Edge Computing in Healthcare Using Machine Learning: A Systematic Literature Review
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.Three key parts of our review. This review examines recent research on integrating machine learning with edge computing in healthcare. It is structured around three key parts: the demographic characteristics of the selected studies; the themes, tools, motivations, and data sources; and the key limitations, challenges, and future research directions ...
Amir Mashmool +7 more
wiley +1 more source
A Soft Sensor Modeling Method Based on Local Migration Modeling Framework. [PDF]
Sensors (Basel)Wang B, Huang S, Cai H.
europepmc +1 more source
Generating Stable and Metastable Critical Points in Uncertain Systems via Flow‐Based Models
Expert Systems, Volume 43, Issue 3, March 2026.ABSTRACT This work proposes the use of conditional flow‐based generative models to learn an approximation of the distribution of the critical points of a cost function. This approximation is used to incrementally identify all critical points, in the feasible domain of said function, by iteratively alternating the sampling of the distribution and the ...
Callum Wilson, Massimiliano Vasile
wiley +1 more source
Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm for Mobile Edge Computing Networks (EHRL). [PDF]
PLoS OneBayoumi H +3 more
europepmc +1 more source
Expert Systems, Volume 43, Issue 3, March 2026.ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN ...
Abdullah Al Siam +3 more
wiley +1 more source
Enhancing cardiac disease prediction with explainable bidirectional LSTM. [PDF]
Sci RepLipsa S +5 more
europepmc +1 more source
Quantifying the Influence of Climate on Storm Activity Using Machine Learning
Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.Abstract Midlatitude storms vary due to the slowly evolving climate and the rapidly changing synoptic conditions. While the impact of both factors has been studied extensively, their relative contributions remain poorly quantified. We use 84 years of ERA‐5 reanalysis data and convolutional neural networks to assess the relative importance of seasonal ...
Or Hadas, Yohai Kaspi
wiley +1 more source
MPIDNN-GPPI: multi-protein language model with an improved deep neural network for generalized protein‒protein interaction prediction. [PDF]
BMC GenomicsLi Y, Wang C, Gu H, Long Z, Fan M, Li L.
europepmc +1 more source
Predictability of Storms in an Idealized Climate Revealed by Machine Learning
Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.Abstract The midlatitude climate and weather are shaped by storms, yet the factors governing their predictability remain insufficiently understood. Here, we use a Convolutional Neural Network (CNN) to predict and quantify uncertainty in the intensity growth and trajectory of over 200,000 storms simulated with a 200‐year aquaplanet GCM.
Wuqiushi Yao, Or Hadas, Yohai Kaspi
wiley +1 more source
Firefly algorithm and DNN for improved contactless heart rate measurement from videos. [PDF]
Sci RepSaini R +6 more
europepmc +1 more source