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Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition.
Interdiscip Sci
January 2025
Institute for Complexity Science, Henan University of Technology, Zhengzhou, 450001, China.
Artificial intelligence technology has demonstrated remarkable diagnostic efficacy in modern biomedical image analysis. However, the practical application of artificial intelligence is significantly limited by the presence of similar pathologies among different diseases and the diversity of pathologies within the same disease. To address this issue, this paper proposes a reinforced collaborative-competitive representation classification (RCCRC) method.
View Article and Find Full Text PDFAdvanced LC-IMS-MS Protocol for Holistic Metabolite Analysis in Wine and Grape Samples.
Methods Mol Biol
January 2025
Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach, San Michele all'Adige, Italy.
The final aim of metabolomics is the comprehensive and holistic study of the metabolome in biological samples. Therefore, the use of instruments that enable the analysis of metabolites belonging to various chemical classes in a wide range of concentrations is essential, without compromising on robustness, resolution, sensitivity, specificity, and metabolite annotation. These characteristics are crucial for the analysis of very complex samples, such as wine, whose metabolome is the result of the sum of metabolites derived from grapes, yeast(s), bacteria(s), and chemical or physical modification during winemaking.
View Article and Find Full Text PDFAttention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification.
Sensors (Basel)
January 2025
Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns.
View Article and Find Full Text PDFAssociations Between Vocational Interests and Personality "Beyond" the Big-Five.
Psychol Rep
January 2025
Departments of Psychology and Management and Organizational Studies, Faculty of Social Science, The University of Western Ontario, London, ON, Canada.
This investigation explores the relationships between vocational interests and personality dimensions suggested to be "beyond" the Big Five or Five Factor Model. Participants (653 adults; 125 men and 528 women, with a mean age of 40.57 years, = 16.
View Article and Find Full Text PDFMFC-ACL: Multi-view fusion clustering with attentive contrastive learning.
Neural Netw
December 2024
College of Automation, Chongqing University of Posts and Telecommunications, Nan'an District, 400065, Chongqing, China. Electronic address:
Multi-view clustering can better handle high-dimensional data by combining information from multiple views, which is important in big data mining. However, the existing models which simply perform feature fusion after feature extraction for individual views, mostly fails to capture the holistic attribute information of multi-view data due to ignoring the significant disparities among views, which seriously affects the performance of multi-view clustering. In this paper, inspired by the attention mechanism, an approach called Multi-View Fusion Clustering with Attentive Contrastive Learning (MFC-ACL) is proposed to tackle these issues.
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