Interpretability in the medical field: A systematic mapping and review study

H Hakkoum, I Abnane, A Idri - Applied Soft Computing, 2022 - Elsevier
Context: Recently, the machine learning (ML) field has been rapidly growing, mainly owing
to the availability of historical datasets and advanced computational power. This growth is …

Applications of machine learning in networking: a survey of current issues and future challenges

MA Ridwan, NAM Radzi, F Abdullah, YE Jalil - IEEE access, 2021 - ieeexplore.ieee.org
Communication networks are expanding rapidly and becoming increasingly complex. As a
consequence, the conventional rule-based algorithms or protocols may no longer perform at …

[HTML][HTML] A comparison among interpretative proposals for Random Forests

M Aria, C Cuccurullo, A Gnasso - Machine Learning with Applications, 2021 - Elsevier
The growing success of Machine Learning (ML) is making significant improvements to
predictive models, facilitating their integration in various application fields. Despite its …

Comparing machine learning algorithms to predict vegetation fire detections in Pakistan

F Shahzad, K Mehmood, K Hussain, I Haidar… - Fire Ecology, 2024 - Springer
Vegetation fires have major impacts on the ecosystem and present a significant threat to
human life. Vegetation fires consists of forest fires, cropland fires, and other vegetation fires …

Cautious weighted random forests

H Zhang, B Quost, MH Masson - Expert Systems with Applications, 2023 - Elsevier
Random forest is an efficient and accurate classification model, which makes decisions by
aggregating a set of trees, either by voting or by averaging class posterior probability …

ForestPrune: compact depth-pruned tree ensembles

B Liu, R Mazumder - International Conference on Artificial …, 2023 - proceedings.mlr.press
Tree ensembles are powerful models that achieve excellent predictive performances, but
can grow to unwieldy sizes. These ensembles are often post-processed (pruned) to reduce …

FDPBoost: Federated differential privacy gradient boosting decision trees

Y Li, Y Feng, Q Qian - Journal of Information Security and Applications, 2023 - Elsevier
The big data era has led to an exponential increase in data usage, resulting in significantly
advancements in data-driven domains and data mining. However, due to privacy and …

Driver identification methods in electric vehicles, a review

D Zhao, J Hou, Y Zhong, W He, Z Fu… - World Electric Vehicle …, 2022 - mdpi.com
Driver identification is very important to realizing customized service for drivers and road
traffic safety for electric vehicles and has become a research hotspot in the field of modern …

Interpretable support vector machine for authentication of omega-3 fish oil supplements using Raman spectroscopy

WF Soares, BD Chinchin-Piñan, RM Silva, JEL Villa - Food Control, 2024 - Elsevier
The use of machine learning algorithms to develop automated analytical methods and smart
sensors has drastically increased in recent years. Although these algorithms often provide …

Machine-learning prediction of the computed band gaps of double perovskite materials

J Zhang, Y Li, X Zhou - arXiv preprint arXiv:2301.03372, 2023 - arxiv.org
Prediction of the electronic structure of functional materials is essential for the engineering of
new devices. Conventional electronic structure prediction methods based on density …