Results 111 to 120 of about 9,883,756 (332)
Machine Learning and Knowledge ExtractionBackground: Customer churn significantly impacts business revenues. Machine Learning (ML) and Deep Learning (DL) methods are increasingly adopted to predict churn, yet a systematic synthesis of recent advancements is lacking.
Mehdi Imani +3 more
semanticscholar +1 more source
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey [PDF]
Information FusionRecent advancements in deep learning (DL) have posed a significant challenge for automatic speech recognition (ASR). ASR relies on extensive training datasets, including confidential ones, and demands substantial computational and storage resources ...
H. Kheddar +2 more
semanticscholar +1 more source
Intelligent Micro/Nanorobots for Targeted Interventional Therapy: From Bench to Clinic
Advanced Robotics Research, EarlyView.Zirui Zhang et al. reviewed the application and challenges of mobile nanomachines in interventional therapy. By converting exogenous energy, including chemical, magnetic, optical, and ultrasonic sources, into mechanical forces, micro/nanorobots (MNRs) enable precise actuation at unprecedented scales. Evolving far beyond traditional drug delivery, these
Zirui Zhang +5 more
wiley +1 more source
De ComputisMachine learning (ML) and deep learning (DL), subsets of artificial intelligence (AI), are the core technologies that lead significant transformation and innovation in various industries by integrating AI-driven solutions.
Kamran Razzaq, M. Shah
semanticscholar +1 more source
Advanced Science, EarlyView.This review identifies current and future directions in abiotic nanostructured catalysts to develop reliable and sustainable glucose fuel cells to power the next generation of bioelectronic devices. ABSTRACT The global rise in incidence of chronic diseases has led to the demand for innovative solutions that help patients manage their conditions with ...
Asghar Niyazi +3 more
wiley +1 more source
Advanced Science, EarlyView.This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
Advanced Science, EarlyView.A pneumatically actuated multi‐tissue microphysiological system is integrated with AI‐based machine vision and automatic sampling and replenishment systems. The platform allows for the emulation of translationally relevant long‐term pharmacokinetic exposure scenarios for multiple weeks while enabling longitudinal monitoring of response biomarkers ...
Jibbe Keulen +15 more
wiley +1 more source
Deep Learning: A Review for the Radiation Oncologist
Frontiers in Oncology, 2019 Introduction: Deep Learning (DL) is a machine learning technique that uses deep neural networks to create a model. The application areas of deep learning in radiation oncology include image segmentation and detection, image phenotyping, and radiomic ...
Luca Boldrini +4 more
doaj +1 more source
A review of machine learning and deep learning for Parkinson’s disease detection
Discover Artificial IntelligenceMillions of people worldwide suffer from Parkinson’s disease (PD), a neurodegenerative disorder marked by motor symptoms such as tremors, bradykinesia, and stiffness. Accurate early diagnosis is crucial for effective management and treatment.
Hajar Rabie, M. Akhloufi
semanticscholar +1 more source
A Generative Neuro‐Symbolic AI for Protein Sequence Design
Advanced Science, EarlyView.We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne +12 more
wiley +1 more source