Surveys

Global exponential synchronization of delayed memristive neural networks with reaction-diffusion terms.

This paper investigates the global exponential synchronization problem of delayed memristive neural networks (MNNs) with reaction-diffusion terms. First, by utilizing the pinning control technique, two novel kinds of control methods are introduced to achieve synchronization of delayed MNNs with reaction-diffusion terms. Then, with the help of inequality techniques, pinning control technique, the d...

Automated content analysis across six languages.

Corpus selection bias in international relations research presents an epistemological problem: How do we know what we know? Most social science research in the field of text analytics relies on English language corpora, biasing our ability to understand international phenomena. To address the issue of corpus selection bias, we introduce results that suggest that machine translation may be used to ...

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches.

BACKGROUND: The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory bio...

Patients' views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire.

OBJECTIVES: The patients' view on the implementation of artificial intelligence (AI) in radiology is still mainly unexplored territory. The aim of thi...

SECNLP: A survey of embeddings in clinical natural language processing.

Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well as capture prior knowledge which can t...

ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder.

Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to tak...

Machine learning algorithm validation with a limited sample size.

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high...

Mean-field models in swarm robotics: a survey.

We present a survey on the application of fluid approximations, in the form of mean-field models, to the design of control strategies in swarm robotic...

Ultrafast Plane Wave Imaging With Line-Scan-Quality Using an Ultrasound-Transfer Generative Adversarial Network.

In the medical ultrasound field, ultrafast imaging has recently become a hot topic. However, the diagnostic reliability of ultrafast high-frame rate p...

An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data.

In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted...

Measuring and improving adaptive capacity in resilient systems by means of an integrated DEA-Machine learning approach.

Resilient systems strive to enhance the safety of complex systems through building and developing adaptive technological and organizational capacities...

Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability.

The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of co...

Self Multi-Head Attention-based Convolutional Neural Networks for fake news detection.

With the rapid development of the internet, social media has become an essential tool for getting information, and attracted a large number of people ...

Synchronization in an array of coupled neural networks with delayed impulses: Average impulsive delay method.

In the paper, synchronization of coupled neural networks with delayed impulses is investigated. In order to overcome the difficulty that time delays c...

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.

Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by...

EMG-based lumbosacral joint compression force prediction using a support vector machine.

Electromyography-assisted optimization (EMGAO) approach is widely used to predict lumbar joint loads under various dynamic and static conditions. Howe...

PTSD and its dissociative subtype through the lens of the insula: Anterior and posterior insula resting-state functional connectivity and its predictive validity using machine learning.

Individuals with post-traumatic stress disorder (PTSD) typically experience states of reliving and hypervigilance; however, the dissociative subtype o...

LncRNA-Disease Associations Prediction Using Bipartite Local Model With Nearest Profile-Based Association Inferring.

There is much evidence that long non-coding RNA (lncRNA) is associated with many diseases. However, it is time-consuming and expensive to identify mea...

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SN...

Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening.

Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes ...