Journal of Environmental Sciences Study Reveals How Artificial Intelligence can Transform PM2.5 Monitoring

New deep-learning framework reconstructs hourly PM2.5 chemical composition using air-quality and meteorological data ...
IEEE

Photovoltaic Power Generation Forecasting Model Based on Bayesian Optimization of CNN-BiLSTM-Time Weighted Attention

Abstract: Photovoltaic power generating power prediction is very important for energy management and power grid dispatching. However, due to the nonlinearity and randomness of meteorological ...
CNN

Tesla scraps Model S and Model X to build robots

Tesla CEO Elon Musk, who turned an upstart electric vehicle maker into an industry-changing powerhouse, is pulling the plug on the two models that helped get him there, as he struggles with another ...
KTVZ

A bomb cyclone is more likely to impact parts of the East Coast this weekend. Here are the updated scenarios

(CNN) — A powerful winter storm will form off the East Coast this weekend amid a bout of deadly cold, but predicting its track, intensity and who could see snow will take another day or so to become ...
IEEE

Multi-Scale CNN-Based Lung Cancer Classification: Integrating Deep Learning, Texture Analysis, Bayesian Optimization and Ensemble Learning for Enhanced Diagnostic Accuracy

Abstract: A proper classification system of lung cancer stands vital for early-stage diagnosis along with therapy planning. This research presents a combination of multi-scale Convolutional Neural ...
IEEE

A Novel Evolutionary Bayesian Optimization Algorithm Based on Decomposition for Expensive Constrained Multiobjective Optimization Problems

Abstract: This article proposes a novel constrained multiobjective evolutionary Bayesian optimization algorithm based on decomposition (named CMOEBO/D) for expensive constrained multiobjective ...
IEEE

Safe Bayesian Optimization Across Noise Models via Scenario Programming

Abstract: Safe Bayesian optimization (BO) with Gaussian processes is an effective tool for tuning control policies in safety-critical real-world systems, specifically due to its sample efficiency and ...
IEEE

Fast Kernel-Based Regularized System Identification using Bayesian Optimization

Abstract: This paper proposes a scalable, matrix-free approach to kernel-based regularization for finite impulse response estimation. Our methodology is based on Bayesian optimization, a gradient-free ...