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  • Open Access

    ARTICLE

    Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

    Duc-Dam Nguyen1, Nguyen Viet Tiep2,*, Quynh-Anh Thi Bui1, Hiep Van Le1, Indra Prakash3, Romulus Costache4,5,6,7, Manish Pandey8,9, Binh Thai Pham1

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 467-500, 2025, DOI:10.32604/cmes.2024.056576 - 17 December 2024

    Abstract This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand, India, using advanced ensemble models that combined Radial Basis Function Networks (RBFN) with three ensemble learning techniques: DAGGING (DG), MULTIBOOST (MB), and ADABOOST (AB). This combination resulted in three distinct ensemble models: DG-RBFN, MB-RBFN, and AB-RBFN. Additionally, a traditional weighted method, Information Value (IV), and a benchmark machine learning (ML) model, Multilayer Perceptron Neural Network (MLP), were employed for comparison and validation. The models were developed using ten landslide conditioning factors, which included slope, aspect, elevation, curvature, land cover, geomorphology,… More >

  • Open Access

    REVIEW

    A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

    Vajratiya Vajrobol1, Geetika Jain Saxena2, Amit Pundir2, Sanjeev Singh1, Akshat Gaurav3, Savi Bansal4,5, Razaz Waheeb Attar6, Mosiur Rahman7, Brij B. Gupta7,8,9,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 49-90, 2025, DOI:10.32604/cmes.2024.056500 - 17 December 2024

    Abstract Mental health is a significant issue worldwide, and the utilization of technology to assist mental health has seen a growing trend. This aims to alleviate the workload on healthcare professionals and aid individuals. Numerous applications have been developed to support the challenges in intelligent healthcare systems. However, because mental health data is sensitive, privacy concerns have emerged. Federated learning has gotten some attention. This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems. It explores various dimensions of federated learning in mental health, such as More >

  • Open Access

    ARTICLE

    Insight Into the Separation-of-Variable Methods for the Closed-Form Solutions of Free Vibration of Rectangular Thin Plates

    Yufeng Xing*, Ye Yuan, Gen Li

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 329-355, 2025, DOI:10.32604/cmes.2024.056440 - 17 December 2024

    Abstract The separation-of-variable (SOV) methods, such as the improved SOV method, the variational SOV method, and the extended SOV method, have been proposed by the present authors and coworkers to obtain the closed-form analytical solutions for free vibration and eigenbuckling of rectangular plates and circular cylindrical shells. By taking the free vibration of rectangular thin plates as an example, this work presents the theoretical framework of the SOV methods in an instructive way, and the bisection–based solution procedures for a group of nonlinear eigenvalue equations. Besides, the explicit equations of nodal lines of the SOV methods More >

  • Open Access

    ARTICLE

    Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net (MU-Net) on Spine Magnetic Resonance Images

    Lakshmi S V V1, Shiloah Elizabeth Darmanayagam1,*, Sunil Retmin Raj Cyril2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 733-757, 2025, DOI:10.32604/cmes.2024.056424 - 17 December 2024

    Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >

  • Open Access

    ARTICLE

    Transforming Education with Photogrammetry: Creating Realistic 3D Objects for Augmented Reality Applications

    Kaviyaraj Ravichandran*, Uma Mohan*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 185-208, 2025, DOI:10.32604/cmes.2024.056387 - 17 December 2024

    Abstract Augmented reality (AR) is an emerging dynamic technology that effectively supports education across different levels. The increased use of mobile devices has an even greater impact. As the demand for AR applications in education continues to increase, educators actively seek innovative and immersive methods to engage students in learning. However, exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration. Concurrently, this surge in demand has prompted the identification of specific barriers, one of which is three-dimensional (3D) modeling. Creating 3D objects for augmented reality education applications can be challenging and… More > Graphic Abstract

    Transforming Education with Photogrammetry: Creating Realistic 3D Objects for Augmented Reality Applications

  • Open Access

    ARTICLE

    Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

    Suganya Athisayamani1, A. Robert Singh2, Gyanendra Prasad Joshi3, Woong Cho4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 155-183, 2025, DOI:10.32604/cmes.2024.056129 - 17 December 2024

    Abstract In radiology, magnetic resonance imaging (MRI) is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures. MRI is particularly effective for detecting soft tissue anomalies. Traditionally, radiologists manually interpret these images, which can be labor-intensive and time-consuming due to the vast amount of data. To address this challenge, machine learning, and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans. This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods. There are three… More >

  • Open Access

    ARTICLE

    A Novel Model for Describing Rail Weld Irregularities and Predicting Wheel-Rail Forces Using a Machine Learning Approach

    Linlin Sun1,2, Zihui Wang3, Shukun Cui1,2, Ziquan Yan1,2,*, Weiping Hu3, Qingchun Meng3

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 555-577, 2025, DOI:10.32604/cmes.2024.056023 - 17 December 2024

    Abstract Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed railways. They can cause significant wheel-rail dynamic interactions, leading to wheel-rail noise, component damage, and deterioration. Few researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry irregularities. However, the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric shape. In this study, novel theoretical models were developed for three categories of rail weld irregularities, based… More >

  • Open Access

    ARTICLE

    An SPH Framework for Earthquake-Induced Liquefaction Hazard Assessment of Geotechnical Structures

    Sourabh Mhaski*, G. V. Ramana

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 251-277, 2025, DOI:10.32604/cmes.2024.055963 - 17 December 2024

    Abstract Earthquake-induced soil liquefaction poses significant risks to the stability of geotechnical structures worldwide. An understanding of the liquefaction triggering, and the post-failure large deformation behaviour is essential for designing resilient infrastructure. The present study develops a Smoothed Particle Hydrodynamics (SPH) framework for earthquake-induced liquefaction hazard assessment of geotechnical structures. The coupled flow-deformation behaviour of soils subjected to cyclic loading is described using the PM4Sand model implemented in a three-phase, single-layer SPH framework. A staggered discretisation scheme based on the stress particle SPH approach is adopted to minimise numerical inaccuracies caused by zero-energy modes and tensile… More >

  • Open Access

    ARTICLE

    Artificial Circulation System Algorithm: A Novel Bio-Inspired Algorithm

    Nermin Özcan1,2,*, Semih Utku3, Tolga Berber4

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 635-663, 2025, DOI:10.32604/cmes.2024.055860 - 17 December 2024

    Abstract Metaheuristics are commonly used in various fields, including real-life problem-solving and engineering applications. The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm (ACSA). The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process. The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions, identified as classical benchmark functions. The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities. Furthermore, the paper evaluates ACSA in comparison to 64 metaheuristic… More >

  • Open Access

    ARTICLE

    Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

    A. Robert Singh1, Suganya Athisayamani2, Gyanendra Prasad Joshi3, Bhanu Shrestha4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 299-327, 2025, DOI:10.32604/cmes.2024.055599 - 17 December 2024

    Abstract Myocardial perfusion imaging (MPI), which uses single-photon emission computed tomography (SPECT), is a well-known estimating tool for medical diagnosis, employing the classification of images to show situations in coronary artery disease (CAD). The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks (CNNs). This paper uses a SPECT classification framework with three steps: 1) Image denoising, 2) Attenuation correction, and 3) Image classification. Image denoising is done by a U-Net architecture that ensures effective image denoising. Attenuation correction is implemented by a convolution neural network model that… More >

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