Object detection and classification using few-shot learning in smart agriculture: A scoping mini review

N Ragu, J Teo - Frontiers in Sustainable Food Systems, 2023 - frontiersin.org
Smart agriculture is the application of modern information and communication technologies
(ICT) to agriculture, leading to what we might call a third green revolution. These include …

FragNet, a contrastive learning-based transformer model for clustering, interpreting, visualizing, and navigating chemical space

AD Shrivastava, DB Kell - Molecules, 2021 - mdpi.com
The question of molecular similarity is core in cheminformatics and is usually assessed via a
pairwise comparison based on vectors of properties or molecular fingerprints. We recently …

Contrastive self-supervised learning for stress detection from ecg data

S Rabbani, N Khan - Bioengineering, 2022 - mdpi.com
In recent literature, ECG-based stress assessment has become popular due to its proven
correlation to stress and increased accessibility of ECG data through commodity hardware …

Caveat emptor: On the Need for Baseline Quality Standards in Computer Vision Wood Identification

P Ravindran, AC Wiedenhoeft - Forests, 2022 - mdpi.com
Computer vision wood identification (CVWID) has focused on laboratory studies reporting
consistently high model accuracies with greatly varying input data quality, data hygiene, and …

PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs

Y You, S Chang, Z Yang, Q Sun - Frontiers in Neuroscience, 2023 - frontiersin.org
Traditional supervised learning methods require large quantities of labeled data. However,
labeling sleep data according to polysomnography by well-trained sleep experts is a very …

Asymmetric Graph Contrastive Learning

X Chang, J Wang, R Guo, Y Wang, W Li - Mathematics, 2023 - mdpi.com
Learning effective graph representations in an unsupervised manner is a popular research
topic in graph data analysis. Recently, contrastive learning has shown its success in …

Enhancing Self-Supervised Learning through Explainable Artificial Intelligence Mechanisms: A Computational Analysis

E Neghawi, Y Liu - Big Data and Cognitive Computing, 2024 - mdpi.com
Self-supervised learning continues to drive advancements in machine learning. However,
the absence of unified computational processes for benchmarking and evaluation remains a …

Two-View Mammogram Synthesis from Single-View Data Using Generative Adversarial Networks

A Yamazaki, T Ishida - Applied Sciences, 2022 - mdpi.com
While two-view mammography taking both mediolateral-oblique (MLO) and cranio-caudual
(CC) views is the current standard method of examination in breast cancer screening, single …

OPEN ACCESS EDITED BY

MF Manzoor, T Tufail, S Yıkmış, S Morya… - … for Processing and …, 2024 - books.google.com
Herbal medicine gained popularization over the years, and it was the base of the modern
medicine's development. According to a survey, 70–80% of the world population directly or …

Feature Contrastive Learning for No-Reference Segmentation Quality Evaluation

X Li, B Peng, Z Xie - Electronics, 2023 - mdpi.com
No-reference segmentation quality evaluation aims to evaluate the quality of image
segmentation without any reference image during the application process. It usually …