AIMC Topic: Algorithms

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Tracking strategy changes using machine learning classifiers.

Behavior research methods
In complex tasks, high performers often have better strategies than low performers, even with similar amounts of practice. Relatively little research has examined how people form and change strategies in tasks that permit a large set of strategies. O...

Sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms.

Waste management (New York, N.Y.)
Sensor-based material flow characterization (SBMC) promises to improve the performance of future-generation sorting plants by enabling new applications like automatic quality monitoring or process control. Prerequisite for this is the derivation of m...

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children.

Nutrients
INTRODUCTION: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, RE...

Scoring Performance on the Y-Balance Test Using a Deep Learning Approach.

Sensors (Basel, Switzerland)
The Y Balance Test (YBT) is a dynamic balance assessment typically used in sports medicine. This work proposes a deep learning approach to automatically score this YBT by estimating the normalized reach distance (NRD) using a wearable sensor to regis...

Improving the Ability of a Laser Ultrasonic Wave-Based Detection of Damage on the Curved Surface of a Pipe Using a Deep Learning Technique.

Sensors (Basel, Switzerland)
With the advent of the Fourth Industrial Revolution, the economic, social, and technological demands for pipe maintenance are increasing due to the aging of the infrastructure caused by the increase in industrial development and the expansion of citi...

Algorithm Change Protocols in the Regulation of Adaptive Machine Learning-Based Medical Devices.

Journal of medical Internet research
One of the greatest strengths of artificial intelligence (AI) and machine learning (ML) approaches in health care is that their performance can be continually improved based on updates from automated learning from data. However, health care ML models...

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images.

Computational and mathematical methods in medicine
Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early det...

Application of Deep Neural Network Factor Analysis Model in Operating Room Management Nursing Analysis of Postoperative Infection Nursing after Thoracic Surgery.

Computational and mathematical methods in medicine
Thoracic surgery is the main surgical method for the treatment of respiratory diseases and lung diseases, but infections caused by improper care are prone to occur during the operation, which can induce pulmonary edema and lung injury and affect the ...

COVID-19 detection using chest X-ray images based on a developed deep neural network.

SLAS technology
AIM: Currently, a new coronavirus called COVID-19 is the biggest challenge of the human at 21st century. Now, the spread of this virus is such that mortality has risen strongly in all cities of countries. Therefore, it is necessary to think of a solu...

Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy exists in the 12-lead ECG, which could impose a systematic overfitting on DL, causing poo...