Using machine learning approaches for multi-omics data analysis: A review
With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …
become essential for biomedical studies to undertake an integrative (combined) approach to …
A systematic literature review on machine learning applications for sustainable agriculture supply chain performance
Agriculture plays an important role in sustaining all human activities. Major challenges such
as overpopulation, competition for resources poses a threat to the food security of the planet …
as overpopulation, competition for resources poses a threat to the food security of the planet …
Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge …
Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be
used for predictive and descriptive modelling as well as for discriminative variable selection …
used for predictive and descriptive modelling as well as for discriminative variable selection …
A review of supervised machine learning algorithms
Supervised machine learning is the construction of algorithms that are able to produce
general patterns and hypotheses by using externally supplied instances to predict the fate of …
general patterns and hypotheses by using externally supplied instances to predict the fate of …
An insider data leakage detection using one-hot encoding, synthetic minority oversampling and machine learning techniques
T Al-Shehari, RA Alsowail - Entropy, 2021 - mdpi.com
Insider threats are malicious acts that can be carried out by an authorized employee within
an organization. Insider threats represent a major cybersecurity challenge for private and …
an organization. Insider threats represent a major cybersecurity challenge for private and …
An up-to-date comparison of state-of-the-art classification algorithms
Current benchmark reports of classification algorithms generally concern common classifiers
and their variants but do not include many algorithms that have been introduced in recent …
and their variants but do not include many algorithms that have been introduced in recent …
Clinical decision support systems for triage in the emergency department using intelligent systems: a review
M Fernandes, SM Vieira, F Leite, C Palos… - Artificial Intelligence in …, 2020 - Elsevier
Abstract Motivation Emergency Departments'(ED) modern triage systems implemented
worldwide are solely based upon medical knowledge and experience. This is a limitation of …
worldwide are solely based upon medical knowledge and experience. This is a limitation of …
[HTML][HTML] Adoption of machine learning techniques in ecology and earth science
A Thessen - One Ecosystem, 2016 - oneecosystem.pensoft.net
Background The natural sciences, such as ecology and earth science, study complex
interactions between biotic and abiotic systems in order to understand and make …
interactions between biotic and abiotic systems in order to understand and make …
Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities
Land subsidence caused by land use change and overexploitation of groundwater is an
example of mismanagement of natural resources, yet subsidence remains difficult to predict …
example of mismanagement of natural resources, yet subsidence remains difficult to predict …
Predicting breast cancer risk using personal health data and machine learning models
Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can
inform screening and preventative actions. Previous works found that adding inputs to the …
inform screening and preventative actions. Previous works found that adding inputs to the …