AIMC Topic: Machine Learning

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Adaptive lift chiller units fault diagnosis model based on machine learning.

PloS one
The early minor faults generated by the chiller in operation are not easy to perceive, and the severity will gradually increase with time. The traditional fault diagnosis method has low accuracy and poor stability for early fault diagnosis. In this p...

Integrating machine learning and neural networks for new diagnostic approaches to idiopathic pulmonary fibrosis and immune infiltration research.

PloS one
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. T...

Microfinance institutions failure prediction in emerging countries, a machine learning approach.

PloS one
This study is about what matters: predicting when microfinance institutions might fail, especially in places where financial stability is closely linked to economic inclusion. The challenge? Creating something practical and usable. The Adjusted Gross...

Patent value prediction in biomedical textiles: A method based on a fusion of machine learning models.

PloS one
Patent value prediction is essential for technology innovation management. This study aims to enhance technology innovation management in the field of biomedical textiles by processing complex biomedical patent information to improve the accuracy of ...

New perspective: An in vitro study on inferring the post-mortem interval (PMI) of human skeletal muscle based on ATR-FTIR spectroscopy combined with machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The determination of the PMI remains one of the most critical challenges within the field of forensic science. Nonetheless, the estimation of PMI has emerged as one of the most complex and challenging domains of research within the field, primarily d...

Amino acid sequence-based IDR classification using ensemble machine learning and quantum neural networks.

Computational biology and chemistry
Biologically traditional methods, such as the Uversky plot, which rely on hydrophobicity and net charge, have inherent limitations in accurately distinguishing intrinsically disordered regions (IDRs) from ordered protein regions. To overcome these co...

Prediction of Lymphoma Aggressiveness Using Machine Learning Algorithms.

International journal of laboratory hematology
INTRODUCTION: Lymph nodes are essential to diagnose lymphoid neoplasms, metastases, and infections. Some lymphomas, particularly aggressive non-Hodgkin lymphomas (NHL), need urgent diagnosis. Combining lymph node cytology (LNC) and flow cytometry (FC...

Assessing the risk of problem gambling among lottery loyalty program members: A machine learning approach.

Addictive behaviors
BACKGROUND AND AIMS: Lottery gambling is a relatively benign form of gambling. Nonetheless, individuals with gambling problems may engage in lottery play and/or play the lottery exclusively. Lottery loyalty programs have data that could be used to sc...

SEISMIC-HF 1: key findings from AHA24 and implications for remote cardiac monitoring.

Heart failure reviews
While there is continued progress in developing therapies for patients with heart failure, the condition results in significant morbidity and a sizeable economic impact on our society. Recent advances in wearable sensors combined with machine learnin...

Red alarm: Controllable backdoor attack in continual learning.

Neural networks : the official journal of the International Neural Network Society
Continual learning (CL) studies the problem of learning a single model from a sequence of disjoint tasks. The main challenge is to learn without catastrophic forgetting, a scenario in which the model's performance on previous tasks degrades significa...