AIMC Topic: Machine Learning

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Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis.

Computational intelligence and neuroscience
With the development of science and technology, the rapid development of social economy, the motor as a new type of transmission equipment, in the production and life of people occupies a pivotal position. Under the rapid development of computer and ...

A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning.

Sensors (Basel, Switzerland)
Carrying out status monitoring and fault-diagnosis research on cutter-wear status is of great significance for real-time understanding of the health status of Tunnel Boring Machine (TBM) equipment and reducing downtime losses. In this work, we propos...

Individualized Diagnosis and Prescription in Traditional Medicine: Decision-Making Process Analysis and Machine Learning-Based Analysis Tool Development.

The American journal of Chinese medicine
While pattern identification (PI) is an essential process in traditional medicine (TM), it is difficult to objectify since it relies heavily on implicit knowledge. Therefore, this study aimed to propose a machine learning (ML)-based analysis tool to ...

A machine learning approach to predicting early and late postoperative reintubation.

Journal of clinical monitoring and computing
Accurate estimation of surgical risks is important for informing the process of shared decision making and informed consent. Postoperative reintubation (POR) is a severe complication that is associated with postoperative morbidity. Previous studies h...

Modeling Threats to AI-ML Systems Using STRIDE.

Sensors (Basel, Switzerland)
The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine Learning (ML), the most developed subfield of AI, allows...

IDA-MIL: Classification of Glomerular with Spike-like Projections via Multiple Instance Learning with Instance-level Data Augmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Tiny spike-like projections on the basement membrane of glomeruli are the main pathological feature of membranous nephropathy at stage II (MN II), which is the most significant stage for the diagnosis and treatment of renal ...

In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods.

Chemical research in toxicology
Liver microsomal stability is an important property considered for the screening of drug candidates in the early stage of drug development. Determination of hepatic metabolic stability can be performed by an in vitro assay, but it requires quite a fe...

Comprehensive Machine Learning Prediction of Extensive Enzymatic Reactions.

The journal of physical chemistry. B
New enzyme functions exist within the increasing number of unannotated protein sequences. Novel enzyme discovery is necessary to expand the pathways that can be accessed by metabolic engineering for the biosynthesis of functional compounds. According...

PyPLIF HIPPOS and Receptor Ensemble Docking Increase the Prediction Accuracy of the Structure-Based Virtual Screening Protocol Targeting Acetylcholinesterase.

Molecules (Basel, Switzerland)
In this article, the upgrading process of the structure-based virtual screening (SBVS) protocol targeting acetylcholinesterase (AChE) previously published in 2017 is presented. The upgraded version of PyPLIF called PyPLIF HIPPOS and the receptor ense...

A universal similarity based approach for predictive uncertainty quantification in materials science.

Scientific reports
Immense effort has been exerted in the materials informatics community towards enhancing the accuracy of machine learning (ML) models; however, the uncertainty quantification (UQ) of state-of-the-art algorithms also demands further development. Most ...