AIMC Topic: Neural Networks, Computer

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MIMI-ONET: Multi-Modal image augmentation via Butterfly Optimized neural network for Huntington DiseaseDetection.

Brain research
Huntington's disease (HD) is a chronic neurodegenerative ailment that affects cognitive decline, motor impairment, and psychiatric symptoms. However, the existing HD detection methods are struggle with limited annotated datasets that restricts their ...

Deep learning enabled open-set bacteria recognition using surface-enhanced Raman spectroscopy.

Biosensors & bioelectronics
Accurate bacterial identification is vital in medical and healthcare settings. Traditional methods, though reliable, are often time-consuming, underscoring the need for faster, more efficient alternatives. Deep learning-assisted Surface-enhanced Rama...

SERS based determination of ceftriaxone, ampicillin, and vancomycin in serum using WS/Au@Ag nanocomposites and a 2D-CNN regression model.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Accurate therapeutic drug monitoring (TDM) of antibiotics including ceftriaxone, ampicillin, and vancomycin plays an important role in the treatment of neonatal sepsis, a common and life-threatening disease in neonates. A highly sensitive surface-enh...

Quality prediction of seabream Sparus aurata by deep learning algorithms and explainable artificial intelligence.

Food chemistry
In this study, Convolutional Neural Network (CNN), DenseNet121, Inception V3 and ResNet50 machine learning algorithms were used to determine the quality changes in sea bream stored in refrigerator conditions using eye and gill images. The sea bream w...

Quantitative determination of acid value in palm oil during thermal oxidation using Raman spectroscopy combined with deep learning models.

Food chemistry
Accurate monitoring of acid value (AV) is critical for edible oil quality control, yet traditional chemometric methods often face limitations in handling complex spectral data. This study combines Raman spectroscopy with deep learning, including Conv...

An AI-driven strategy for active compounds discovery and non-destructive quality control in traditional Chinese medicine: A case of Xuefu Zhuyu Oral Liquid.

Talanta
The modernization and globalization of traditional Chinese medicine (TCM) face challenges such as unclear active compounds and inadequate quality control. Taking Xuefu Zhuyu Oral Liquid (XZOL) as an example, this study proposed an artificial intellig...

A machine-learning-integrated portable electrochemiluminescence sensing platform for the visualization and high-throughput immunoassays.

Talanta
Electrochemiluminescence (ECL)-based point-of-care testing (POCT) has the potential to facilitate the rapid identification of diseases, offering advantages such as high sensitivity, strong selectivity, and minimal background interference. However, as...

Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction.

BMC biology
BACKGROUND: Different expression levels of circular RNAs (circRNAs) affect the sensitivity of human cells to drugs, thus producing different responses to the therapeutic effects of drugs. Using traditional biomedical experiments to discover and confi...

Classification of lung cancer severity using gene expression data based on deep learning.

BMC medical informatics and decision making
Lung cancer is one of the most prevalent diseases affecting people and is a main factor in the rising death rate. Recently, Machine Learning (ML) and Deep Learning (DL) techniques have been utilized to detect and classify various types of cancer, inc...

A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis.

Scientific reports
Knee osteoarthritis (KOA) is a severe arthrodial joint condition with significant global socioeconomic consequences. Early recognition and treatment of KOA is critical for avoiding disease progression and developing effective treatment programs. The ...