AIMC Topic: Humans

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Multimodal image synthesis based on disentanglement representations of anatomical and modality specific features, learned using uncooperative relativistic GAN.

Medical image analysis
Growing number of methods for attenuation-coefficient map estimation from magnetic resonance (MR) images have recently been proposed because of the increasing interest in MR-guided radiotherapy and the introduction of positron emission tomography (PE...

Development of lumbar spine MRI referrals vetting models using machine learning and deep learning algorithms: Comparison models vs healthcare professionals.

Radiography (London, England : 1995)
INTRODUCTION: Referrals vetting is a necessary daily task to ensure the appropriateness of radiology referrals. Vetting requires extensive clinical knowledge and may challenge those responsible. This study aims to develop AI models to automate the ve...

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16.

Sensors (Basel, Switzerland)
The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional...

Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers.

Sensors (Basel, Switzerland)
Development and operations (DevOps), artificial intelligence (AI), big data and edge-fog-cloud are disruptive technologies that may produce a radical transformation of the industry. Nevertheless, there are still major challenges to efficiently applyi...

A Survey of Robot Swarms' Relative Localization Method.

Sensors (Basel, Switzerland)
For robot swarm applications, accurate positioning is one of the most important requirements for avoiding collisions and keeping formations and cooperation between individuals. However, in some worst cases, the GNSS (Global Navigation Satellite Syste...

Enhancing PM Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model.

Sensors (Basel, Switzerland)
In a world where humanity's interests come first, the environment is flooded with pollutants produced by humans' urgent need for expansion. Air pollution and climate change are side effects of humans' inconsiderate intervention. Particulate matter of...

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China.

International journal of environmental research and public health
Ozone (O3), whose concentrations have been increasing in eastern China recently, plays a key role in human health, biodiversity, and climate change. Accurate information about the spatiotemporal distribution of O3 is crucial for human exposure studie...

A deep learning model to classify neoplastic state and tissue origin from transcriptomic data.

Scientific reports
Application of deep learning methods to transcriptomic data has the potential to enhance the accuracy and efficiency of tissue classification and cell state identification. Herein, we developed a multitask deep learning model for tissue classificatio...

Human Intelligence Analysis through Perception of AI in Teaching and Learning.

Computational intelligence and neuroscience
Instructional practices have undergone a drastic change as a result of the development of new educational technology. Artificial intelligence (AI) as a teaching and learning technology will be examined in this theoretical review study. To enhance the...

Evaluation of the Physical Education Teaching and Training Efficiency by the Integration of Ideological and Political Courses with Lightweight Deep Learning.

Computational intelligence and neuroscience
The purpose is to improve the training effect of physical education (PE) based on the teaching concept of ideological and political courses. The research is supported by the lightweight deep learning (DL) model of the Internet of things (IoT). Throug...