Sökning: "Scikit-Learn"

Visar resultat 1 - 5 av 18 uppsatser innehållade ordet Scikit-Learn.

  1. 1. Comparing Julia and Python : An investigation of the performance on image processing with deep neural networks and classification

    Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för programvaruteknik

    Författare :Viktor Axillus; [2020]
    Nyckelord :julia; python; performance; comparison; machine learning; image processing; GPU; GPU-acceleration; neural networks; autoencoder; classification; knn; k-nearest neighbor;

    Sammanfattning : Python is the most popular language when it comes to prototyping and developing machine learning algorithms. Python is an interpreted language that causes it to have a significant performance loss compared to compiled languages. LÄS MER

  2. 2. A Comparative Study of Facial Recognition Techniques : With focus on low computational power

    Kandidat-uppsats, Högskolan i Skövde/Institutionen för informationsteknologi; Högskolan i Skövde/Institutionen för informationsteknologi; Högskolan i Skövde/Institutionen för informationsteknologi

    Författare :Timmy Schenkel; Oliver Ringhage; Nicklas Branding; [2019]
    Nyckelord :Machine Learning; Facial Recognition; Low Computational Power;

    Sammanfattning : Facial recognition is an increasingly popular security measure in scenarios with low computational power, such as phones and Raspberry Pi’s. There are many facial recognition techniques available. The aim is to compare three such techniques in both performance and time metrics. LÄS MER

  3. 3. Predicting house prices with machine learning methods

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

    Författare :Isak Engström; Alan Ihre; [2019]
    Nyckelord :;

    Sammanfattning : In this study, the machine learning algorithms k-Nearest-Neighbours regression (k-NN) and Random Forest (RF) regression were used to predict house prices from a set of features in the Ames housing data set. The algorithms were selected from an assessment of previous research and the intent was to compare their relative performance at this task. LÄS MER

  4. 4. Machine Learning for a Network-based Intrusion Detection System : An application using Zeek and the CICIDS2017 dataset

    M1-uppsats, KTH/Hälsoinformatik och logistik

    Författare :Vilhelm Gustavsson; [2019]
    Nyckelord :Machine Learning; Flow-based traffic characterization; Intrusion Detection System IDS ; Zeek; Bro; CICIDS2017; Scikit-Learn; Maskininlärning; Flödesbaserad trafik-karaktärisering; Intrångsdetekteringssystem IDS ; Zeek; Bro; CICIDS2017; Scikit-Learn;

    Sammanfattning : Cyber security is an emerging field in the IT-sector. As more devices are connected to the internet, the attack surface for hackers is steadily increasing. Network-based Intrusion Detection Systems (NIDS) can be used to detect malicious traffic in networks and Machine Learning is an up and coming approach for improving the detection rate. LÄS MER

  5. 5. A comparative study of social bot classification techniques

    Kandidat-uppsats, Högskolan i Skövde/Institutionen för informationsteknologi; Högskolan i Skövde/Institutionen för informationsteknologi; Högskolan i Skövde/Institutionen för informationsteknologi

    Författare :Filip Örnbratt; Jonathan Isaksson; Mario Willing; [2019]
    Nyckelord :manual bot classification; social bot; metadata; machine learning; supervised learning; unsupervised learning; random forest; k-means;

    Sammanfattning : With social media rising in popularity over the recent years, new so called social bots are infiltrating by spamming and manipulating people all over the world. Many different methods have been presented to solve this problem with varying success. LÄS MER