AIMC Topic: Humans

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Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-Resolution Electrogastrogram.

IEEE transactions on bio-medical engineering
OBJECTIVE: Pediatric functional nausea is challenging for patients to manage and for clinicians to treat since it lacks objective diagnosis and assessment. A data-driven non-invasive diagnostic screening tool that distinguishes the electro-pathophysi...

Machine Learning for Touch Localization on an Ultrasonic Lamb Wave Touchscreen.

Sensors (Basel, Switzerland)
Classification and regression employing a simple Deep Neural Network (DNN) are investigated to perform touch localization on a tactile surface using ultrasonic guided waves. A robotic finger first simulates the touch action and captures the data to t...

Machine Learning Prediction of Clinical Trial Operational Efficiency.

The AAPS journal
Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, ...

Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning.

Scientific reports
The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of tec...

Deep learning for spirometry quality assurance with spirometric indices and curves.

Respiratory research
BACKGROUND: Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitiv...

Gender Bias and Conversational Agents: an ethical perspective on Social Robotics.

Science and engineering ethics
The increase in the spread of conversational agents urgently requires to tackle the ethical issues linked to their design. In fact, developers frequently include in their products cues that trigger social biases in order to maximize the performance a...

Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models.

Computational and mathematical methods in medicine
INTRODUCTION: While the COVID-19 pandemic was waning in most parts of the world, a new wave of COVID-19 Omicron and Delta variants in Central Asia and the Middle East caused a devastating crisis and collapse of health-care systems. As the diagnostic ...

Construction and Implementation of Procedural Nursing System for General Surgery Laparoscopic Surgery Based on Deep Learning.

Journal of healthcare engineering
In order to explore the construction and implementation effect of a procedural nursing system for laparoscopic surgery in general surgery based on deep learning, this article selects 150 cases of laparoscopic surgery patients admitted to our hospital...

Training of artificial neural networks with the multi-population based artifical bee colony algorithm.

Network (Bristol, England)
Nowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers' acquisition of the learning and interpr...

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality.

Critical reviews in food science and nutrition
Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the ...