Special Issues
Table of Content

Emerging Machine Learning Methods and Applications

Submission Deadline: 30 June 2025 View: 1350 Submit to Special Issue

Guest Editors

Prof. Daniel-Ioan Curiac

Email: daniel.curiac@aut.upt.ro

Affiliation: Department of Automation and Applied Informatics, Politehnica University Timisoara, 300223, Timisoara , Romania

Homepage:

Research Interests: artificial intelligence, wireless sensor and actuator networks, information security


Summary

Over the past decade, there has been substantial progress in machine learning (ML) technology and its application, which has captured the interest of researchers across different domains. The goal of this Special Issue is to share the latest research findings and advancements in machine learning, emphasizing their practical use in various fields such as science, engineering, medicine, industry, robotics, and others. We welcome researchers and professionals to submit their high-quality research or review papers on these subjects to this Special Issue. Through this, a comprehensive overview of the challenges and opportunities ML has brought to these important sectors is provided.


We welcome original research or review papers and case studies that address the following topics (but not limited to):

machine learning

unsupervised learning

supervised learning

reinforcement learning

deep learning

transformers

regression

classification

transfer learning

natural language processing

speech processing

image processing and machine vision


Keywords

machine learning, deep learning, AI

Published Papers


  • Open Access

    ARTICLE

    Optimizing Feature Selection by Enhancing Particle Swarm Optimization with Orthogonal Initialization and Crossover Operator

    Indu Bala, Wathsala Karunarathne, Lewis Mitchell
    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 727-744, 2025, DOI:10.32604/cmc.2025.065706
    (This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
    Abstract Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets, significantly increasing data complexity and dimensionality in medical diagnostics. Efficient feature selection methods are critical for improving diagnostic accuracy, reducing computational costs, and enhancing the interpretability of predictive models. Particle Swarm Optimization (PSO), a widely used metaheuristic inspired by swarm intelligence, has shown considerable promise in feature selection tasks. However, conventional PSO often suffers from premature convergence and limited exploration capabilities, particularly in high-dimensional spaces. To overcome these limitations, this study proposes an enhanced PSO framework incorporating Orthogonal… More >

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