Results 81 to 90 of about 18,749 (290)

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Support Spinor Machine

, 2017
We generalize a support vector machine to a support spinor machine by using the mathematical structure of wedge product over vector machine in order to extend field from vector field to spinor field.
Bartoš, Erik, Chunithpaisan, Sanphet, Kanjamapornkul, Kabin, Pinčák, Richard   +3 more
core   +1 more source

Smart Flexible Tactile Sensors: Recent Progress in Device Designs, Intelligent Algorithms, and Multidisciplinary Applications

Advanced Intelligent Discovery, EarlyView.
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang, Ming Dong, Rongrong Bao, Caofeng Pan   +3 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Modulation of Current Source Inverter [PDF]

Journal of Intelligent Procedures in Electrical Technology, 2011
Direct torque control with Current Source Inverter (CSI) instead of voltage source inverter is so appropriate because of determining the torque of induction motor with machine current and air gap flux. In addition, Space-Vector Modulation (SVM) is a more
Golam Reza Arab Markadeh, Forugh Taki, Saeed Abazari   +2 more
doaj  

Space Vector Modulation for an Indirect Matrix Converter with Improved Input Power Factor

Energies, 2017
Pulse width modulation strategies have been developed for indirect matrix converters (IMCs) in order to improve their performance. In indirect matrix converters, the LC input filter is used to remove input current harmonics and electromagnetic ...
Nguyen Dinh Tuyen, Phan Quoc Dzung
doaj   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings

, 2017
Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks. Traditionally, the mean or median vector is selected.
Ayesha, Buddhi, de Silva, Nisansa, Jayawardana, Vindula, Lakmal, Dimuthu, Perera, Amal Shehan, Sugathadasa, Keet   +5 more
core   +1 more source

Revealing Protein–Protein Interactions Using a Graph Theory‐Augmented Deep Learning Approach

Advanced Intelligent Discovery, EarlyView.
This study presents a fast, cost‐efficient approach for classifying protein–protein interactions by integrating graph‐theory parametrization with deep learning (DL). Multiscale features extracted from graph‐encoded polarized‐light microscopy (PLM) images enable accurate prediction of binding strengths.
Bahar Dadfar, Gözde Kabay, Matthias Franzreb, Kody Whisnant, Nicholas A. Kotov, Joerg Lahann   +5 more
wiley   +1 more source