Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Recent results revived the interest in the implementation of analog devices able to perform brainlike operations. Here we introduce a training algorithm for a memristor network which is inspired by previous work on biological learning. Robust results are obtained from computer simulations of a network of voltage-controlled memristive devices. Its implementation in hardware is straightforward, being scalable and requiring very little peripheral computation overhead.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevE.105.054306 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
discusses what nurse leaders can learn from examining the practices of other industries - those that aim to learn from mistakes rather than blame people for them - thus helping to improve patient safety.
View Article and Find Full Text PDFAutomatic trending and analysis of SPECT quality assurance with artificial intelligence optical character recognition.
Med Phys
August 2025
The University of Texas MD Anderson Cancer Houston, Houston, Texas, USA.
Background: To guarantee high-quality patient scans, thorough quality assurance (QA) of SPECT or gamma cameras, including performance, review, and documentation, is essential.
Purpose: We developed a novel Nuclear Medicine Quality Assurance server (NMQA) with an AI deep learning (AIDL) optical character recognition (OCR) system to automate QA data retrieval and review from SPECT and gamma cameras. The system extracts and compares daily and weekly QA data against specifications.
MRI detection and grading of knee osteoarthritis - a pilot study using an AI technique with a novel imaging-based scoring system.
Biomater Sci
September 2025
Regenerative Engineering Laboratory, Department of Textile and Fibre Engineering, Indian Institute of Technology Delhi, New Delhi-110016, India.
Precise and rapid identification of knee osteoarthritis (OA) is essential for efficient management and therapy planning. Conventional diagnostic techniques frequently depend on subjective interpretation, which have shortcomings, particularly during the first phases of the illness. In this study, magnetic resonance imaging (MRI) was used to create knee datasets as novel techniques for evaluating knee OA.
View Article and Find Full Text PDFPrecision Unveiled in Unborn: A Cutting-Edge Hybrid Machine Learning Approach for Fetal Health State Classification.
Cardiovasc Eng Technol
August 2025
Fortis Hospital, Noida, Uttar Pradesh, India.
Purpose: Understanding and categorizing fetal health is an influential field of research that profoundly impacts the well-being of both mother and child. The primary desire to precisely examine and cure fetal disorders during pregnancy to enhance fetal and maternal outcomes is the driving force behind the classification of fetal health. Fetal cardiac abnormalities (structural or functional) need immediate doctor attention, and their early identification and detection in all stages of pregnancy can help doctors with the timely treatment of the mother and the unborn child by enabling appropriate prenatal counseling and management.
View Article and Find Full Text PDFArtificial Intelligence and Extended Reality in the Training of Vascular Surgeons: A Narrative Review.
Med Sci (Basel)
August 2025
Department of Emergency Medicine, Medical University of Gdańsk, University Clinical Centre in Gdańsk, 80-952 Gdańsk, Poland.
Background: The rapid shift from open to endovascular techniques in vascular surgery has significantly decreased trainee exposure to high-stakes open procedures. Simulation-based training, especially that incorporating virtual reality (VR) and artificial intelligence (AI), provides a promising way to bridge this skill gap.
Objective: This narrative review aims to assess the current evidence on the integration of extended reality (XR) and AI into vascular surgeon training, focusing on technical skill development, performance evaluation, and educational results.