[PDF][PDF] Human-cyber-physical system for Industry 5.0: A review from a human-centric perspective
Industry 5.0 heralds a new wave of the industrial revolution, placing a spotlight on human-
centric intelligent manufacturing. At the core of Industry 5.0 lies the human-cyberphysical …
centric intelligent manufacturing. At the core of Industry 5.0 lies the human-cyberphysical …
Real-time sensing and fault diagnosis for transmission lines
Protection of high voltage transmission lines is one of the crucial problems in the power
system engineering. Accurate and timely detection and identification of transmission line …
system engineering. Accurate and timely detection and identification of transmission line …
Solving dynamic traveling salesman problems with deep reinforcement learning
A traveling salesman problem (TSP) is a well-known NP-complete problem. Traditional TSP
presumes that the locations of customers and the traveling time among customers are fixed …
presumes that the locations of customers and the traveling time among customers are fixed …
Fully complex-valued dendritic neuron model
A single dendritic neuron model (DNM) that owns the nonlinear information processing
ability of dendrites has been widely used for classification and prediction. Complex-valued …
ability of dendrites has been widely used for classification and prediction. Complex-valued …
A Survey of Neural Trees: Co-Evolving Neural Networks and Decision Trees
Neural networks (NNs) and decision trees (DTs) are both popular models of machine
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …
A synthetic minority oversampling technique based on Gaussian mixture model filtering for imbalanced data classification
Data imbalance is a common phenomenon in machine learning. In the imbalanced data
classification, minority samples are far less than majority samples, which makes it difficult for …
classification, minority samples are far less than majority samples, which makes it difficult for …
A length-adaptive non-dominated sorting genetic algorithm for Bi-objective high-dimensional feature selection
As a crucial data preprocessing method in data mining, feature selection (FS) can be
regarded as a bi-objective optimization problem that aims to maximize classification …
regarded as a bi-objective optimization problem that aims to maximize classification …
Interpretability diversity for decision-tree-initialized dendritic neuron model ensemble
To construct a strong classifier ensemble, base classifiers should be accurate and diverse.
However, there is no uniform standard for the definition and measurement of diversity. This …
However, there is no uniform standard for the definition and measurement of diversity. This …
Pruning method for dendritic neuron model based on dendrite layer significance constraints
X Luo, X Wen, Y Li, Q Li - CAAI Transactions on Intelligence …, 2023 - Wiley Online Library
The dendritic neural model (DNM) mimics the non‐linearity of synapses in the human brain
to simulate the information processing mechanisms and procedures of neurons. This …
to simulate the information processing mechanisms and procedures of neurons. This …
Comparison of CNN classification model using machine learning with bayesian optimizer
S Surono, MYF Afitian, A Setyawan… - HighTech and …, 2023 - hightechjournal.org
One of the best-known and frequently used areas of Deep Learning in image processing is
the Convolutional Neural Network (CNN), which has architectural designs such as …
the Convolutional Neural Network (CNN), which has architectural designs such as …