The emergence of Industry 5.0 marks a paradigm shift toward human-centric, sustainable, and resilient manufacturing. Unlike ...

Every year, thousands of new materials are created, yet many never reach their full potential because their applications aren't immediately obvious—a challenge University of Toronto researchers aim to ...

Choosing the right generative AI architecture is crucial for professional problem-solving applications. Generalist models ...

MIT researchers employed a novel application of tools and analysis to show that astrocytes ensure neural information ...

AI's role in predictive analytics for energy savings helps Smart Cities manage resources more efficiently, say experts ...

Techniques such as Raman spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), and near-infrared (NIR) spectroscopy provide detailed biochemical fingerprints ...

In Recentive, the Federal Circuit acknowledged the growing significance of AI and machine learning and emphasized that its holding is limited to generic machine learning applications.

As data volumes surge across every industry and machine learning tools become more accessible, predictive analytics is evolving from a niche discipline into a cornerstone of application innovation.

Mustafa Abdullah, D. and Mohsin Abdulazeez, A. (2021) Machine Learning Applications Based on SVM Classification a Review. Qubahan Academic Journal, 1, 81-90.

Key applications of machine learning in finance Machine learning is changing the face of finance by automating complex processes, increasing security and allowing better decision-making.

Welcome to the "Lung Cancer Prediction" repository, where we utilize machine learning models such as Random Forest, Logistic Regression, and SVM to predict lung cancer risks. This project focuses on ...