Sökning: "Bias in Machine Learning"

Visar resultat 1 - 5 av 50 uppsatser innehållade orden Bias in Machine Learning.

  1. 1. Modeling and Interpreting CTG Curves from Labor Using Machine Learning and Pattern Recognition

    Master-uppsats, Lunds universitet/Matematisk statistik

    Författare :Sandra Kandefelt; Sara Perklev; [2022]
    Nyckelord :machine learning; CTG; pattern recognition; labor; TOCO; FHR; Mathematics and Statistics;

    Sammanfattning : The main monitoring method during labor is cardiotocography, CTG, which measures the fetal heartbeat, FHR, as well as the uterine contractions, TOCO. The CTG is a valuable tool in assessing the fetal status and it is evaluated intermittently by clinicians during the progress of the labor. LÄS MER

  2. 2. Jämförelse mellan hältbedömning av häst utförd av 12 klinikveterinärer och en applikation baserad på datorseende

    Uppsats för yrkesexamina på avancerad nivå, SLU/Dept. of Anatomy, Physiology and Biochemistry

    Författare :Julia Johanna Stenius; [2022]
    Nyckelord :hälta; rörelseanalys; rörelseasymmetri; hältbedömning; objektiv rörelseanalys; artificiell intelligens; datorseende; neurala nätverk; överensstämmelse;

    Sammanfattning : Ortopediska sjukdomar är hästens största medicinska problem världen över och sjukdomar i rörelseapparaten är den vanligaste anledningen till att svenska hästar behandlas av en veterinär. För framgångsrik behandling av ortopediska problem krävs i första hand en korrekt identifiering av det halta benet. LÄS MER

  3. 3. Using XAI Tools to Detect Harmful Bias in ML Models

    Kandidat-uppsats, Umeå universitet/Institutionen för datavetenskap

    Författare :Klaus Virtanen; [2022]
    Nyckelord :Explainable AI; XAI; Machine Learning; Bias; Bias in Machine Learning; LIME; SHAP;

    Sammanfattning : In the past decade, machine learning (ML) models have become farmore powerful, and are increasingly being used in many important contexts. At the same time, ML models have become more complex, and harder to understand on their own, which has necessitated an interesting explainable AI (XAI), a field concerned with ensuring that ML and other AI system can be understood by human users and practitioners. LÄS MER

  4. 4. Design, implementation and evaluation of a deep learning prototype to classify non-pigmented malignant skin cancer from dermatoscopic images

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Maria del Pilar Aguilera Manzanera; [2022]
    Nyckelord :Melanoma; Skin cancer; Dermatoscopy; Image classification; Machine learning; Artificial intelligence; Convolutional neural networks; Dermatology; Squamous cell carcinoma; Basal cell carcinoma; Actinic keratosis; Computer-aided Diagnostics; Digital dermatology; Technology and Engineering;

    Sammanfattning : The current trends for most fair-skinned populations are that the incidence of melanoma and non-pigmented skin lesions is growing, and this growing trend will continue for the upcoming years. The emergence of deep learning networks and their promising results in solving real-world healthcare problems and improving diagnostic accuracy opens new possibilities. LÄS MER

  5. 5. Under the Guise of Machine Neutrality : Machine Learning Uncertainty Exploration as Design Material to Identify Gender Bias in AI Systems

    Master-uppsats, Malmö universitet/Institutionen för konst, kultur och kommunikation (K3)

    Författare :Gelson Veloso; [2022]
    Nyckelord :Interaction Design; AI; Feminist HCI; gender; uncertainty; Explainable AI;

    Sammanfattning : Structural gendered inequality permeates intelligent systems, shaping everyday lives and reinforcing gender oppression. This study investigates how uncertainty, as an inherent characteristic of Machine Learning (ML) models, can be translated as a design material to highlight gender bias in Artificial Intelligence (AI) systems. LÄS MER