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Tags (2) : explainable AI
Machine Learning Boosts Biosensor Accuracy for Microcystin Detection
07/10/2026 | Companies
analytical chemistry,
artificial intelligence,
biosensor calibration,
biosensors,
climate change,
cyanobacteria,
cyanobacterial toxins,
cyanotoxins,
drinking water safety,
electrical conductivity,
electrochemical impedance,
electrochemical sensors,
environmental health,
environmental monitoring,
environmental sensing,
explainable AI,
freshwater ecosystems,
freshwater monitoring,
Hanbat National University,
harmful algal blooms,
Machine learning,
MC-LR,
microcystin-LR,
on-site water testing,
pH,
predictive modeling,
rapid toxin detection,
recreational water,
screen-printed carbon electrode,
SHAP,
SPCE biosensor,
total dissolved solids,
toxin detection,
turbidity,
University of Central Florida,
UV254,
water pollution,
water quality,
water research,
XGBoost
AI-Powered 6G: Key Use Cases, Network Design, and Validation Insights
11/17/2025 | Companies
6G,
6G design,
AI,
AI-driven simulation,
AI-native networks,
anomaly detection,
autonomous mobility,
beamforming,
C-V2X,
channel estimation,
CSI compression,
cybersecurity,
deep learning,
digital twin networks,
digital twins,
EDA tools,
edge intelligence,
explainable AI,
GANs,
generative AI,
GNNs,
hardware-in-the-loop,
holographic communication,
machine learning,
network automation,
network performance testing,
network validation,
physics-informed AI,
predictive maintenance,
RAN optimization,
reinforcement learning,
RFIC design,
RIS,
semantic communication,
smart factories,
sub-terahertz,
synthetic data,
system-level testing,
telecom,
THz communications,
transformers,
UM-MIMO,
wireless networks
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