Nurfazira Mohamed Fadil¹, Kelvin Kang Wee Tang^1,2,*, Malavige Don Eranda Kanchana Gunathilaka³, Abdullah Hisam Omar^1,2, Muhammad Fahim Supian¹

Revue Internationale de Géomatique, Vol.34, pp. 569-585, 2025, DOI:10.32604/rig.2025.066200 - 06 August 2025

Abstract Single-beam echo sounders remain popular for seabed mapping because they possess an affordable cost and user-friendly design, delivering essential services for marine navigation, coastal management and resource conservation. High-amplitude echoes known as sea-spikes can severely harm depth measurement precision by disrupting readings, thus lowering the overall data accuracy. The manual processing method for outliers produces subjective results and demands excessive labor, which makes it difficult to accomplish trustworthy data processing. The study presents the Sea-Spike Filtering System (SSFS) as a semi-automatic system that utilizes mean absolute deviation (MAD) together with median filter (MF) techniques to… More > Graphic Abstract

Kelvin Kang Wee Tang^1,*, Muhammad Hafiz Mohd Yatim¹, Norhadija Darwin², Wan Anom Wan Aris¹, Sim Ching Yen³, Nurfazira Mohamed Fadil³

Revue Internationale de Géomatique, Vol.34, pp. 587-602, 2025, DOI:10.32604/rig.2025.065583 - 06 August 2025

Abstract The integration of Unmanned Aerial Vehicles (UAVs) and Uncrewed Surface Vehicles (USVs) has revolutionized topographic and bathymetric mapping, significantly enhancing the accuracy and efficiency of geospatial data acquisition processes. This innovative approach synergistically combines terrestrial data collected by UAVs with underwater data obtained through USVs, culminating in the creation of unified high-resolution Digital Elevation Models (DEMs) of the river basin region represents a vital step toward understanding the dynamic interactions between land and water bodies. Hence, the seamless Topo-Bathymetric Elevation Model offers a detailed perspective of the river system, supporting informed decision-making in addressing sediment… More >

Qaisara Yusriena Yusaini¹, Muhammad Abdul Hakim Muhamad^1,*, Raiz Razali^1,*, Rozaimi Che Hasan², Mohd Shahmy Mohd Said¹, Mohd Zainee Mohd Zainal^1,2, Ikhsan Nuradi³

Revue Internationale de Géomatique, Vol.34, pp. 535-552, 2025, DOI:10.32604/rig.2025.065284 - 06 August 2025

Abstract Sediment mapping is a crucial component of environmental science, particularly in the marine environment, where the analysis of seabed sediments is essential for various purposes, including marine resource management, habitat preservation, and infrastructure development. Sediment refers to the solid particles that are transported and deposited in different areas. Multibeam echosounders have revolutionized the field of seabed sediment mapping by providing unparalleled resolution and accuracy in seafloor surveys. This study aimed to produce sediment maps by implementing multi-frequency, e.g., 200, 400, 550, and 700 kHz multibeam data using a machine learning algorithm, e.g., Support Vector Machine… More >

Yazeed Alkharijah^1,2, Shehzad Khalid³, Syed Muhammad Usman^4,*, Amina Jameel³, Danish Hamid⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1113-1141, 2025, DOI:10.32604/cmes.2025.068726 - 31 July 2025

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 >

Zhiwei Chen^#, Runze Liu^#, Shitao Huang, Yangyang Guo^*, Yongjun Ren

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 37-81, 2025, DOI:10.32604/cmes.2025.067809 - 31 July 2025

Abstract The rapid advancement of artificial intelligence technology is driving transformative changes in medical diagnosis, treatment, and management systems through large-scale deep learning models—a process that brings both groundbreaking opportunities and multifaceted challenges. This study focuses on the medical and healthcare applications of large-scale deep learning architectures, conducting a comprehensive survey to categorize and analyze their diverse uses. The survey results reveal that current applications of large models in healthcare encompass medical data management, healthcare services, medical devices, and preventive medicine, among others. Concurrently, large models demonstrate significant advantages in the medical domain, especially in high-precision More >

Da-Chun Wu^*, Bing-Han Sie

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 535-580, 2025, DOI:10.32604/cmes.2025.067239 - 31 July 2025

Abstract Data hiding methods involve embedding secret messages into cover objects to enable covert communication in a way that is difficult to detect. In data hiding methods based on image interpolation, the image size is reduced and then enlarged through interpolation, followed by the embedding of secret data into the newly generated pixels. A general improving approach for embedding secret messages is proposed. The approach may be regarded a general model for enhancing the data embedding capacity of various existing image interpolation-based data hiding methods. This enhancement is achieved by expanding the range of pixel values… More >

Muhammad Usama¹, Arshad Aziz², Imtiaz Hassan², Shynar Akhmetzhanova³, Sultan Noman Qasem^4,*, Abdullah M. Albarrak⁴, Tawfik Al-Hadhrami⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1225-1248, 2025, DOI:10.32604/cmes.2025.067098 - 31 July 2025

Abstract The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges, particularly in safeguarding sensitive patient data and maintaining the integrity of medical services. As healthcare becomes more data-driven, cyberattacks targeting these systems continue to rise, necessitating the development of robust, domain-adapted Intrusion Detection Systems (IDS). However, current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare. To address this gap, this study introduces HCKDDCUP, a synthetic dataset modeled on the widely used KDDCUP benchmark, augmented with healthcare-relevant attributes such as patient data, treatments, and… More >

Nadhmi A. Gazem¹, Sultan Noman Qasem^2,3, Umair Naeem⁴, Shahid Latif⁵, Ibtehal Nafea⁶, Faisal Saeed⁷, Mujeeb Ur Rehman^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 407-427, 2025, DOI:10.32604/cmes.2025.066517 - 31 July 2025

Abstract Falls are a leading cause of injury and morbidity among older adults, especially those with Alzheimer’s disease (AD), who face increased risks due to cognitive decline, gait instability, and impaired spatial awareness. While wearable sensor-based fall detection systems offer promising solutions, their effectiveness is often hindered by domain shifts resulting from variations in sensor placement, sampling frequencies, and discrepancies in dataset distributions. To address these challenges, this paper proposes a novel unsupervised domain adaptation (UDA) framework specifically designed for cross-dataset fall detection in Alzheimer’s disease (AD) patients, utilizing advanced transfer learning to enhance generalizability. The… More >

Sidra Jubair¹, Jie Yang^1,2,*, Bilal Ali³, Walid Emam⁴, Yusra Tashkandy⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 511-534, 2025, DOI:10.32604/cmes.2025.066514 - 31 July 2025

Abstract Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning, particularly when class overlap significantly deteriorates classification performance. Traditional oversampling methods often generate synthetic samples without considering density variations, leading to redundant or misleading instances that exacerbate class overlap in high-density regions. To address these limitations, we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE, a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap. The originality of WGAN-VDE lies in its density-aware sample refinement, ensuring that synthetic samples are positioned in underrepresented More >

Fatma S. Alrayes¹, Syed Umar Amin^2,*, Nada Ali Hakami², Mohammed K. Alzaylaee³, Tariq Kashmeery⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 581-614, 2025, DOI:10.32604/cmes.2025.065188 - 31 July 2025

Abstract The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems (IDS) for enhanced network security. While conventional IDSs can be unsuitable for detecting different and emerging attacks, there is a demand for better techniques to improve detection reliability. This study introduces a new method, the Deep Adaptive Multi-Layer Attention Network (DAMLAN), to boost the result of intrusion detection on network data. Due to its multi-scale attention mechanisms and graph features, DAMLAN aims to address both known and unknown intrusions. The real-world NSL-KDD dataset, a popular choice among IDS researchers, is used to… More >