AIMC Topic: Machine Learning

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Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Atrial fibrillation (AF) is common in CKD and associated with poor kidney and cardiovascular outcomes. Prediction models developed using novel methods may be useful to identify patients with CKD at highest risk of incident ...

Human Interaction Understanding With Joint Graph Decomposition and Node Labeling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The task of human interaction understanding involves both recognizing the action of each individual in the scene and decoding the interaction relationship among people, which is useful to a series of vision applications such as camera surveillance, v...

Domain-Adaptive Fall Detection Using Deep Adversarial Training.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. However, it is not easy to obtain large-scale annotated fall events with various specifications of sensors or ...

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks.

Sensors (Basel, Switzerland)
The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the comple...

Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

BMC endocrine disorders
BACKGROUND: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients' management. However, to our knowledge, an inst...

A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes.

Fertility and sterility
OBJECTIVE: To determine whether a machine learning causal inference model can optimize trigger injection timing to maximize the yield of fertilized oocytes (2PNs) and total usable blastocysts for a given cohort of stimulated follicles.

Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane.

Biochemical and biophysical research communications
An early and accurate pregnancy diagnosis method is required to improve the reproductive performance of cows. Here we developed an easy pregnancy detection method using vaginal mucosal membrane (VMM) with application of Reverse Transcription-Loop-med...

Short-term wind speed prediction using hybrid machine learning techniques.

Environmental science and pollution research international
Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have bee...

The divided brain: Functional brain asymmetry underlying self-construal.

NeuroImage
Self-construal (orientations of independence and interdependence) is a fundamental concept that guides human behaviour, and it is linked to a large number of brain regions. However, understanding the connectivity of these regions and the critical pri...

Structure-aware machine learning identifies microRNAs operating as Toll-like receptor 7/8 ligands.

RNA biology
MicroRNAs (miRNAs) can serve as activation signals for membrane receptors, a recently discovered function that is independent of the miRNAs' conventional role in post-transcriptional gene regulation. Here, we introduce a machine learning approach, Br...