AIMC Topic: Machine Learning

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Using tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and computing costs limit the field's adoption. Generalizable pretrained networks can overcome these costs, but...

[Acoustic technology empowers the diagnosis and treatment of respiratory diseases: challenges, and prospects].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases
Respiratory diseases is a major challenge to global public health. In recent years, acoustic technology has shown great potential as a non-invasive and convenient diagnostic method for detecting and monitoring respiratory diseases. With the developme...

Interpretable Multimodal Deep Ensemble Framework Dissecting Bloodbrain Barrier Permeability with Molecular Features.

The journal of physical chemistry letters
Blood-brain barrier permeability (BBBP) prediction plays a critical role in the drug discovery process, particularly for compounds targeting the central nervous system. While machine learning (ML) has significantly advanced the prediction of BBBP, th...

Machine Learning Accelerated Discovery of Antimicrobial Inorganic Nanomaterials.

The journal of physical chemistry letters
The growing prevalence of infectious diseases and the increasing threat of bacterial resistance have drawn widespread attention to antimicrobial inorganic nanomaterials. However, the diversity, abundance, and complex mechanisms of these materials pre...

Artificial neural networks for magnetoencephalography: a review of an emerging field.

Journal of neural engineering
. Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. While MEG data analytics have t...

The fluorescence spectrum combined with a broad learning system to characterize the content of difenoconazole in cabbage.

Analytical methods : advancing methods and applications
Pesticide residue detection plays an important role in vegetable quality and food safety. In this work, we propose a method for detecting difenoconazole pesticide residues based on fluorescence spectroscopy technology and machine learning algorithms....

Machine learning-guided fabrication of carbon dot-pepsin nano-conjugates for enhanced bioimaging, synergistic drug delivery, and visible light-induced photosensitization.

Nanoscale
Carbon dots (CDs) are emerging as next-generation bioimaging agents due to their strong fluorescence, photobleaching resistance, and biocompatibility. However, their small size often limits efficient cell internalization, leading to unspecified cellu...

Evolutionary accumulation modeling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance.

mBio
Can we understand and predict the evolutionary pathways by which bacteria acquire multi-drug resistance (MDR)? These questions have substantial potential impact in basic biology and in applied approaches to address the global health challenge of anti...

[Advances in the application of machine learning-related combined models in infectious disease prediction].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to ...

Assessing training needs and influencing factors among personnel at centers for disease control and prevention in northeast China: a cross-sectional study framed by SDT and TPB using machine learning techniques.

BMC public health
OBJECTIVES: Training public health personnel is crucial for enhancing the capacity of public health systems. However, existing research often falls short in providing a comprehensive theoretical framework and fails to account for the intricate interp...