AIMC Topic: Humans

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Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnormalities. Despite this interest, deep feature representation for ECG is still challenging and intrigu...

Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0-6 Years.

International journal of environmental research and public health
Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud techno...

Determinants of implementing of pet robots in nursing homes for dementia care.

BMC geriatrics
BACKGROUND: Pet robots have been employed as viable substitutes to pet therapy in nursing homes. Despite their potential to enhance the psychosocial health of residents with dementia, there is a lack of studies that have investigated determinants of ...

Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study.

BMC neurology
BACKGROUNDS: We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchmarking models for quality improvement in stroke care.

A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy.

Scientific reports
Early regression-the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)-is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coo...

Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach.

Nutrition & diabetes
BACKGROUND: Studies on Type-2 Diabetes Mellitus (T2DM) have revealed heterogeneous sub-populations in terms of underlying pathologies. However, the identification of sub-populations in epidemiological datasets remains unexplored. We here focus on the...

Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm.

Physics in medicine and biology
The output of a deep learning (DL) auto-segmentation application should be reviewed, corrected if needed and approved before being used clinically. This verification procedure is labour-intensive, time-consuming and user-dependent, which potentially ...

Research on Performance Optimization Algorithm of Resource and Environment Audit Based on Computer Technology.

Computational intelligence and neuroscience
With the rapid development of the economy and society, the sustainable development of resources and environment has been paid more and more attention. As an important part of the national environmental supervision system, resources and environmental ...

Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning.

Computational intelligence and neuroscience
Depression is a global prevalent ailment for possible mental illness or mental disorder globally. Recognizing depressed early signs is critical for evaluating and preventing mental illness. With the progress of machine learning, it is possible to mak...

Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network.

Computational intelligence and neuroscience
This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding rec...