Laser‐induced breakdown spectroscopy (LIBS), an atomic emission technique, is widely applied in fields like geology and biology. This rapid elemental analysis method leverages computational tools to boost precision and speed up data processing. This review explores machine learning and deep learning methods for analyzing LIBS spectral data, tackling ...
Pegah Dehbozorgi +3 more
wiley +1 more source
Forecasting the diabetic retinopathy progression using generative adversarial networks. [PDF]
Qiao H +14 more
europepmc +1 more source
Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation [PDF]
Liangjian Chen +7 more
openalex +1 more source
Latent Diffusion Models for Virtual Battery Material Screening and Characterization
A newly developed virtual tool is designed to enhance the extraction of meaningful information from characterization technique data and effectively guides the screening of target battery materials based on functional requirements. Efficient characterization of battery materials is fundamental to understanding the underlying electrochemical mechanisms ...
Deepalaxmi Rajagopal +3 more
wiley +1 more source
Detection of unseen malware threats using generative adversarial networks and deep learning models. [PDF]
Joshi C, Kumar J, Kumawat G.
europepmc +1 more source
Mining Chemical Space with Generative Models for Battery Materials
Revolutionizing Li‐ion battery material discovery with MatterGen, a foundational generative AI model for crystal structure inverse design. Explored stable, unique, and novel compositions and their analysis with respect to the state‐of‐the‐art databases, followed by DFT validation, provides a new direction for accelerating materials discovery ...
Chiku Parida +3 more
wiley +1 more source
Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives. [PDF]
Izu-Belloso RM +2 more
europepmc +1 more source
Generative Deep Learning for Advanced Battery Materials
This review explores the role of generative deep learning (DL) in battery materials analysis and highlights the fundamental principles of generative DL and its applications in designing battery materials. The importance of using multimodal data is underscored to effectively address the challenges faced during the development of battery materials across
Deepalaxmi Rajagopal +3 more
wiley +1 more source
Auxiliary Discrminator Sequence Generative Adversarial Networks for Few Sample Molecule Generation. [PDF]
Tang H, Long J, Ji B, Wang J.
europepmc +1 more source
ABSTRACT In‐line Raman spectroscopy combined with accurate quantification models can offer detailed real‐time insights into a bioprocess by monitoring key process parameters. However, traditional approaches for model calibration require extensive data collection from multiple bioreactor runs, resulting in process‐specific models that are sensitive to ...
Maarten Klaverdijk +3 more
wiley +1 more source

