[HTML][HTML] A survey, review, and future trends of skin lesion segmentation and classification
Abstract The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion
analysis is an emerging field of research that has the potential to alleviate the burden and …
analysis is an emerging field of research that has the potential to alleviate the burden and …
Dealing with missing values in proteomics data
Proteomics data are often plagued with missingness issues. These missing values (MVs)
threaten the integrity of subsequent statistical analyses by reduction of statistical power …
threaten the integrity of subsequent statistical analyses by reduction of statistical power …
Early prediction of diabetes using an ensemble of machine learning models
Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of
significant complications, including cardiovascular disease, kidney failure, diabetic …
significant complications, including cardiovascular disease, kidney failure, diabetic …
[HTML][HTML] DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation
Abstract Background and Objective: Although automated Skin Lesion Classification (SLC) is
a crucial integral step in computer-aided diagnosis, it remains challenging due to variability …
a crucial integral step in computer-aided diagnosis, it remains challenging due to variability …
Dig information of nanogenerators by machine learning
J Zhang, Y Yu, L Zhang, J Chen, X Wang, X Wang - Nano Energy, 2023 - Elsevier
Nanogenerators (NGs) are one of the promising energy solutions, which collect different
forms of energy in the environment, and have great potential applications in self-powered …
forms of energy in the environment, and have great potential applications in self-powered …
A machine learning model for predicting deterioration of COVID-19 inpatients
O Noy, D Coster, M Metzger, I Atar… - Scientific reports, 2022 - nature.com
The COVID-19 pandemic has been spreading worldwide since December 2019, presenting
an urgent threat to global health. Due to the limited understanding of disease progression …
an urgent threat to global health. Due to the limited understanding of disease progression …
Effective handling of missing values in datasets for classification using machine learning methods
A Palanivinayagam, R Damaševičius - Information, 2023 - mdpi.com
The existence of missing values reduces the amount of knowledge learned by the machine
learning models in the training stage thus affecting the classification accuracy negatively. To …
learning models in the training stage thus affecting the classification accuracy negatively. To …
Handling missing values and imbalanced classes in machine learning to predict consumer preference: Demonstrations and comparisons to prominent methods
Y Liu, B Li, S Yang, Z Li - Expert Systems with Applications, 2024 - Elsevier
Consumer preference prediction aims to predict consumers' future purchases based on their
historical behavior-level data. Using machine learning algorithms, the prediction results …
historical behavior-level data. Using machine learning algorithms, the prediction results …
Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO3
We used data-driven methods to understand the formation of impurity phases in BiFeO3 thin-
film synthesis through the sol–gel technique. Using a high-quality dataset of 331 synthesis …
film synthesis through the sol–gel technique. Using a high-quality dataset of 331 synthesis …
A data quality management framework for equipment failure risk estimation: Application to the oil and gas industry
In engineering asset management, accurate failure risk estimation is essential for averting
equipment breakdowns and optimizing risk-based maintenance strategies. Data quality and …
equipment breakdowns and optimizing risk-based maintenance strategies. Data quality and …