AIMC Topic: Machine Learning

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Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study.

Journal of medical Internet research
BACKGROUND: Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge because of the lack of a ...

Automatic detection of passing and shooting in water polo using machine learning: a feasibility study.

Sports biomechanics
There is currently no efficient way to quantify overhead throwing volume in water polo. Therefore, this study aimed to test the feasibility of a method to detect passes and shots in water polo automatically using inertial measurement units (IMU) and ...

Using artificial intelligence methods for systematic review in health sciences: A systematic review.

Research synthesis methods
The exponential increase in published articles makes a thorough and expedient review of literature increasingly challenging. This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the re...

Transfer learning for data-efficient abdominal muscle segmentation with convolutional neural networks.

Medical physics
BACKGROUND: Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emerging imaging biomarker of patient frailty. Data annotation remains the bottleneck for training deep learning auto-segmentation models.

Three-Dimensional Convolutional Neural Networks Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions.

Journal of chemical theory and computation
Deep learning methods provide a novel way to establish a correlation between two quantities. In this context, computer vision techniques such as three-dimensional (3D)-convolutional neural networks become a natural choice to associate a molecular pro...

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning.

Scientific reports
Detecting fraud related to electricity consumption is usually a difficult challenge as the input datasets are sometimes unreliable due to missing and inconsistent records, faults, misinterpretation of meter reading remarks, status, etc. In this paper...

An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease.

Computational intelligence and neuroscience
Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similar...

Machine learning predicts cancer subtypes and progression from blood immune signatures.

PloS one
Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapi...

Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry.

Biosensors
The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as autom...

Comparative analysis of explainable machine learning prediction models for hospital mortality.

BMC medical research methodology
BACKGROUND: Machine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acce...