AIMC Topic: Machine Learning

Clear Filters Showing 11511 to 11520 of 34417 articles

One model to use them all: training a segmentation model with complementary datasets.

International journal of computer assisted radiology and surgery
PURPOSE: Understanding surgical scenes is crucial for computer-assisted surgery systems to provide intelligent assistance functionality. One way of achieving this is via scene segmentation using machine learning (ML). However, such ML models require ...

Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast.

International journal of biometeorology
Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns th...

Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis.

Sensors (Basel, Switzerland)
This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digita...

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process.

International journal of molecular sciences
This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from waste (). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar...

Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study.

BMC cancer
BACKGROUND: Cervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomic and descriptive semantic fe...

Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach.

Scientific reports
In hypertensive intracerebral hemorrhage (HICH) patients, while emergency surgeries effectively reduce intracranial pressure and hematoma volume, their significant risk of causing postoperative rehemorrhage necessitates early detection and management...

Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.

BMJ health & care informatics
BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of mainte...

Identifying time-resolved features of nocturnal sleep characteristics of narcolepsy using machine learning.

Journal of sleep research
The differential diagnosis of narcolepsy type 1, a rare, chronic, central disorder of hypersomnolence, is challenging due to overlapping symptoms with other hypersomnolence disorders. While recent years have seen significant growth in our understandi...

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features.

GeroScience
To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A r...

Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVES: To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first-trimester screening for pre-eclampsia in a large Asian population.