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Emerging Machine Learning Methods and Applications

Submission Deadline: 30 June 2025 (closed) View: 2752 Submit to Journal

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

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Open Access

ARTICLE

YOLOv10-HQGNN: A Hybrid Quantum Graph Learning Framework for Real-Time Faulty Insulator Detection
Nghia Dinh, Vinh Truong Hoang, Viet-Tuan Le, Kiet Tran-Trung, Ha Duong TTi Hong, Bay Nguyen Van, Hau Nguyen Trung, Tien Ho Huong, Kittikhun Meethongjan
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069587
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
Abstract Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators, which serve both mechanical and electrical functions. Even a single defective insulator can lead to equipment breakdown, costly service interruptions, and increased maintenance demands. While unmanned aerial vehicles (UAVs) enable rapid and cost-effective collection of high-resolution imagery, accurate defect identification remains challenging due to cluttered backgrounds, variable lighting, and the diverse appearance of faults. To address these issues, we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced More >

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ARTICLE

Individual Software Expertise Formalization and Assessment from Project Management Tool Databases
Traian-Radu Ploscă, Alexandru-Mihai Pescaru, Bianca-Valeria Rus, Daniel-Ioan Curiac
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069707
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
Abstract Objective expertise evaluation of individuals, as a prerequisite stage for team formation, has been a long-term desideratum in large software development companies. With the rapid advancements in machine learning methods, based on reliable existing data stored in project management tools’ datasets, automating this evaluation process becomes a natural step forward. In this context, our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems. For this, we mathematically formalize two categories of expertise: technology-specific expertise, which denotes the skills required for a particular technology, and general expertise, which encapsulates overall knowledge More >

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ARTICLE

Graph Attention Networks for Skin Lesion Classification with CNN-Driven Node Features
Ghadah Naif Alwakid, Samabia Tehsin, Mamoona Humayun, Asad Farooq, Ibrahim Alrashdi, Amjad Alsirhani
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >

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ARTICLE

An Improved Forest Fire Detection Model Using Audio Classification and Machine Learning
Kemahyanto Exaudi, Deris Stiawan, Bhakti Yudho Suprapto, Hanif Fakhrurroja, Mohd. Yazid Idris, Tami A. Alghamdi, Rahmat Budiarto
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.069377
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
Abstract Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by… More >

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ARTICLE

A Comparative Study of Data Representation Techniques for Deep Learning-Based Classification of Promoter and Histone-Associated DNA Regions
Sarab Almuhaideb, Najwa Altwaijry, Isra Al-Turaiki, Ahmad Raza Khan, Hamza Ali Rizvi
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3095-3128, 2025, DOI:10.32604/cmc.2025.067390
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
Abstract Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid (DNA) sequence, making DNA sequence classification an integral step in performing bioinformatics analysis, where large biomedical datasets are transformed into valuable knowledge. Existing methods rely on a feature extraction step and suffer from high computational time requirements. In contrast, newer approaches leveraging deep learning have shown significant promise in enhancing accuracy and efficiency. In this paper, we investigate the performance of various deep learning architectures: Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (CNN-LSTM), CNN-Bidirectional Long Short-Term Memory (CNN-BiLSTM), Residual Network (ResNet), and… More >

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ARTICLE

Slice-Based 6G Network with Enhanced Manta Ray Deep Reinforcement Learning-Driven Proactive and Robust Resource Management
Venkata Satya Suresh kumar Kondeti, Raghavendra Kulkarni, Binu Sudhakaran Pillai, Surendran Rajendran
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4973-4995, 2025, DOI:10.32604/cmc.2025.066428
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
Abstract Next-generation 6G networks seek to provide ultra-reliable and low-latency communications, necessitating network designs that are intelligent and adaptable. Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures. Nonetheless, sustaining elevated Quality of Service (QoS) in dynamic, resource-limited systems poses significant hurdles. This study introduces an innovative packet-based proactive end-to-end (ETE) resource management system that facilitates network slicing with improved resilience and proactivity. To get around the drawbacks of conventional reactive systems, we develop a cost-efficient slice provisioning architecture that takes into account limits on radio, processing, and… More >

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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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Emerging Machine Learning Methods and Applications

Guest Editors

Summary

Keywords

Published Papers

View

139

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25

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1

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284

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90

View

1019

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539

View

897

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369

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1496

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726

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2048

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1431

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1047

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384

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