AIMC Topic: Humans

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Mini-batch optimization enables training of ODE models on large-scale datasets.

Nature communications
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established paramete...

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques.

BioMed research international
Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of th...

Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer.

Journal of healthcare engineering
This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combine...

Metaheuristics with Deep Learning-Enabled Parkinson's Disease Diagnosis and Classification Model.

Journal of healthcare engineering
Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is significant to detect the PD at the initial stages so that the person can live a peaceful life for a ...

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques.

Journal of healthcare engineering
Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, b...

Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network.

Computational and mathematical methods in medicine
Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the prediction model of infection in patients with lung cancer during chemotherapy by an artificial neural...

Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition.

Computational intelligence and neuroscience
The traditional human action recognition (HAR) method is based on RGB video. Recently, with the introduction of Microsoft Kinect and other consumer class depth cameras, HAR based on RGB-D (RGB-Depth) has drawn increasing attention from scholars and i...

Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model.

Computational intelligence and neuroscience
Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. Because of the emergence of smart wea...

Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning.

Nature medicine
Early use of effective antimicrobial treatments is critical for the outcome of infections and the prevention of treatment resistance. Antimicrobial resistance testing enables the selection of optimal antibiotic treatments, but current culture-based t...

Interpretable machine learning for high-dimensional trajectories of aging health.

PLoS computational biology
We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine ...