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AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden.

Publication Type: Academic Journal

Source(s): Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Oct 24; Vol. 23 (21). Date of Electronic Publication: 2023 Oct 24.

Abstract: The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, the...

Paired-associate versus cross-situational: How do verbal working memory and word familiarity affect word learning?

Publication Type: Academic Journal

Source(s): Memory & cognition [Mem Cognit] 2023 Oct; Vol. 51 (7), pp. 1670-1682. Date of Electronic Publication: 2023 Apr 03.

Abstract: Word learning is one of the first steps into language, and vocabulary knowledge predicts reading, speaking, and writing ability. There are several pathways to word learning and little is known about how they differ. Previous research has investigated p...

Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.

Publication Type: Academic Journal

Source(s): Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2023 Nov 17; Vol. 30 (12), pp. 2004-2011.

Abstract: Objective: Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatric ...

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition.

Publication Type: Academic Journal

Source(s): Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Nov 09; Vol. 23 (22). Date of Electronic Publication: 2023 Nov 09.

Abstract: Sign language recognition, an essential interface between the hearing and deaf-mute communities, faces challenges with high false positive rates and computational costs, even with the use of advanced deep learning techniques. Our proposed solution is a...

Hybrid deep learning approach to improve classification of low-volume high-dimensional data.

Publication Type: Academic Journal

Source(s): BMC bioinformatics [BMC Bioinformatics] 2023 Nov 07; Vol. 24 (1), pp. 419. Date of Electronic Publication: 2023 Nov 07.

Abstract: Background: The performance of machine learning classification methods relies heavily on the choice of features. In many domains, feature generation can be labor-intensive and require domain knowledge, and feature selection methods do not scale well in...

Deep Learning for Epidemiologists: An Introduction to Neural Networks.

Publication Type: Academic Journal

Source(s): American journal of epidemiology [Am J Epidemiol] 2023 Nov 03; Vol. 192 (11), pp. 1904-1916.

Authors:

Abstract: Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning fro...

Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects.

Publication Type: Academic Journal

Source(s): Pharmacological research [Pharmacol Res] 2023 Nov; Vol. 197, pp. 106984. Date of Electronic Publication: 2023 Nov 07.

Abstract: The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a promising approach. This integration enhanc...

How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning.

Publication Type: Academic Journal

Source(s): Cognitive psychology [Cogn Psychol] 2023 Nov; Vol. 146, pp. 101598. Date of Electronic Publication: 2023 Sep 14.

Abstract: Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsole...

Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.

Publication Type: Academic Journal

Source(s): Magnetic resonance in medicine [Magn Reson Med] 2023 Nov; Vol. 90 (5), pp. 2052-2070. Date of Electronic Publication: 2023 Jul 10.

Abstract: Purpose: To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans.Methods: We propose Noise2Recon, a consistency training method for ...

On-Device Execution of Deep Learning Models on HoloLens2 for Real-Time Augmented Reality Medical Applications.

Publication Type: Academic Journal

Source(s): Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Oct 25; Vol. 23 (21). Date of Electronic Publication: 2023 Oct 25.

Abstract: The integration of Deep Learning (DL) models with the HoloLens2 Augmented Reality (AR) headset has enormous potential for real-time AR medical applications. Currently, most applications execute the models on an external server that communicates with th...

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