AIMC Topic: Humans

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Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis.

Scientific reports
Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Uti...

PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI.

Journal of neural engineering
. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD.This ...

Prediction of aptamer affinity using an artificial intelligence approach.

Journal of materials chemistry. B
Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesiz...

Virus-host interactions predictor (VHIP): Machine learning approach to resolve microbial virus-host interaction networks.

PLoS computational biology
Viruses of microbes are ubiquitous biological entities that reprogram their hosts' metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental ...

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data.

Environmental health perspectives
BACKGROUND: Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon...

Assessing the nonlinear impact of green space exposure on psychological stress perception using machine learning and street view images.

Frontiers in public health
INTRODUCTION: Urban green space (GS) exposure is recognized as a nature-based strategy for addressing urban challenges. However, the stress relieving effects and mechanisms of GS exposure are yet to be fully explored. The development of machine learn...

Advancements in automated classification of chronic obstructive pulmonary disease based on computed tomography imaging features through deep learning approaches.

Respiratory medicine
Chronic Obstructive Pulmonary Disease (COPD) represents a global public health issue that significantly impairs patients' quality of life and overall health. As one of the primary causes of chronic respiratory diseases and global mortality, effective...

Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning.

PloS one
Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. Antimicrobial Resistance occurs when bacteria, viruses, and parasites no longer respond to these medic...

Improving Human Activity Recognition With Wearable Sensors Through BEE: Leveraging Early Exit and Gradient Boosting.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Early-exiting has recently provided an ideal solution for accelerating activity inference by attaching internal classifiers to deep neural networks. It allows easy activity samples to be predicted at shallower layers, without executing deeper layers,...