Work Force

Many-objective BAT algorithm.

In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. ...

Operational framework and training standard requirements for AI-empowered robotic surgery.

BACKGROUND: For autonomous robot-delivered surgeries to ever become a feasible option, we recommend ...

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.

BACKGROUND AND OBJECTIVE: The novel Coronavirus also called COVID-19 originated in Wuhan, China in D...

Effectiveness of robot-assisted gait training on patients with burns: a preliminary study.

Gait enables individuals to move forward and is considered a natural skill. However, gait disturbanc...

Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators.

Studies on improving the adaptability of upper limb rehabilitation training do not often consider th...

TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network.

Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increas...

A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers.

Late-arriving patients have become a prominent concern in several ambulatory care clinics across the...

Effect of Robot-Assisted Gait Training on Selective Voluntary Motor Control in Ambulatory Children with Cerebral Palsy.

This pilot study investigated the efficacy of a four week robot-assisted gait training in twelve chi...

The effects of different levels of realism on the training of CNNs with only synthetic images for the semantic segmentation of robotic instruments in a head phantom.

PURPOSE: The manual generation of training data for the semantic segmentation of medical images usin...

A deep metric learning approach for histopathological image retrieval.

To distinguish ambiguous images during specimen slides viewing, pathologists usually spend lots of t...

A Comparison between Several Response Surface Methodology Designs and a Neural Network Model to Optimise the Oxidation Conditions of a Lignocellulosic Blend.

In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are...

A deep learning system for differential diagnosis of skin diseases.

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are s...

Deep neural model with self-training for scientific keyphrase extraction.

Scientific information extraction is a crucial step for understanding scientific publications. In th...

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets.

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CX...

Training memristor-based multilayer neuromorphic networks with SGD, momentum and adaptive learning rates.

Neural networks implemented with traditional hardware face inherent limitation of memory latency. Sp...

Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video).

BACKGROUND AND AIMS: EUS is considered one of the most sensitive modalities for pancreatic cancer de...

Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction.

Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of...

A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonan...

Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye.

In this study, we propose a method for training convolutional neural networks to make them identify ...

Future of Robotic Surgical Education: The (R)evolution from Training the Barber-surgeon to Subspecialist Robot-surgeon.

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

PURPOSE: To compare performance of independently developed deep learning algorithms for detecting gl...