AIMC Topic: Algorithms

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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...

Comparison of Convolutional and Conventional Artificial Neural Networks for Laser-Induced Breakdown Spectroscopy Quantitative Analysis.

Applied spectroscopy
The introduction of "deep learning" algorithms for feature identification in digital imaging has paved the way for artificial intelligence applications that up to a decade ago were considered technologically impossible to achieve, from the developmen...

Usage of Evolutionary Algorithms in Swarm Robotics and Design Problems.

Sensors (Basel, Switzerland)
In this study, the general structure of swarm robotics is examined. Algorithms inspired by nature, which form the basis of swarm robotics, are introduced. Communication topologies in robotic swarms, which are similar to the communication methods betw...

An Improved MobileNet Network with Wavelet Energy and Global Average Pooling for Rotating Machinery Fault Diagnosis.

Sensors (Basel, Switzerland)
In recent years, neural networks have shown good performance in terms of accuracy and efficiency. However, along with the continuous improvement in diagnostic accuracy, the number of parameters in the network is increasing and the models can often on...

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...

Few-Shot Text Classification with Global-Local Feature Information.

Sensors (Basel, Switzerland)
Meta-learning frameworks have been proposed to generalize machine learning models for domain adaptation without sufficient label data in computer vision. However, text classification with meta-learning is less investigated. In this paper, we propose ...

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...

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...

Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network.

Computational intelligence and neuroscience
We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and opti...

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...