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Machine Learning and Deep Learning-Based Pattern Recognition

Submission Deadline: 15 February 2026 View: 4090 Submit to Special Issue

Guest Editors

Prof. Dr. Jungpil Shin

Email: jpshin@u-aizu.ac.jp

Affiliation: School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, JAPAN

Homepage:

Research Interests: Pattern recognition, image processing, computer vision, machine learning, human-computer interaction, non-touch interfaces, human gesture recognition, automatic control, Parkinson’s disease diagnosis, ADHD diagnosis, user authentication, machine intelligence, bioinformatics, as well as handwriting analysis, recognition, and synthesis 

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Prof. Dr. Yong Seok Hwang

Email:thestone@kw.ac.kr

Affiliation: Department of Electronic Engineering, Kwangwoon University, Seoul 01897, SOUTH KOREA

Homepage:

Research Interests: Machine learning based volumetric Meta holographic optical element (VMHOE), Deep learning based meta holo micro display (OLEDoS/LCoS) architecture for augmented reality (AR) devices. Machin learning based hologram data processing, machine learning, Deep learning, human–computer interaction, non-touch interfaces, human gesture recognition, ADHD and autism diagnosis, and digital therapeutics

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Summary

In today’s digital era, patterns are omnipresent, shaping many aspects of our lives. These patterns can be physically observed or computationally identified through sophisticated algorithms. In the digital realm, patterns are often represented as vectors or matrices of feature values. With the rapid evolution of artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have emerged as powerful tools for analyzing and processing these features.


Machine learning, a core branch of AI, empowers computers to make decisions with minimal human intervention by leveraging pattern data. Deep learning, a subfield of ML, has gained significant attention for its ability to handle complex, high-dimensional data. The use of ML and DL models for extracting and analyzing meaningful features from text, images, videos, or sensor data is the foundation of pattern recognition (PR).


Pattern recognition is a critical enabler for a wide range of applications, including computer vision, sensor data analysis, natural language processing, speech recognition, robotics, bioinformatics, and beyond. This Special Issue aims to showcase cutting-edge research and innovative methodologies that advance the field of PR through ML and DL approaches.


We invite high-quality original research articles and comprehensive reviews that contribute to both theoretical developments and practical applications in the domain of pattern recognition. Topics of interest include, but are not limited to:

· Image processing, segmentation, and recognition

· Computer vision

· Speech recognition

· Automated target recognition

· Character recognition

· Gesture and human activity recognition

· Industrial inspection

· Medical diagnosis and health informatics

· Biosignal processing and bioinformatics

· Remote sensing

· Applications in healthcare

· Integration of ML and DL with the Internet of Things (IoT)

· Analysis of large datasets

· Current state-of-the-art and future trends in ML and DL for pattern recognition


This Special Issue seeks to provide a platform for researchers and practitioners to present innovative solutions, share insights, and discuss emerging trends in the ever-evolving field of pattern recognition.


Published Papers

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  • Open Access

    ARTICLE

    Side-Scan Sonar Image Synthesis Based on CycleGAN with 3D Models and Shadow Integration

    Byeongjun Kim, Seung-Hun Lee, Won-Du Chang
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1237-1252, 2025, DOI:10.32604/cmes.2025.073530
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Side-scan sonar (SSS) is essential for acquiring high-resolution seafloor images over large areas, facilitating the identification of subsea objects. However, military security restrictions and the scarcity of subsea targets limit the availability of SSS data, posing challenges for Automatic Target Recognition (ATR) research. This paper presents an approach that uses Cycle-Consistent Generative Adversarial Networks (CycleGAN) to augment SSS images of key subsea objects, such as shipwrecks, aircraft, and drowning victims. The process begins by constructing 3D models to generate rendered images with realistic shadows from multiple angles. To enhance image quality, a shadow extractor and More >

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    173

  • Open Access

    ARTICLE

    Graph Neural Network-Assisted Lion Swarm Optimization for Traffic Congestion Prediction in Intelligent Urban Mobility Systems

    Meshari D. Alanazi, Gehan Elsayed, Turki M. Alanazi, Anis Sahbani, Amr Yousef
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2277-2309, 2025, DOI:10.32604/cmes.2025.070726
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Traffic congestion plays a significant role in intelligent transportation systems (ITS) due to rapid urbanization and increased vehicle concentration. The congestion is dependent on multiple factors, such as limited road occupancy and vehicle density. Therefore, the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment. Conventional prediction systems face difficulties in identifying highly congested areas, which leads to reduced prediction accuracy. The problem is addressed by integrating Graph Neural Networks (GNN) with the Lion Swarm Optimization (LSO) framework to tackle the congestion prediction problem. Initially, the traffic information is… More >

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    332

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    101

  • Open Access

    ARTICLE

    Advancing Radiological Dermatology with an Optimized Ensemble Deep Learning Model for Skin Lesion Classification

    Adeel Akram, Tallha Akram, Ghada Atteia, Ayman Qahmash, Sultan Alanazi, Faisal Mohammad Alotaibi
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2311-2337, 2025, DOI:10.32604/cmes.2025.069697
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics, particularly in dermatology. This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks (DNNs) with transfer learning, a customized DNN, and an optimized self-learning binary differential evolution (SLBDE) algorithm for feature selection and fusion. Leveraging computational techniques alongside medical imaging modalities, the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness. The methodology is evaluated on benchmark datasets, including ISIC 2017 and the Argentina Skin Lesion dataset, demonstrating superior accuracy, precision, and F1-score… More >

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    401

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    114

  • Open Access

    ARTICLE

    Quantum Genetic Algorithm Based Ensemble Learning for Detection of Atrial Fibrillation Using ECG Signals

    Yazeed Alkhrijah, Marwa Fahim, Syed Muhammad Usman, Qasim Mehmood, Shehzad Khalid, Mohamad A. Alawad, Haya Aldossary
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2339-2355, 2025, DOI:10.32604/cmes.2025.071512
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Atrial Fibrillation (AF) is a cardiac disorder characterized by irregular heart rhythms, typically diagnosed using Electrocardiogram (ECG) signals. In remote regions with limited healthcare personnel, automated AF detection is extremely important. Although recent studies have explored various machine learning and deep learning approaches, challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification. In this study, we propose a novel framework that integrates robust preprocessing, comprehensive feature extraction, and an ensemble classification strategy. In the first step, ECG signals are divided into equal-sized segments using a… More >

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    400

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    154

  • Open Access

    ARTICLE

    Lightweight Residual Multi-Head Convolution with Channel Attention (ResMHCNN) for End-to-End Classification of Medical Images

    Sudhakar Tummala, Sajjad Hussain Chauhdary, Vikash Singh, Roshan Kumar, Seifedine Kadry, Jungeun Kim
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3585-3605, 2025, DOI:10.32604/cmes.2025.069731
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things (IoMT). Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer. Therefore, this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention (ResMHCNN) blocks to classify medical images. We introduced three novel lightweight deep learning models (BT-Net, LCC-Net, and BC-Net) utilizing the ResMHCNN block as their backbone. These models were cross-validated and tested on three publicly available medical image datasets:… More >

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    836

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    396

  • Open Access

    ARTICLE

    Displacement Feature Mapping for Vehicle License Plate Recognition Influenced by Haze Weather

    Mohammed Albekairi, Radhia Khdhir, Amina Magdich, Somia Asklany, Ghulam Abbas, Amr Yousef
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3607-3644, 2025, DOI:10.32604/cmes.2025.069681
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation, which lead to pixel displacements. This article introduces a Displacement Region Recognition Method (DR2M) to address such a problem. This method operates on displaced features compared to the training input observed throughout definite time frames. The technique focuses on detecting features that remain relatively stable under haze, using a frame-based analysis to isolate edges minimally affected by visual noise. The edge detection failures are identified using a bilateral neural network through displaced feature training. The training converges bilaterally… More >

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    1121

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    420

  • Open Access

    ARTICLE

    Augmented Deep-Feature-Based Ear Recognition Using Increased Discriminatory Soft Biometrics

    Emad Sami Jaha
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3645-3678, 2025, DOI:10.32604/cmes.2025.068681
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract The human ear has been substantiated as a viable nonintrusive biometric modality for identification or verification. Among many feasible techniques for ear biometric recognition, convolutional neural network (CNN) models have recently offered high-performance and reliable systems. However, their performance can still be further improved using the capabilities of soft biometrics, a research question yet to be investigated. This research aims to augment the traditional CNN-based ear recognition performance by adding increased discriminatory ear soft biometric traits. It proposes a novel framework of augmented ear identification/verification using a group of discriminative categorical soft biometrics and deriving… More >

    Graphic Abstract

    Augmented Deep-Feature-Based Ear Recognition Using Increased Discriminatory Soft Biometrics

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    423

  • Open Access

    ARTICLE

    Active Learning-Enhanced Deep Ensemble Framework for Human Activity Recognition Using Spatio-Textural Features

    Lakshmi Alekhya Jandhyam, Ragupathy Rengaswamy, Narayana Satyala
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3679-3714, 2025, DOI:10.32604/cmes.2025.068941
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Human Activity Recognition (HAR) has become increasingly critical in civic surveillance, medical care monitoring, and institutional protection. Current deep learning-based approaches often suffer from excessive computational complexity, limited generalizability under varying conditions, and compromised real-time performance. To counter these, this paper introduces an Active Learning-aided Heuristic Deep Spatio-Textural Ensemble Learning (ALH-DSEL) framework. The model initially identifies keyframes from the surveillance videos with a Multi-Constraint Active Learning (MCAL) approach, with features extracted from DenseNet121. The frames are then segmented employing an optimized Fuzzy C-Means clustering algorithm with Firefly to identify areas of interest. A deep ensemble More >

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    430

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    255

  • Open Access

    ARTICLE

    A Hybrid Approach for Heavily Occluded Face Detection Using Histogram of Oriented Gradients and Deep Learning Models

    Thaer Thaher, Muhammed Saffarini, Majdi Mafarja, Abdulaziz Alashbi, Abdul Hakim Mohamed, Ayman A. El-Saleh
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2359-2394, 2025, DOI:10.32604/cmes.2025.065388
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Face detection is a critical component in modern security, surveillance, and human-computer interaction systems, with widespread applications in smartphones, biometric access control, and public monitoring. However, detecting faces with high levels of occlusion, such as those covered by masks, veils, or scarves, remains a significant challenge, as traditional models often fail to generalize under such conditions. This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients (HOG) and Canny edge detection with modern deep learning models. The goal is to improve face detection accuracy under occlusions. The… More >

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    1754

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    1511

  • Open Access

    ARTICLE

    Fusing Geometric and Temporal Deep Features for High-Precision Arabic Sign Language Recognition

    Yazeed Alkhrijah, Shehzad Khalid, Syed Muhammad Usman, Amina Jameel, Danish Hamid
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1113-1141, 2025, DOI:10.32604/cmes.2025.068726
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Arabic Sign Language (ArSL) recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing (DHH) community. Researchers have proposed multiple methods for automated recognition of ArSL; however, these methods face multiple challenges that include high gesture variability, occlusions, limited signer diversity, and the scarcity of large annotated datasets. Existing methods, often relying solely on either skeletal data or video-based features, struggle with generalization and robustness, especially in dynamic and real-world conditions. This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint… More >

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    1197

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    683

  • Open Access

    ARTICLE

    A Novel Attention-Based Parallel Blocks Deep Architecture for Human Action Recognition

    Yasir Khan Jadoon, Yasir Noman Khalid, Muhammad Attique Khan, Jungpil Shin, Fatimah Alhayan, Hee-Chan Cho, Byoungchol Chang
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1143-1164, 2025, DOI:10.32604/cmes.2025.066984
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Real-time surveillance is attributed to recognizing the variety of actions performed by humans. Human Action Recognition (HAR) is a technique that recognizes human actions from a video stream. A range of variations in human actions makes it difficult to recognize with considerable accuracy. This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames. The input is provided to the model in the form of video frames. The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes. Features are extracted from each… More >

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    735

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    628

  • Open Access

    ARTICLE

    Video-Based Human Activity Recognition Using Hybrid Deep Learning Model

    Jungpil Shin, Md. Al Mehedi Hasan, Md. Maniruzzaman, Satoshi Nishimura, Sultan Alfarhood
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3615-3638, 2025, DOI:10.32604/cmes.2025.064588
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Activity recognition is a challenging topic in the field of computer vision that has various applications, including surveillance systems, industrial automation, and human-computer interaction. Today, the demand for automation has greatly increased across industries worldwide. Real-time detection requires edge devices with limited computational time. This study proposes a novel hybrid deep learning system for human activity recognition (HAR), aiming to enhance the recognition accuracy and reduce the computational time. The proposed system combines a pre-trained image classification model with a sequence analysis model. First, the dataset was divided into a training set (70%), validation set… More >

    Graphic Abstract

    Video-Based Human Activity Recognition Using Hybrid Deep Learning Model

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    1185

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    529

  • Open Access

    ARTICLE

    BioSkinNet: A Bio-Inspired Feature-Selection Framework for Skin Lesion Classification

    Tallha Akram, Fahdah Almarshad, Anas Alsuhaibani, Syed Rameez Naqvi
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2333-2359, 2025, DOI:10.32604/cmes.2025.064079
    （This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Melanoma is the deadliest form of skin cancer, with an increasing incidence over recent years. Over the past decade, researchers have recognized the potential of computer vision algorithms to aid in the early diagnosis of melanoma. As a result, a number of works have been dedicated to developing efficient machine learning models for its accurate classification; still, there remains a large window for improvement necessitating further research efforts. Limitations of the existing methods include lower accuracy and high computational complexity, which may be addressed by identifying and selecting the most discriminative features to improve classification… More >

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    1044

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    514

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