Product Alert

A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning.

Autonomous transportation systems have the potential to greatly impact the way we travel. A vital aspect of these systems is their connectivity, facilitated by intelligent transport applications. However, the safety ensured by the vehicular network can be easily compromised by malicious traffic with the exponential growth of IoT devices. One aspect is malicious traffic identification in Vehicular ...

GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series.

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with type 1 or 2 diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been achieved by leveraging advanced deep learning methods to model multimodal data, i.e., sensor data and self-reported event data, organized as multi-varia...

Computer-aided cholelithiasis diagnosis using explainable convolutional neural network.

Accurate and precise identification of cholelithiasis is essential for saving the lives of millions of people worldwide. Although several computer-aid...

Using machine learning to predict outcomes following transcarotid artery revascularization.

Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedure that carries a non-negligible risk of complicat...

Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.

Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been ...

Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.

BACKGROUND: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post...

Advancing newborn care: Precise time of birth detection using ai-driven thermal imaging with adaptive normalization.

Around 5%-10% of newborns need assistance to start breathing. Currently, there is a lack of evidence-based research, objective data collection, and op...

Robot-Assisted Endodontic Retreatment: A Case Report with Clinical Considerations.

Fiber posts present significant challenges for nonsurgical endodontic retreatment, as improper removal may result in iatrogenic root perforation or ev...

Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes.

OBJECTIVE: This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in ...

MCTASmRNA: A deep learning framework for alternative splicing events classification.

Alternative splicing (AS) plays crucial post-transcriptional gene function regulation roles in eukaryotic. Despite progress in studying AS at the RNA ...

Automated post-run analysis of arrayed quantitative PCR amplification curves using machine learning.

BACKGROUND: The TaqMan Array Card (TAC) is an arrayed, high-throughput qPCR platform that can simultaneously detect multiple targets in a single react...

Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report.

BackgroundIntensive care unit (ICU) acquired weakness is a detrimental condition characterized by muscle weakness, difficulty in weaning from mechanic...

Predicting work ability impairment in post COVID-19 patients: a machine learning model based on clinical parameters.

The Post COVID-19 condition (PCC) is a complex disease affecting health and everyday functioning. This is well reflected by a patient's inability to w...

Effects of UV-B light exposure during automatic milking on vitamin D levels in Holstein Friesian cows.

Vitamin D is essential for cattle and can be synthesized in the skin under ultraviolet irradiation. This study investigated the effects of narrow-band...

Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.

BACKGROUND: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (E...

Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning.

With the growing global challenge of drug abuse, there is an urgent need for rapid, accurate, and cost-effective drug detection methods. This study in...

Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.

Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learnin...

Accelerating computational fluid dynamics simulation of post-combustion carbon capture modeling with MeshGraphNets.

Packed columns are commonly used in post-combustion processes to capture CO emissions by providing enhanced contact area between a CO-laden gas and CO...

AI-assisted digital video analysis reveals changes in gait among three-day event horses during competition.

The value and welfare of performance horses is closely tied to locomotor behaviors, but we lack objective and quantitative measures for these characte...

Machine learning and statistical shape modelling for real-time prediction of stent deployment in realistic anatomies.

The rise in minimally invasive procedures has created a demand for efficient and reliable planning software to predict intra- and post-operative outco...