Overview Understanding key machine learning algorithms is crucial for solving real-world data problems effectively.Data scientists should master both supervised ...

To evaluate the efficacy of federated learning for treatment response prediction in bipolar disorder, we developed a robust simulation pipeline encompassing synthetic data generation, neural network ...

The prototype, called Project Ire, reverse engineers software "without any clues about its origin or purpose," and then determines if the code is malicious or benign, using large language models (LLM) ...

Haghish, E.F. and Czajkowski, N. (2023) Reconsidering False Positives in Machine Learning Binary Classification Models of Suicidal Behavior. Current Psychology, 43, 10117-10121.

Tree species classification using machine learning and deep learning models of single tree point cloud data. The final step involves analyzing and evaluating the classification results.

In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify ...

Class imbalance poses a critical challenge in binary classification problems, particularly when rare but significant events are underrepresented in the training set. While traditional machine learning ...

Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR ...

The patent, issued on February 25, 2025, covers a novel method for analyzing binary software efficiently by leveraging machine learning to predict peak memory usage and dynamically allocate ...