Staffing & Scheduling

Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution.

Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example, as per RECIST or RANO criteria, is tedious and time-consuming, and can miss important tumor response information. Most notably, the prevalent response criteria often excl...

On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images.

Deep learning-based segmentation methods provide an effective and automated way for assessing the structure and function of the heart in cardiac magnetic resonance (CMR) images. However, despite their state-of-the-art performance on images acquired from the same source (same scanner or scanner vendor) as images used during training, their performance degrades significantly on images coming from di...

A class of doubly stochastic shift operators for random graph signals and their boundedness.

A class of doubly stochastic graph shift operators (GSO) is proposed, which is shown to exhibit: (i) lower and upper L-boundedness for locally station...

Scalable graph neural network for NMR chemical shift prediction.

Graph neural networks (GNNs) have been proven effective in the fast and accurate prediction of nuclear magnetic resonance (NMR) chemical shifts of a m...

Development of Liquid Chromatographic Retention Index Based on Cocamide Diethanolamine Homologous Series (C()-DEA).

There is a growing need for indexing and harmonizing retention time (tR) data in liquid chromatography derived under different conditions to aid in th...

Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium.

OBJECTIVE: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrati...

Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future.

Anatomy is taught in the early years of an undergraduate medical curriculum. The subject is volatile and of voluminous content, given the complex natu...

Multilayer redox-based HfO/AlO/TiO memristive structures for neuromorphic computing.

Redox-based memristive devices have shown great potential for application in neuromorphic computing systems. However, the demands on the device charac...

Orientation-Preserving Rewards' Balancing in Reinforcement Learning.

Auxiliary rewards are widely used in complex reinforcement learning tasks. However, previous work can hardly avoid the interference of auxiliary rewar...

FAPNET: Feature Fusion with Adaptive Patch for Flood-Water Detection and Monitoring.

In satellite remote sensing applications, waterbody segmentation plays an essential role in mapping and monitoring the dynamics of surface water. Sate...

Learning Transferable Parameters for Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) enables a learning machine to adapt from a labeled source domain to an unlabeled target domain under the distribu...

Association of artificial intelligence use and the retention of elderly caregivers: A cross-sectional study based on empowerment theory.

AIM: The purpose of this study is to investigate how the use of artificial intelligence is associated with the retention of elderly caregivers.

Conformal prediction under feedback covariate shift for biomolecular design.

Many applications of machine-learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that ...

Data Center Traffic Prediction Algorithms and Resource Scheduling.

This paper uses intelligent methods such as a time recurrent neural network to predict network traffic, mainly to solve the problems of resource imbal...

Deep learning-based neural networks for day-ahead power load probability density forecasting.

Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload ...

What are clinically relevant performance metrics in robotic surgery? A systematic review of the literature.

A crucial element of any surgical training program is the ability to provide procedure-specific, objective, and reliable measures of performance. Duri...

Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool.

In this paper, the side effects of drug therapy in the process of cancer treatment are reduced by designing two optimal non-linear controllers. The re...

Perception without preconception: comparison between the human and machine learner in recognition of tissues from histological sections.

Deep neural networks (DNNs) have shown success in image classification, with high accuracy in recognition of everyday objects. Performance of DNNs has...

Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments.

The Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response tim...

Shift Pose: A Lightweight Transformer-like Neural Network for Human Pose Estimation.

High-performing, real-time pose detection and tracking in real-time will enable computers to develop a finer-grained and more natural understanding of...