AIMC Topic: Humans

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Artificial Intelligence Based Customer Churn Prediction Model for Business Markets.

Computational intelligence and neuroscience
The introduction of artificial intelligence (AI) and machine learning (ML) technologies in recent years has resulted in improved company performance. Customer churn forecast is a difficult problem in many corporate sectors, particularly the telecommu...

A Deep Learning-Based TE Method for MSs' Mental Health Analysis.

Journal of environmental and public health
Teaching evaluation (TE) is of great significance in education and can judge the value and appropriateness of the curriculum, which is a distinguished part of the educational work. Compared with other courses focusing on imparting knowledge, mental h...

Forecasting Slope Displacement of the Agricultural Mountainous Area Based on the ACO-SVM Model.

Computational intelligence and neuroscience
Due to the combined influence of complex engineering geological conditions and environmental factors from agricultural mountainous areas, the evolution of slope deformation is complicated and nonlinear. Support vector machine (SVM) technology could e...

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods.

Computational intelligence and neuroscience
Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with ...

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System.

Computational intelligence and neuroscience
Pedestrian detection and tracking based on computer vision has gradually become an international pattern recognition, which is one of the most active research topics in the field of computer vision and artificial intelligence. Using the theoretical r...

Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis.

NeuroImage. Clinical
Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to achieve replicable accuracy. This is problematic for fMRI datasets as they are typically smal...

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program.

PloS one
We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists' subjective evaluation criteria according to the Korea insti...

Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance.

eNeuro
The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, there have been methodological advances in designing machine-learning algorithms to improve artifact d...

Estimation of Cerebral Blood Flow and Arterial Transit Time From Multi-Delay Arterial Spin Labeling MRI Using a Simulation-Based Supervised Deep Neural Network.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: An inherently poor signal-to-noise ratio (SNR) causes inaccuracy and less precision in cerebral blood flow (CBF) and arterial transit time (ATT) when using arterial spin labeling (ASL). Deep neural network (DNN)-based parameter estimation...

Robotic radical distal gastrectomy for gastric cancer using the soft coagulation scissors technique.

Journal of robotic surgery
We have developed a novel technique for safe and precise lymph-node dissection during robotic gastrectomy for gastric cancer using monopolar curved scissors with soft coagulation. This technique is called the soft coagulation scissors technique. The ...