Sökning: "Förklarbar AI"

Visar resultat 1 - 5 av 11 uppsatser innehållade orden Förklarbar AI.

1. 1. Computationally Efficient Explainable AI: Bayesian Optimization for Computing Multiple Counterfactual Explanantions

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Giorgio Sacchi; [2023]
   Nyckelord :Explainable AI; Counterfactual Explanations CFEs ; Bayesian Optimization BO ; Black-Box Models; Model-Agnostic; Machine Learning ML ; Efficient Computation; High-Stake Decisions; Förklarbar AI; Kontrafaktuell Förklaring CFE ; Bayesiansk Optimering BO ; Svarta lådmodeller; Modellagnostisk; Maskininlärning; Beräkningsmässigt Effektiv; Beslut med höga insatser;

   Sammanfattning : In recent years, advanced machine learning (ML) models have revolutionized industries ranging from the healthcare sector to retail and E-commerce. However, these models have become increasingly complex, making it difficult for even domain experts to understand and retrace the model's decision-making process. LÄS MER

3. 2. Data Augmentations for Improving Vision-Based Damage Detection : in Land Transport Infrastructure

   Master-uppsats, KTH/Lantmäteri – fastighetsvetenskap och geodesi

   Författare :Punnawat Siripatthiti; [2023]
   Nyckelord :Computer Vision; Data Augmentation; Object Detection; Crack Detection; Road Damage Detection; Sleeper Defect Detection; datorseende; dataökning; objektdetektering; sprickdetektering; vägbeläggning; järnvägsslipers;

   Sammanfattning : Crack, a typical term most people know, is a common form of distress or damage in road pavements and railway sleepers. It poses significant challenges to their structural integrity, safety, and longevity. Over the years, researchers have developed various data-driven technologies for image-based crack detection in road and sleeper applications. LÄS MER

4. 3. Development of a Machine Learning Survival Analysis Pipeline with Explainable AI for Analyzing the Complexity of ED Crowding : Using Real World Data collected from a Swedish Emergency Department

   Master-uppsats, KTH/Medicinteknik och hälsosystem

   Författare :Tobias Haraldsson; [2023]
   Nyckelord :SHAP; Explainable AI; Survival Analysis; LOS; Machine Learning; ED Crowding; SHAP; Förklarbar AI; Överlevnadsanalys; LOS; Maskininlärning; Överbelastning på Akuten;

   Sammanfattning : One of the biggest challenges in healthcare is Emergency Department (ED)crowding which creates high constraints on the whole healthcare system aswell as the resources within and can be the cause of many adverse events.Is is a well known problem were a lot of research has been done and a lotof solutions has been proposed, yet the problem still stands unsolved. LÄS MER

5. 4. Primary stage Lung Cancer Prediction with Natural Language Processing-based Machine Learning

   Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Författare :Ahmad Sadek; [2022]
   Nyckelord :Lung cancer; precision medicine; machine learning; explainable AI; Natural Language Processing NLP; patient stratification; oncology; Lungcancer; precisionsmedicin; maskininlärning; NLP; förklarbar AI; patientstratifiering; onkologi;

   Sammanfattning : Early detection reduces mortality in lung cancer, but it is also considered as a challenge for oncologists and for healthcare systems. In addition, screening modalities like CT-scans come with undesired effects, many suspected patients are wrongly diagnosed with lung cancer. LÄS MER

7. 5. Comparison of Logistic Regression and an Explained Random Forest in the Domain of Creditworthiness Assessment

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

   Författare :Marcus Ankaräng; Jakob Kristiansson; [2021]
   Nyckelord :Classification; Creditworthiness; Explainable Artificial Intelligence; Logistic Regression; Machine Learning; Random Forest; SHAP; XAI;

   Sammanfattning : As the use of AI in society is developing, the requirement of explainable algorithms has increased. A challenge with many modern machine learning algorithms is that they, due to their often complex structures, lack the ability to produce human-interpretable explanations. LÄS MER