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Innovative strategies for enhancing AuNP-based point-of-care diagnostics: Focus on coronavirus detection.
Talanta
December 2024
College of Materials Science & Engineering, HuaQiao University, Amoy, Fujian, 361021, China; School of Medicine, Huaqiao University, Quanzhou, Fujian, 362021, China. Electronic address:
Highly pathogenic coronaviruses have consistently threatened humanity, encompassing SARS-CoV, MERS-CoV, SARS-CoV-2 and others. Swift detection and accurate diagnosis play a crucial role in promptly identifying high-risk populations, enabling timely intervention, and effectively breaking the transmission chain to reduce casualties. However, the diagnostic "gold standard" reverse transcription-polymerase chain reaction (RT-PCR) failed to meet the overwhelming demand during the pandemic due to insufficient equipment and trained personnel, impeding the effective control of viral spread.
View Article and Find Full Text PDFNormalized group activations based feature extraction technique using heterogeneous data for Alzheimer's disease classification.
PeerJ Comput Sci
November 2024
Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
Several deep learning networks are developed to identify the complex atrophic patterns of Alzheimer's disease (AD). Among various activation functions used in deep neural networks, the rectifier linear unit is the most used one. Even though these functions are analyzed individually, group activations and their interpretations are still not explored for neuroimaging analysis.
View Article and Find Full Text PDFUnsupervised learning for lake underwater vegetation classification: Constructing high-precision, large-scale aquatic ecological datasets.
Sci Total Environ
December 2024
School of Engineering, Dali University, Yunnan 671003, China; National Observation and Research Station of Erhai Lake Ecosystem in Yunnan, Dali 671006, China.; Air-Space-Ground Integrated Intelligence and Big Data Application Engineering Research Center of Yunnan Provincial Department of Education, Yunnan 671003, China. Electronic address:
Article Synopsis
- * Supervised AI techniques can identify vegetation but rely on labeled datasets that are costly and time-consuming to create, leading to issues with accuracy when applied to new environments.
- * This study introduces an unsupervised classification method that significantly reduces the need for manual annotation, achieving high accuracy in identifying underwater vegetation while utilizing innovative techniques like dimensionality reduction and a voting mechanism, making it more efficient and scalable.
Equipping AI for Unbiased and Inclusive Neurology.
JAMA Neurol
November 2024
Office of Intramural Research, US National Institutes of Health, Bethesda, Maryland.
Binary classification from N-Tuple Comparisons data.
Neural Netw
February 2025
Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China. Electronic address:
Pairwise comparison classification (Pcomp) is a recently thriving weakly-supervised method that generates a binary classifier based on feedback information from comparisons between unlabeled data pairs (one is more likely to be positive than the other). However, this approach turns out challenging in more complex scenarios involving comparisons among more than two instances. To overcome this problem, this paper starts with a comprehensive exploration of the triplet comparisons data (the first instance is more likely to be positive than the second instance, and the second instance is more likely to be positive than the third instance).
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