Position-transitional particle swarm optimization-incorporated latent factor analysis
High-dimensional and sparse (HiDS) matrices are frequently found in various industrial
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures
This paper proposes a customized convolutional neural network for crack detection in
concrete structures. The proposed method is compared to four existing deep learning …
concrete structures. The proposed method is compared to four existing deep learning …
Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction
An artificial neural network (ANN) that mimics the information processing mechanisms and
procedures of neurons in human brains has achieved a great success in many fields, eg …
procedures of neurons in human brains has achieved a great success in many fields, eg …
A data-characteristic-aware latent factor model for web services QoS prediction
How to accurately predict unknown quality-of-service (QoS) data based on observed ones is
a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) …
a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) …
Fast and accurate non-negative latent factor analysis of high-dimensional and sparse matrices in recommender systems
A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS)
matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and …
matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and …
Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors
Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns
hidden in such dynamic data for predicting missing ones with high accuracy. However …
hidden in such dynamic data for predicting missing ones with high accuracy. However …
Symmetric nonnegative matrix factorization-based community detection models and their convergence analysis
Community detection is a popular yet thorny issue in social network analysis. A symmetric
and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative …
and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative …
A double-space and double-norm ensembled latent factor model for highly accurate web service QoS prediction
Quality-of-Service (QoS), which describes the non-functional characteristics of Web service,
is of great significance in service selection. Since users cannot invoke all services to obtain …
is of great significance in service selection. Since users cannot invoke all services to obtain …
An efficient group recommendation model with multiattention-based neural networks
Group recommendation research has recently received much attention in a recommender
system community. Currently, several deep-learning-based methods are used in group …
system community. Currently, several deep-learning-based methods are used in group …
Generating randomness: making the most out of disordering a false order into a real one
Y Ilan - Journal of Translational Medicine, 2019 - Springer
Randomness is far from a disturbing disorder in nature. Rather, it underlies many processes
and functions. Randomness can be used to improve the efficacy of development and of …
and functions. Randomness can be used to improve the efficacy of development and of …