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  • Open Access

    ARTICLE

    Track Defects Recognition Based on Axle-Box Vibration Acceleration and Deep-Learning Techniques

    Xianxian Yin1, Shimin Yin1, Yiming Bu2, Xiukun Wei3,*

    Structural Durability & Health Monitoring, Vol.18, No.5, pp. 623-640, 2024, DOI:10.32604/sdhm.2024.050195 - 19 July 2024

    Abstract As an important component of load transfer, various fatigue damages occur in the track as the rail service life and train traffic increase gradually, such as rail corrugation, rail joint damage, uneven thermite welds, rail squats fastener defects, etc. Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit. In this paper, an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network, and the coexistence of the above-mentioned typical track defects in the track system… More >

  • Open Access

    ARTICLE

    UNet Based on Multi-Object Segmentation and Convolution Neural Network for Object Recognition

    Nouf Abdullah Almujally1, Bisma Riaz Chughtai2, Naif Al Mudawi3, Abdulwahab Alazeb3, Asaad Algarni4, Hamdan A. Alzahrani5, Jeongmin Park6,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1563-1580, 2024, DOI:10.32604/cmc.2024.049333 - 18 July 2024

    Abstract The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes. Various technologies, such as augmented reality-driven scene integration, robotic navigation, autonomous driving, and guided tour systems, heavily rely on this type of scene comprehension. This paper presents a novel segmentation approach based on the UNet network model, aimed at recognizing multiple objects within an image. The methodology begins with the acquisition and preprocessing of the image, followed by segmentation using the fine-tuned UNet architecture. Afterward, we use an annotation tool to accurately label… More >

  • Open Access

    ARTICLE

    Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

    Muhammad Sohail Irshad1,2,*, Tehreem Masood1,2, Arfan Jaffar1,2, Muhammad Rashid3, Sheeraz Akram1,2,4,*, Abeer Aljohani5

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4805-4824, 2024, DOI:10.32604/cmc.2024.050931 - 20 June 2024

    Abstract The application of deep learning techniques in the medical field, specifically for Atrial Fibrillation (AFib) detection through Electrocardiogram (ECG) signals, has witnessed significant interest. Accurate and timely diagnosis increases the patient’s chances of recovery. However, issues like overfitting and inconsistent accuracy across datasets remain challenges. In a quest to address these challenges, a study presents two prominent deep learning architectures, ResNet-50 and DenseNet-121, to evaluate their effectiveness in AFib detection. The aim was to create a robust detection mechanism that consistently performs well. Metrics such as loss, accuracy, precision, sensitivity, and Area Under the Curve… More >

  • Open Access

    ARTICLE

    Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

    Yi-Chun Lai1, Shu-Yin Chiang2, Yao-Chiang Kan3, Hsueh-Chun Lin4,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3783-3803, 2024, DOI:10.32604/cmc.2024.050376 - 20 June 2024

    Abstract Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned… More > Graphic Abstract

    Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

  • Open Access

    ARTICLE

    Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

    M. Adimoolam1, K. Maithili2, N. M. Balamurugan3, R. Rajkumar4, S. Leelavathy5, Raju Kannadasan6, Mohd Anul Haq7,*, Ilyas Khan8, ElSayed M. Tag El Din9, Arfat Ahmad Khan10

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 33-55, 2024, DOI:10.32604/iasc.2024.039009 - 29 March 2024

    Abstract At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural… More >

  • Open Access

    ARTICLE

    Credit Card Fraud Detection Using Improved Deep Learning Models

    Sumaya S. Sulaiman1,2,*, Ibraheem Nadher3, Sarab M. Hameed2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051 - 30 January 2024

    Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network… More >

  • Open Access

    ARTICLE

    Multi-Level Parallel Network for Brain Tumor Segmentation

    Juhong Tie, Hui Peng*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 741-757, 2024, DOI:10.32604/cmes.2023.043353 - 30 December 2023

    Abstract Accurate automatic segmentation of gliomas in various sub-regions, including peritumoral edema, necrotic core, and enhancing and non-enhancing tumor core from 3D multimodal MRI images, is challenging because of its highly heterogeneous appearance and shape. Deep convolution neural networks (CNNs) have recently improved glioma segmentation performance. However, extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution, resulting in the loss of accurate spatial and object parts information, especially information on the small sub-region tumors, affecting segmentation performance. Hence, this paper proposes a novel multi-level parallel network comprising three different… More >

  • Open Access

    ARTICLE

    CNN-Based Multi-Object Tracking Networks with Position Correction and IMM in Intelligent Transportation System

    Chengjuan Ren1, Guangnan Zhang2,*, Dongwon Jeong3, Lele Hao4

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.39, No.4, pp. 1-12, 2023, DOI:10.23967/j.rimni.2023.11.001

    Abstract In multi-object tracking of Intelligent Transportation System, there are objects of different sizes in images or videos, especially pedestrian and traffic lights with low resolution in the image. Meantime, objects are subject to occlusion or loss in object tracking. All of the above-mentioned situations may lead to unsatisfactory multi-object tracking results. Attracted by the effect of deep convolution neural networks, the paper proposes a multi-object tracking network, CNN-Based Multi-Object Tracking Networks with Position Correction and IMM (CNN_PC_IMM) to solve those problems. Our proposed method consists of object detection module and object tracking module. Compared to… More >

  • Open Access

    ARTICLE

    Detecting Android Botnet Applications Using Convolution Neural Network

    Mamona Arshad1, Ahmad Karim1, Salman Naseer2, Shafiq Ahmad3, Mejdal Alqahtani3, Akber Abid Gardezi4, Muhammad Shafiq5,*, Jin-Ghoo Choi5

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2123-2135, 2023, DOI:10.32604/cmc.2022.028680 - 29 November 2023

    Abstract The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat… More >

  • Open Access

    ARTICLE

    Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

    Premanand S., Sathiya Narayanan*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 25-45, 2023, DOI:10.32604/cmc.2023.042590 - 31 October 2023

    Abstract Machine Learning (ML) and Deep Learning (DL) technologies are revolutionizing the medical domain, especially with Electrocardiogram (ECG), by providing new tools and techniques for diagnosing, treating, and preventing diseases. However, DL architectures are computationally more demanding. In recent years, researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches, say for example, combining the convolutional layer blocks of Convolution Neural Networks (CNNs) into ML algorithms such as Extreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) resulting in CNN-XGBoost and CNN-KNN, respectively. However, these approaches are homogenous in the… More >

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