Essays about: "Klassificerare"

Showing result 16 - 20 of 149 essays containing the word Klassificerare.

  1. 16. Noise Robustness of CNN and SNN for EEG Motor imagery classification

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

    Author : Merlin Sewina; [2023]
    Keywords : Machine Learning; Spiking Neural Networks; Convolutional Neural Networks; EEG;

    Abstract : As an able-bodied human, understanding what someone says during a phone call with a lot of background noise is usually a task that is quite easy for us as we are aware of what the information is we want to hear, e.g. the voice of the person we are talking to, and the information that is noise, e.g. READ MORE

  3. 17. Textual Analysis and Detection of AIGenerated Academic Texts : A Study of ChatGPT Output, User Instructions, and Machine-Learning Classifiers

    University essay from Mittuniversitetet/Institutionen för data- och elektroteknik (2023-)

    Author : Adnan Al Medawer; [2023]
    Keywords : AI-generated texts; ChatGPT; Machine-learning; Text characteristics; Language models; Text Analysis; Detection tool.; AI-genererade texter; ChatGPT; Maskininlärning; Textegenskaper; Språkmodeller; Textanalys; AI-Detektion Verktyg;

    Abstract : Den här studien utforskar den textmässiga likheten mellan AI-genererade texter av ChatGPT och ursprungliga akademiska texter, jämför prestandan hos AI-detekteringsverktyg och maskininlärningsklassificerare, inklusive SVM, Logistic Regression och Random Forest, vid detektering av AI-genererat innehåll, och undersöker hur användarinstruktioner påverkar textkvaliteten. En rad mätvärden som stilometri, sentiment, textlikhet, läsbarhet och relevans användes för att analysera textegenskaper. READ MORE

  4. 18. Applications of Formal Explanations in ML

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

    Author : Nikolaos Smyrnioudis; [2023]
    Keywords : Machine Learning; eXplainable Artifical Intelligence; Formal Verification of Machine Learning models; Maskininlärning; förklarbar artificiell intelligens; formell verifiering av maskininlärningsmodeller;

    Abstract : The most performant Machine Learning (ML) classifiers have been labeled black-boxes due to the complexity of their decision process. eXplainable Artificial Intelligence (XAI) methods aim to alleviate this issue by crafting an interpretable explanation for a models prediction. READ MORE

  5. 19. Classification of imbalanced disparate medical data using ontology

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

    Author : Ludvig Karlsson; Gustav Wilhelm Kopp Sundin; [2023]
    Keywords : Ontology; machine learning; random forest; imbalanced data; oncology; digital transformation;

    Abstract : Through the digitization of healthcare, large volumes of data are generated and stored in healthcare operations. Today, a multitude of platforms and digital infrastructures are used for storage and management of data. The systems lack a common ontology which limits the interoperability between datasets. READ MORE

  6. 20. Reverse Engineering of Deep Learning Models by Side-Channel Analysis

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

    Author : Xuan Wang; [2023]
    Keywords : Side-Channel Attack; Deep Learning; Reverse Engineering; Perceptron Neural Network; Sidokanalattack; Djup lärning; Omvänd Konstruktion; Perceptron Neurala Nätverk;

    Abstract : Side-Channel Analysis (SCA) aims to extract secrets from cryptographic systems by exploiting the physical leakage acquired from implementations of cryptographic algorithms. With the development of Deep Learning (DL), a new type of SCA called Deep Learning Side-Channel Analysis (DLSCA) utilizes the advantages of DL techniques in data features processing to break cryptographic systems more efficiently. READ MORE