Results 71 to 80 of about 1,116,663 (238)
A Multi‐Sequence Adversarial Fusion U‐Net for Brain Tumor Image Segmentation
IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.In the field of brain tumor image segmentation, in order to avoid the impact of insufficient number of training samples, the method of fusing multi‐modal MRI information before segmentation is widely used. However, when fusing different modal features, existing methods only add fixed weights to the features of each modality, resulting in insufficient ...
Jie Wang, Jinglu Hu
wiley +1 more source
Hyper-learning for population-based incremental learning in dynamic environments [PDF]
, 2009 This article is posted here here with permission from IEEE - Copyright @ 2009 IEEEThe population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning.
Yang, S +5 more
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Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive Learning
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Graph contrastive learning (GCL) relies on acquiring high‐quality positive and negative samples to learn the structural semantics of the input graph. Previous approaches typically sampled negative samples from the same training batch or an irrelevant external graph.
Haoran Yang +7 more
wiley +1 more source
Robust Multi‐Source Batch Normalisation for Test‐Time Batch Adaptation
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Test‐Time Batch Adaptation (TTBA) aims to adapt a pre‐trained source model to small, unlabelled target batches at test time. The TTBA methods focus on adapting the pre‐trained model or the target data in a one‐to‐one alignment paradigm. However, these one‐to‐one alignment paradigms assume that the source domain may share the same knowledge ...
Xinlin Xiao +3 more
wiley +1 more source
Hyper-heuristics: theory and applications
, 2018 This introduction to the field of hyper-heuristics presents the required foundations and tools and illustrates some of their applications. The authors organized the 13 chapters into three parts.
Qu, Rong, Pillay, Nelishia
core +1 more source
IEEE AccessThe rise of online shopping has forced warehouses to improve their operations to keep up with demand. In this work, we focus on a warehouse type known as the Robotic Mobile Fulfillment System (RMFS).
Toby Yung +3 more
doaj +1 more source
Sub‐optimal Internet of Thing devices deployment using branch and bound method
IET Networks, EarlyView.The main contributions of this paper are (1) IoT network deployment problem formation as MILP problem to optimise the transmission among network nodes, and (2) New BB method with a machine learning function to reduce the computational complexity. Abstract The Internet of Thing (IoT) network deployments are widely investigated in 4G and 5G systems and ...
Haesik Kim
wiley +1 more source
British Journal of Learning Disabilities, EarlyView.ABSTRACT Background People with learning disabilities often face significant challenges in understanding health information. Pictorial supports are widely assumed to improve communication for people with learning disabilities, yet little research examines how visual communication can be effectively designed for this group.
Alison Drewett +5 more
wiley +1 more source
Novel Hyper-heuristics Applied to the Domain of Bin Packing [PDF]
Principal to the ideology behind hyper-heuristic research is the desire to increase the level of generality of heuristic procedures so that they can be easily applied to a wide variety of problems to produce solutions of adequate quality within practical
Sim, Kevin
core
VehiclesThis paper introduces a novel neuromorphic-inspired hyper-heuristic framework (NeuHH) for solving the Capacitated Single-Allocation p-Hub Location Routing Problem (CSAp-HLRP), a challenging combinatorial optimization problem that jointly addresses hub ...
Kassem Danach +3 more
doaj +1 more source