AIMC Topic: Algorithms

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Deep Learning-based Automated Aortic Area and Distensibility Assessment: the Multi-Ethnic Study of Atherosclerosis (MESA).

Journal of digital imaging
This study details application of deep learning for automatic segmentation of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging for automatic aortic analysis on the large MESA cohort with assessment on an exter...

On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition.

Sensors (Basel, Switzerland)
This work proposes a novel unsupervised self-organizing network, called the Self-Organizing Convolutional Echo State Network (SO-ConvESN), for learning node centroids and interconnectivity maps compatible with the deterministic initialization of Echo...

An Intelligent System for Proper Management and Disposal of Unused and Expired Medications.

International journal of environmental research and public health
For years, several countries have been concerned about how to dispose of unused pharmaceuticals that can endanger human health and the environment. Moreover, some people are in desperate need of medical attention and medications, but they lack the fi...

Composite-Learning-Based Adaptive Neural Control for Dual-Arm Robots With Relative Motion.

IEEE transactions on neural networks and learning systems
This article presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modeling uncertainties. Different from the traditional symmetric bimanual robot control, we study the dual-arm robot control with relative mot...

Detection of Backdoors in Trained Classifiers Without Access to the Training Set.

IEEE transactions on neural networks and learning systems
With wide deployment of deep neural network (DNN) classifiers, there is great potential for harm from adversarial learning attacks. Recently, a special type of data poisoning (DP) attack, known as a backdoor (or Trojan), was proposed. These attacks d...

Optimal Synchronization of Unidirectionally Coupled FO Chaotic Electromechanical Devices With the Hierarchical Neural Network.

IEEE transactions on neural networks and learning systems
This article solves the problem of optimal synchronization, which is important but challenging for coupled fractional-order (FO) chaotic electromechanical devices composed of mechanical and electrical oscillators and electromagnetic filed by using a ...

SMGEA: A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories.

IEEE transactions on neural networks and learning systems
Deep neural networks are vulnerable to adversarial attacks. More importantly, some adversarial examples crafted against an ensemble of source models transfer to other target models and, thus, pose a security threat to black-box applications (when att...

Output Feedback Control of Micromechanical Gyroscopes Using Neural Networks and Disturbance Observer.

IEEE transactions on neural networks and learning systems
This article addresses the output feedback control of micromechanical (MEMS) gyroscopes using neural networks (NNs) and disturbance observer (DOB). For the unmeasured system states, the state observer and the high gain observer are constructed. The a...

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights.

Sensors (Basel, Switzerland)
COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in via...

Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement.

Sensors (Basel, Switzerland)
We propose a linear regression model for the estimation of human body measurements. The input to the model only consists of the information that a person can self-estimate, such as height and weight. We evaluate our model against the state-of-the-art...