AIMC Topic: Fungi
Clear Filters Showing 11 to 20 of 73 articles
Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.
PloS one
Jan 24, 2025
Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art painting...
Machine learning models for predicting indoor airborne fungal concentrations in public facilities utilizing environmental variables.
Environmental pollution (Barking, Essex : 1987)
Jan 16, 2025
Airborne fungi are major contributors to substandard indoor air quality, with potential implications for public health, especially in public facilities. The risk of chronic exposure can be significantly reduced by accurately predicting airborne funga...
Targeted isolation and AI-based analysis of edible fungal polysaccharides: Emphasizing tumor immunological mechanisms and future prospects as mycomedicines.
International journal of biological macromolecules
Nov 26, 2024
Edible fungal polysaccharides have emerged as significant bioactive compounds with diverse therapeutic potentials, including notable anti-tumor effects. Derived from various fungal sources, these polysaccharides exhibit complex biological activities ...
Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.
Sensors (Basel, Switzerland)
Nov 9, 2024
This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as , , , , and were chosen based on their ecological importance and distinct morphological characteristics. Th...
Effective detection of indoor fungal contamination through the identification of volatile organic compounds using mass spectrometry and machine learning.
Environmental pollution (Barking, Essex : 1987)
Oct 28, 2024
Indoor fungal contamination poses significant challenges to human health and indoor air quality. This study addresses an effective approach using mass spectrometry and machine learning to identify microbial volatile organic compounds (MVOCs) originat...
Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms.
Biosensors & bioelectronics
Oct 21, 2024
The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition parti...
Optimization of fungal secondary metabolites production via response surface methodology coupled with multi-parameter optimized artificial neural network model.
Bioresource technology
Sep 20, 2024
Filamentous fungi's secondary metabolites (SMs) possess significant application owing to their distinct structure and diverse bioactivities, yet their restricted yield levels often hinder further research and application. The study developed a respon...
Investigating PCB degradation by indigenous fungal strains isolated from the transformer oil-contaminated site: degradation kinetics, Bayesian network, artificial neural networks, QSAR with DFT, molecular docking, and molecular dynamics simulation.
Environmental science and pollution research international
Sep 6, 2024
The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian network...
Innovative infrastructure to access Brazilian fungal diversity using deep learning.
PeerJ
Jul 9, 2024
In the present investigation, we employ a novel and meticulously structured database assembled by experts, encompassing macrofungi field-collected in Brazil, featuring upwards of 13,894 photographs representing 505 distinct species. The purpose of ut...
Ensemble learning algorithms to elucidate the core microbiome's impact on carbon content and degradation properties at the soil aggregate level.
The Science of the total environment
Jul 4, 2024
Soil aggregates are crucial for soil organic carbon (OC) accumulation. This study, utilizing a 32-year fertilization experiment, investigates whether the core microbiome can elucidate variations in carbon content and decomposition across different ag...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823