English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In recent years, there have been many popular Convolutional Neural Networks(CNNs), such as Google's Inception-V4, that have performed very well forvarious image classification problems. These commonly used CNN models usuallyuse the same activation function, such as RELU, for all neurons in theconvolutional layers; they are "Single-function CNNs." However, SCNNs may notalways be optimal. Thus, a "Multi-function CNN" (MCNN), which uses differentactivation functions for different neurons, has been shown to outperform aSCNN. Also, CNNs typically have very large architectures that use a lot ofmemory and need a lot of data in order to be trained well. As a result, theytend to have very high training and prediction times too. An important researchproblem is how to automatically and efficiently find the best CNN with bothhigh classification performance and compact architecture with high training andprediction speeds, small power usage, and small memory size for any imageclassification problem. It is very useful to intelligently find an effective,fast, energy-efficient, and memory-efficient "Compressed Multi-function CNN"(CMCNN) from a large number of candidate MCNNs. A new compensatory algorithmusing a new genetic algorithm (GA) is created to find the best CMCNN with anideal compensation between performance and architecture size. The optimal CMCNNhas the best performance and the smallest architecture size. Simulations usingthe CIFAR10 dataset showed that the new compensatory algorithm could findCMCNNs that could outperform non-compressed MCNNs in terms of classificationperformance (F1-score), speed, power usage, and memory usage. Other effective,fast, power-efficient, and memory-efficient CMCNNs based on popular CNNarchitectures will be developed for image classification problems in importantreal-world applications, such as brain informatics and biomedical imaging.

A New Compensatory Genetic Algorithm-Based Method for Effective  Compressed Multi-function Convolutional Neural Network Model Selection with  Multi-Objective Optimization