AIMC Topic: Algorithms

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Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology.

The Plant journal : for cell and molecular biology
Advances in high-throughput omics technologies are leading plant biology research into the era of big data. Machine learning (ML) performs an important role in plant systems biology because of its excellent performance and wide application in the ana...

Improving Network-Based Anomaly Detection in Smart Home Environment.

Sensors (Basel, Switzerland)
The Smart Home (SH) has become an appealing target of cyberattacks. Due to the limitation of hardware resources and the various operating systems (OS) of current SH devices, existing security features cannot protect such an environment. Generally, th...

Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion.

Sensors (Basel, Switzerland)
Automatic recognition of human emotions is not a trivial process. There are many factors affecting emotions internally and externally. Expressing emotions could also be performed in many ways such as text, speech, body gestures or even physiologicall...

Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.

Sensors (Basel, Switzerland)
Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely a...

Clinically focused multi-cohort benchmarking as a tool for external validation of artificial intelligence algorithm performance in basic chest radiography analysis.

Scientific reports
Artificial intelligence (AI) algorithms evaluating [supine] chest radiographs ([S]CXRs) have remarkably increased in number recently. Since training and validation are often performed on subsets of the same overall dataset, external validation is man...

Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow.

Scientific reports
In this study, we propose a two-stage workflow used for the segmentation and scoring of lung diseases. The workflow inherits quantification, qualification, and visual assessment of lung diseases on X-ray images estimated by radiologists and clinician...

Fast VMAT planning for prostate radiotherapy: dosimetric validation of a deep learning-based initial segment generation method.

Physics in medicine and biology
. To develop and evaluate a deep learning based fast volumetric modulated arc therapy (VMAT) plan generation method for prostate radiotherapy.. A customized 3D U-Net was trained and validated to predict initial segments at 90 evenly distributed contr...

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.

BioMed research international
Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always results in death. Chemotherapy, surgery, radiation therapy, and immunotherapy are all potential treatment...

Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms.

Sensors (Basel, Switzerland)
Nowadays in modern societies, a sedentary lifestyle is almost inevitable for a majority of the population. Long hours of sitting, especially in wrong postures, may result in health complications. A smart chair with the capability to identify sitting ...

Survival prediction models: an introduction to discrete-time modeling.

BMC medical research methodology
BACKGROUND: Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods...