An adaptive detection framework to identify low-rate Distributed Denial of Service (DDoS) attacks in cloud environments. Leveraging the Decision Tree machine learning algorithm, the framework offers a ...

This important work sets out to identify the neural substrates of associative fear responses in adult zebrafish. Through a compelling and innovative paradigm and analysis, the authors suggest brain ...

Their proposed model, outlined in a paper published in Nature Human Behavior, combines advanced machine learning algorithms with behavioral science theory. "Human-decision research is rich in ...

The machine learning model the team implemented overcame the challenge of limited data to incorporate the alpha conotoxins' amino acid sequences, secondary structure propensities and electrostatic ...

Quantum machine learning takes classical data and encodes it in quantum states. The quantum computer can then uncover patterns in the data that would be hard for classical systems to detect.

In this research work authors have experimentally validated a blend of Machine Learning and Nonlinear Model Predictive Control (NMPC) framework designed to track the temperature profile in a Batch ...

Context Key Objective Can readily available laboratory data train and test the performance of a machine learning (ML)–based risk models for abnormal lymphocytosis associated with chronic lymphocytic ...

The study shows that machine learning and deep learning algorithms can accurately classify tree species using individual tree point clouds, and the operation process of PointMLP is more concise and ...

Early prediction of in-hospital pneumonia mortality can effectively be done using a machine learning (ML) model based on clinical data.

Five machine learning algorithms—logistic regression, decision tree, multilayer perceptron, random forest, and support vector machine—were trained and validated. Model performance was evaluated using ...