Learning-Based Genetic Algorithm to Schedule an Extended Flexible Job Shop.

This work considers an extended flexible job-shop scheduling problem from a semiconductor manufacturing environment. To find its high-quality solution in a reasonable time, a learning-based genetic algorithm (LGA) that incorporates a parallel long short-term memory network-embedded autoencoder model is proposed. In it, genetic algorithm is selected as a main optimizer.

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies.

A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initialization method specifically designed to improve the performance of DNM in classifying high-dimensional data, notable for its simplicity, speed, and straightforward implementation.

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 work proposes a learners' interpretability diversity (LID) to measure the diversity of interpretable machine learners.

Enhanced Ageing Performance of Sulfonic Acid-Grafted Pt/C Catalysts.

Chemical functionalization of carbon support for Pt catalysts is a promising way to enhance the performance of catalysts. In this study, Pt/C catalysts grafted with various amounts of phenylsulfonic acid groups were prepared under mild conditions. The influence of sulfonic acid groups on the physiochemical characteristics and electrochemical activities of the modified catalysts were studied using X-ray diffraction, X-ray photoelectron spectroscopy, a transmission electron microscope, and cyclic voltammetry (CV).

Decision-Tree-Initialized Dendritic Neuron Model for Fast and Accurate Data Classification.

This work proposes a decision tree (DT)-based method for initializing a dendritic neuron model (DNM). Neural networks become larger and larger, thus consuming more and more computing resources. This calls for a strong need to prune neurons that do not contribute much to their network's output.