Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms
Objective: Mammogram-based automatic breast cancer detection has a primary role in
accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is …
accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is …
Fairness amidst non‐IID graph data: A literature review
The growing importance of understanding and addressing algorithmic bias in artificial
intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the …
intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the …
Fairness in large language models: A taxonomic survey
Large Language Models (LLMs) have demonstrated remarkable success across various
domains. However, despite their promising performance in numerous real-world …
domains. However, despite their promising performance in numerous real-world …
Research on unsupervised feature learning for android malware detection based on restricted Boltzmann machines
Android malware detection has attracted much attention in recent years. Existing methods
mainly research on extracting static or dynamic features from mobile apps and build mobile …
mainly research on extracting static or dynamic features from mobile apps and build mobile …
FairAIED: Navigating fairness, bias, and ethics in educational AI applications
The integration of Artificial Intelligence (AI) into education has transformative potential,
providing tailored learning experiences and creative instructional approaches. However, the …
providing tailored learning experiences and creative instructional approaches. However, the …
Fair decision-making under uncertainty
There has been concern within the artificial intelligence (AI) community and the broader
society regarding the potential lack of fairness of AI-based decision-making systems …
society regarding the potential lack of fairness of AI-based decision-making systems …
Preventing discriminatory decision-making in evolving data streams
Bias in machine learning has rightly received significant attention over the past decade.
However, most fair machine learning (fair-ML) works to address bias in decision-making …
However, most fair machine learning (fair-ML) works to address bias in decision-making …
An effective ensemble machine learning approach to classify breast cancer based on feature selection and lesion segmentation using preprocessed mammograms
Simple Summary The screening of breast cancer in its earlier stages can play a crucial role
in minimizing mortality rate by enabling clinicians to administer timely treatments and …
in minimizing mortality rate by enabling clinicians to administer timely treatments and …
Towards fair machine learning software: Understanding and addressing model bias through counterfactual thinking
The increasing use of Machine Learning (ML) software can lead to unfair and unethical
decisions, thus fairness bugs in software are becoming a growing concern. Addressing …
decisions, thus fairness bugs in software are becoming a growing concern. Addressing …
Breast mass classification using diverse contextual information and convolutional neural network
Masses are one of the early signs of breast cancer, and the survival rate of women suffering
from breast cancer can be improved if masses can be correctly identified as benign or …
from breast cancer can be improved if masses can be correctly identified as benign or …