AIMC Topic: Machine Learning

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Expansion quantization network: A micro-emotion detection and annotation framework.

PloS one
Textemotion detection constitutes a crucial foundation for advancing artificial intelligence from basic comprehension to the exploration of emotional reasoning. Most existing emotion detection datasets rely on manual annotations, which are associated...

Early adherence to biofeedback training predicts long-term improvement in stroke patients: A machine learning approach.

PloS one
Biofeedback-based treadmill training generally involves 10 or more sessions to assess its effectiveness during stroke rehabilitation. Improvements are seen in some patients during the assessment, while others do not progress. Our aim in this study is...

CattleNet-XAI: An explainable CNN framework for efficient cattle weight estimation.

PloS one
Accurate estimation of cattle weight is essential for effective farm management, health assessment, and productivity optimization. Traditional manual methods for weight estimation, however, are labor-intensive, time-consuming, and prone to inaccuraci...

Automated framework for multi-domain social media text analysis for business strategy employing multilayer perceptron with Word2Vec features and LIME XAI.

PloS one
Sentiment analysis is a pivotal domain in Natural Language Processing (NLP), particularly for understanding opinions expressed in sequential and textual data with the usage of machine learning. It involves identifying and categorizing emotions expres...

Development and validation of a machine learning model for on-site prediction of coronary heart disease in high-risk adults using clinical data.

PloS one
BACKGROUND: Risk of coronary heart disease (CHD) in a specific period of years can be assessed using scores calculated by models, such as pooled cohort equations (PCEs) and Framingham Risk Score. However, there are few studies on on-site estimation o...

QDs fluorescent immunosensor based on magnetic separation coupled with machine learning for aflatoxin B1 detection in vegetable oils.

Food chemistry
Aflatoxin B1 (AFB1) is a common mycotoxin frequently found in vegetable oils. It poses a severe threat to public health, therefore there is a need for rapid and sensitive detection methods. In this study, a novel competitive immunofluorescent biosens...

Charting the virosphere: computational synergies of AI and bioinformatics in viral discovery and evolution.

Journal of virology
The advancement of metagenomic sequencing has revealed a vast viral diversity while simultaneously exposing limitations of homology-based tools such as BLAST and HMMER, which often fail to detect highly divergent viral genomes. The integration of art...

Recent Advances in Integrating Machine Learning with Omics Approaches in Food Science and Nutrition Research.

Journal of agricultural and food chemistry
Omics technologies are revolutionizing food and nutrition research by enabling high-throughput analysis of food components and microorganisms and revealing the intricate relationships between food and human health. Machine learning (ML) methods are p...

How Feasible Is Docking of PROTACs to POI-E3L Complexes? Testing Physics-Based and ML-Based Docking Tools.

Journal of chemical information and modeling
Targeted protein degradation (TPD) is an innovative drug discovery approach that leverages small molecules to induce proximity between a protein of interest (POI) and an E3 ubiquitin ligase (E3L) for selective degradation. Among TPD modalities, prote...

Accurate Simulations of Water and Aqueous Solutions through Fine-Tuned Dispersion-Corrected Density Functional Theory and Machine-Learning Interatomic Potentials.

Journal of chemical information and modeling
Dispersion-corrected density functional theory (DFT-D) is widely employed to model large molecular systems at an affordable computational cost and to develop machine-learning interatomic potentials (MLIPs), enabling reliable molecular dynamics (MD) s...