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

'Hybrid meta-heuristics' is one of the most interesting recent trends in thefield of optimization and feature selection (FS). In this paper, we haveproposed a binary variant of Atom Search Optimization (ASO) and its hybrid withSimulated Annealing called ASO-SA techniques for FS. In order to map the realvalues used by ASO to the binary domain of FS, we have used two differenttransfer functions: S-shaped and V-shaped. We have hybridized this techniquewith a local search technique called, SA We have applied the proposed featureselection methods on 25 datasets from 4 different categories: UCI, Handwrittendigit recognition, Text, non-text separation, and Facial emotion recognition.We have used 3 different classifiers (K-Nearest Neighbor, Multi-LayerPerceptron and Random Forest) for evaluating the strength of the selectedfeatured by the binary ASO, ASO-SA and compared the results with some recentwrapper-based algorithms. The experimental results confirm the superiority ofthe proposed method both in terms of classification accuracy and number ofselected features.

Atom Search Optimization with Simulated Annealing -- a Hybrid Metaheuristic Approach for Feature Selection