterative stratification; Mask R-CNN; DetectoRS; Imbalanced dataset; Classification; Detection; Segmentation; Litter; Trash; TACO; COCO; MMDetection; Multinomial; Cybercom; AI; Artificial intelligence; Land-based litter; Computer vision; Maskininlärning; Djupinlärning; Instanssegmentering; Objektsegmentering; Mask R-CNN; DetectoRS; Obalanserat dataset; Klassificering; Detektion; Segmentering; Skräp; TACO; COCO; MMDetection; Multinomial; Cybercom; AI; Artificiell intelligens; Datorseende;

    Abstract : Instance segmentation has a great potential for improving the current state of littering by autonomously detecting and segmenting different categories of litter. With this information, litter could, for example, be geotagged to aid litter pickers or to give precise locational information to unmanned vehicles for autonomous litter collection. READ MORE

  2. 2. Evaluation of Approaches for Representation and Sentiment of Customer Reviews

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

    Author : Stavros Giorgis; [2021]
    Keywords : machine learning; nlp; text analytics; sentiment analysis; transformers; tfidf; bow; fasttext; word2vec; bert; xlnet; roberta; maskininlärning; nlp; textanalys; sentimentanalys; transformatorer; tfidf; bow; fasttext; word2vec; bert; xlnet; roberta;

    Abstract : Classification of sentiment on customer reviews is a real-world application for many companies that offer text analytics and opinion extraction on customer reviews on different domains such as consumer electronics, hotels, restaurants, and car rental agencies. Natural Language Processing’s latest progress has seen the development of many new state-of-the-art approaches for representing the meaning of sentences, phrases, and words in the text using vector space models, so-called embeddings. READ MORE

  3. 3. Brain disease classification using multi-channel 3D convolutional neural networks

    University essay from Linköpings universitet/Statistik och maskininlärning

    Author : Andreas Christopoulos Charitos; [2021]
    Keywords : Deep Learning DL ; fMRI; CNNs; Tensorflow Keras; ASD; Medical Imaging;

    Abstract : Functional magnetic resonance imaging (fMRI) technology has been used in the investigation of human brain functionality and assist in brain disease diagnosis. While fMRI can be used to model both spatial and temporal brain functionality, the analysis of the fMRI images and the discovery of patterns for certain brain diseases is still a challenging task in medical imaging. READ MORE

  4. 4. Data-Driven Engine Fault Classification and Severity Estimation Using Interpolated Fault Modes from Limited Training Data

    University essay from Linköpings universitet/Fordonssystem

    Author : Joakim Säfdal; [2021]
    Keywords : ;

    Abstract : Today modern vehicles are expected to be safe, environmentally friendly, durable and economical. Monitoring the health of the vehicle is therefore more important than ever. As the complexity of vehicular systems increases the need for efficient monitoring methods has increased as well. READ MORE

  5. 5. Detecting Signal Corruptions in Voice Recordings for Speech Therapy

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

    Author : Helmer Nylén; [2021]
    Keywords : Noise; Classification algorithms; Audio recording; Machine learning; Acoustic signal processing; Störning; Klassificeringsalgoritmer; Ljudinspelning; Maskininlärning; Akustisk signalbehandling;

    Abstract : When recording voice samples from a patient in speech therapy the quality of the recording may be affected by different signal corruptions, for example background noise or clipping. The equipment and expertise required to identify small disturbances are not always present at smaller clinics. READ MORE