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Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter.

IEEE transactions on neural networks and learning systems
This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an ...

A multi-technique approach to bridge electronic case report form design and data standard adoption.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVE: The importance of data standards when integrating clinical research data has been recognized. The common data element (CDE) is a consensus-based data element for data harmonization and sharing between clinical researchers, i...

An Artificial Intelligence-Based Diagnostic System for Acute Lymphoblastic Leukemia Detection.

Studies in health technology and informatics
This study suggests a novel Acute Lymphoblastic Leukemia (ALL) diagnostic model, built solely on complete blood count (CBC) records. Using a dataset comprised of CBC records of 86 ALL and 86 control patients respectively, we identified the most ALL-s...

EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This approach holds the underlying assumption that electrodes are equidistant analogous to p...

Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Automated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression of rare but severe vaccine adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algo...

Path planning of mobile robot based on improved ant colony algorithm for logistics.

Mathematical biosciences and engineering : MBE
The path planning of robot is of great significance for the logistics industry, which helps to improve the efficiency of warehousing, sorting and distribution. On the basis of ant colony algorithm, multi step search strategy is used instead of single...

Endoscopy-assisted magnetic navigation of biohybrid soft microrobots with rapid endoluminal delivery and imaging.

Science robotics
High-precision delivery of microrobots at the whole-body scale is of considerable importance for efforts toward targeted therapeutic intervention. However, vision-based control of microrobots, to deep and narrow spaces inside the body, remains a chal...

A deep learning-based medication behavior monitoring system.

Mathematical biosciences and engineering : MBE
The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical domains. In the COVID-19 era, intelligent medication behavior monitoring systems for stable patient...

Artificial immune intelligence-inspired dynamic real-time computer forensics model.

Mathematical biosciences and engineering : MBE
Dynamic computer forensics is a popular area in computer forensics that combines network intrusion technology with computer forensics technology. A novel dynamic computer forensics model is proposed based on an artificial immune system. Simulating th...

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer.

Journal of digital imaging
Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural net...