Search
Search Results
-
Diabetes prediction using Shapley additive explanations and DSaaS over machine learning classifiers: a novel healthcare paradigm
Technologies like cloud computing, Artificial Intelligence (AI), and Machine intelligence technologies must combine to accomplish computational...
-
Explainable prediction of deposited film thickness in IC fabrication with CatBoost and SHapley Additive exPlanations (SHAP) models
This paper presents a study on develo** a chemical vapor deposition film thickness prediction model for semiconductor IC manufacturing. Traditional...
-
BO–SHAP–BLS: a novel machine learning framework for accurate forecasting of COVID-19 testing capabilities
The rapid spread of COVID-19 has resulted in a large number of infections and significant economic impact on countries worldwide, and COVID-19...
-
Considerations when learning additive explanations for black-box models
Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for...
-
DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning
Ensuring the transparency of machine learning models is vital for their ethical application in various industries. There has been a concurrent trend...
-
Explaining Eye Diseases Detected by Machine Learning Using SHAP: A Case Study of Diabetic Retinopathy and Choroidal Nevus
Most visual impairment and eye cancers are preventable if detected in their early stages. Diabetic retinopathy (DR) is a significant cause of...
-
Using interpretable machine learning approaches to predict and provide explanations for student completion of remedial mathematics
The successful completion of remedial mathematics is widely recognized as a crucial factor for college success. However, there is considerable...
-
Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP
ContextThe identification of bugs within issues reported to an issue tracking system is crucial for triage. Machine learning models have shown...
-
Diabetic retinopathy disease detection using shapley additive ensembled densenet-121 resnet-50 model
Diabetic retinopathy (DR) is a common eye disease that results in vision loss by damaging the blood vessels. Diabetic patients are at high risk of...
-
Testing machine learning explanation methods
There are many methods for explaining why a machine learning model produces a given output in response to a given input. The relative merits of these...
-
Blending Shapley values for feature ranking in machine learning: an analysis on educational data
In educational institutions, it is now more important than ever to deliver high-quality academic instruction, and educational data mining is...
-
Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection
Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of...
-
Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values
Shapley values have achieved great popularity in explainable artificial intelligence. However, with standard sampling methods, resulting feature...
-
Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods
In the context of high-dimensional credit card fraud data, researchers and practitioners commonly utilize feature selection techniques to enhance the...
-
XAI-based cross-ensemble feature ranking methodology for machine learning models
Artificial Intelligence (AI) as one robust technology has been used in various fields, making innovative society possible and changing our...
-
Towards Refined Classifications Driven by SHAP Explanations
Machine Learning (ML) models are inherently approximate; as a result, the predictions of an ML model can be wrong. In applications where errors can...
-
Software Defects Detection in Explainable Machine Learning Approach
In the era of ubiquitous software systems, the complexity and urgency in software production have often led to compromises in quality. Traditional...
-
Recent advances in applications of machine learning in reward crowdfunding success forecasting
Entrepreneurs and small businesses have increasingly used reward-based crowdfunding to raise capital for their creative projects, whose success is...
-
Assessment of learning parameters for students' adaptability in online education using machine learning and explainable AI
Technology Enabled Learning (TEL) has a major impact on the learning adaptability of the learners. During the COVID-19 pandemic, there has been a...
-
Artificial Intelligence Model Interpreting Tools: SHAP, LIME, and Anchor Implementation in CNN Model for Hand Gestures Recognition
Explainable AI (XAI) are the tools and frameworks of artificial intelligence applications that make it easier to trust the results and outcomes...