AIMC Topic: Machine Learning

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Machine learning-based prediction of unconfined compressive strength and contaminant leachability in dredged contaminated sediments for land reclamation projects.

Environmental science and pollution research international
This research investigates the application of machine learning techniques for predicting unconfined compressive strength (UCS) and contaminant leachability in dredged contaminated sediments (DCS) with implications for land reclamation projects. Tradi...

Machine learning-based activity prediction of phenoxy-imine catalysts and its structure-activity relationship study.

Molecular diversity
This study systematically investigates the structure-activity relationships of 30 Ti-phenoxy-imine (FI-Ti) catalysts using machine learning (ML) approaches. Among the tested algorithms, XGBoost demonstrated superior predictive performance, achieving ...

An ultra-sensitive, intelligent platform for food safety monitoring: Label-free detection of illegal additives using self-assembled SERS substrates and machine learning.

Food chemistry
To overcome the limitations of SERS in food safety monitoring, particularly significant interference from citrate ions, this study introduces an intelligent SERS-based platform for food safety monitoring. The platform utilizes sodium borohydride to a...

Terahertz molecular vibrational sensing using 3D printed anapole meta-biosensor.

Biosensors & bioelectronics
Terahertz (THz) fingerprint sensing utilizes the absorption of fingerprints generated by the unique vibrational characteristics of molecules to achieve substance-specific identification. By taking full advance of the anapole mode induced-biosensor co...

MCNEL: A multi-scale convolutional network and ensemble learning for Alzheimer's disease diagnosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) significantly threatens community well-being and healthcare resource allocation due to its high incidence and mortality. Therefore, early detection and intervention are crucial for reducing AD-relate...

FedBM: Stealing knowledge from pre-trained language models for heterogeneous federated learning.

Medical image analysis
Federated learning (FL) has shown great potential in medical image computing since it provides a decentralized learning paradigm that allows multiple clients to train a model collaboratively without privacy leakage. However, current studies have show...

Female autism categorization using CNN based NeuroNet57 and ant colony optimization.

Computers in biology and medicine
Autism identification and classification using biomedical medical image analysis has advanced recently. Research shows autistic females have different phenotypic and age-related brain variations than males. Gender-specific hormones and genes affect a...

Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major osteoporotic fractures in older men. The Super Learner (SL) model achieved superior performance (AU...

Towards automatic US-MR fetal brain image registration with learning-based methods.

NeuroImage
Fetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological...

Identification of Novel Fourth-Generation Allosteric Inhibitors Targeting Inactive State of EGFR T790M/L858R/C797S and T790M/L858R Mutations: A Combined Machine Learning and Molecular Dynamics Approach.

The journal of physical chemistry. B
Targeted therapy with an allosteric inhibitor (AIs) is an important area of research in patients with epidermal growth factor receptor (EGFR) mutations. Current treatment of nonsmall cell lung cancer patients with EGFR mutations using orthosteric inh...