AIMC Topic: Neural Networks, Computer

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Learning place cells and remapping by decoding the cognitive map.

eLife
Hippocampal place cells are known for their spatially selective firing and are believed to encode an animal's location while forming part of a cognitive map of space. These cells exhibit marked tuning curves and rate changes when an animal's environm...

Multi-modal classification of retinal disease based on convolutional neural network.

Biomedical physics & engineering express
Retinal diseases such as age-related macular degeneration and diabetic retinopathy will lead to irreversible blindness without timely diagnosis and treatment. Optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) imag...

Neural network-based identification for scallops (Pecten maximus) in natural marine habitats.

PloS one
The Great Atlantic scallop, or King scallop (Pecten maximus), ranks third in value after mackerel and Nephrops in UK fisheries. Its landings have surged over recent decades, making it the UK's fastest-growing fishery. Scallop stock assessments, cruci...

Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

PloS one
In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significant challenges due to the dynamic interaction between physiological and clinical markers. This study aims to uncover these subtle interconnections and i...

A novel contrastive Dual-Branch Network (CDB-Net) for robust EEG-Based Alzheimer's disease diagnosis.

Brain research
Alzheimer's Disease (AD) is neurodegenerative disorder that causes cognitive decline, memory loss, confusion, and changes in behavior. Early and accurate detection is important for timely intervention, current diagnostic methods can be slow, expensiv...

Machine learning for polycyclic aromatic hydrocarbons analysis in roasted lamb: new insights from spectral and chemical data.

Food chemistry
Polycyclic aromatic hydrocarbons (PAHs) generated during lamb roasting pose health risks but are difficult to predict due to their low concentrations and complex features. Existing models fail to address data scarcity and low-concentration prediction...

CLT-MambaSeg: An integrated model of Convolution, Linear Transformer and Multiscale Mamba for medical image segmentation.

Computers in biology and medicine
Recent advances in deep learning have significantly enhanced the performance of medical image segmentation. However, maintaining a balanced integration of feature localization, global context modeling, and computational efficiency remains a critical ...

Quantum-Embedded Graph Neural Network Architecture for Molecular Property Prediction.

Journal of chemical information and modeling
Accurate prediction of molecular properties is crucial for accelerating the development of new drugs, and quantum machine learning (QML) holds great promise in this domain. A typical QML pipeline comprises two core stages: encoding classical data int...

Tomato ripeness prediction using low resolution portable spectrometer and machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Tomato ripeness assessment is critical to ensure optimal product quality. This study proposes a novel approach to predict total soluble solids (TSS) and firmness, and classify tomato ripeness using a low-resolution AS7265x portable spectrometer combi...

Development and validation of deep learning for predicting the growth of ovarian cancer organoids.

Chinese medical journal
BACKGROUND: Organoids have attracted enormous interest in disease modeling, drug screening, and precision medicine. However, developing robust patient-derived organoids (PDOs) was time-consuming, costly, and had low success rates for certain cancer t...