[HTML][HTML] Artificial intelligence in perinatal mental health research: A scoping review

WH Kwok, Y Zhang, G Wang - Computers in Biology and Medicine, 2024 - Elsevier
ABSTRACT The intersection of Artificial Intelligence (AI) and perinatal mental health
research presents promising avenues, yet uncovers significant challenges for innovation …

[HTML][HTML] Conditional generative positive and unlabeled learning

A Papič, I Kononenko, Z Bosnić - Expert Systems with Applications, 2023 - Elsevier
The quantity of data generated increases daily, which makes it difficult to process. In the
case of supervised learning, labeling training examples may represent an especially tedious …

Utilizing Semi-supervised Method in Predicting BRCA1 Pathogenicity Variants

AA Hidayat, JP Trinugroho, R Nirwantono… - Procedia Computer …, 2023 - Elsevier
Quantifying the effect of mutations in the BRCA1 gene is useful for understanding their
clinical consequences on breast cancer. Machine learning models can be applied to predict …

Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology

C Liang - IEEE Access, 2024 - ieeexplore.ieee.org
With the continuous improvement of digitization, the processing and analysis of massive
data has become one of the hot issues. Soft computing technology, as an emerging machine …

Fingerprint image denoising and inpainting using generative adversarial networks

W Zhong, L Mao, Y Ning - Evolutionary Intelligence, 2024 - Springer
Fingerprint is a suitable biometric for identity verification. However, the fingerprint details are
significantly affected by the impression conditions and the non-uniform contact with …

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion

S Paul, S Jain - Recent Advances in Inflammation & Allergy Drug …, 2024 - benthamdirect.com
Background Diseases are medical situations that are allied with specific signs and
symptoms. A disease may be instigated by internal dysfunction or external factors like …

Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data

Z Ren, Q Li, K Cao, MM Li, Y Zhou… - BMC …, 2023 - pmc.ncbi.nlm.nih.gov
Following publication of the original article [1], it was reported that the article entitled “Model
performance and interpretability of semi-supervised generative adversarial networks to …

Examining the Use of Generative Adversarial Network for Predicting Tumor Malignancy

J Bhuvana, M Pandeya, D Kumar - … International Conference on …, 2024 - ieeexplore.ieee.org
This study's paper examines using a generative opposed network as an excellent way to
predict the malignancy of tumors in a clinically applicable manner. The examination …