AI-driven system blends literature, experiments and robotics to discover new materials

Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables.
NatureOpinion

Machine learning reveals potential consequences of cuts to US research

Governments have the right to decide how to spend money on science. Modelling shows that making instant cuts to grants ...
IEEE

Hybrid Feature Extraction and Deep Learning Framework for Power Transformer Fault Classification: A Real-World Case Study

Abstract: Reliable fault diagnosis in power transformers is paramount for ensuring grid stability and safeguarding critical assets. This paper proposes a novel deep learning-based diagnostic framework ...
FMIBlog

Image Recognition in Retail Market to Hit USD 28.0 billion by 2035

By 2035, the image recognition in retail market will be worth USD 28.0 billion, underscoring the transformative power of AI and visual technologies in the retail industry. Annual increments will grow ...
IEEE

A Hybrid Machine Learning Approach Utilizing CNN Feature Extraction with Traditional Classifier to Identify Strawberry Leaf Diseases

Abstract: Strawberry production is globally significant because it contains high nutrients. Strawberry leaf disease shapes a significant barrier to strawberry cultivation worldwide. Numerous ...
Gut

AI-empowered human microbiome research

Recent advances in high-throughput microbiome profiling have generated expansive data sets that offer unprecedented ...