Darthy Rabecka V^1,*, Britto Pari J¹, Man-Fai Leung^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 827-853, 2025, DOI:10.32604/cmc.2025.064924 - 29 August 2025

Abstract Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks. This article offers an intriguing architecture for semantic, instance, and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks (Bi-FPN). When implemented in place of the EfficientNet-B5 backbone, EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications. By ensuring superior multi-scale feature fusion, Bi-FPN integration enhances the segmentation of complex objects across various urban environments. The design suggested is examined on rigorous datasets, encompassing Cityscapes, Common Objects in Context, KITTI Karlsruhe Institute of… More >

Yazeed Alkhrijah^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 >

Sanjay P. Pande¹, Sarika Khandelwal², Ganesh K. Yenurkar^3,*, Rakhi D. Wajgi³, Vincent O. Nyangaresi^4,5,*, Pratik R. Hajare⁶, Poonam T. Agarkar⁷

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5937-5975, 2025, DOI:10.32604/cmc.2025.064505 - 30 July 2025

Abstract Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems, particularly in dynamic and unstructured environments. While recent advancements in deep learning have significantly enhanced road scene classification, simultaneously achieving high accuracy, computational efficiency, and adaptability across diverse conditions continues to be difficult. To address these challenges, this study proposes HybridLSTM, a novel and efficient framework that integrates deep learning-based, object-based, and handcrafted feature extraction methods within a unified architecture. HybridLSTM is designed to classify four distinct road scene categories—crosswalk (CW), highway (HW), overpass/tunnel (OP/T), and parking (P)—by… More >

Weiwei Chen¹, Xiaodie Zhou², Huiyu Li¹, Yuchen Yang¹, Lu Lu¹, Chunyan Zhu¹, Rong Fang¹, Xiaoyuan Chu¹, Shuping Zhou^3,*, Qian Sun^1,*

Oncology Research, Vol.33, No.8, pp. 2123-2139, 2025, DOI:10.32604/or.2025.063484 - 18 July 2025

Abstract Background: Primary bone marrow diffuse large B-cell lymphoma (PBM-DLBCL) represents an uncommon yet clinically aggressive hematologic malignancy. Despite its significant clinical impact, this entity lacks standardized diagnostic criteria in current WHO classifications. Methods: We performed a retrospective analysis of 55 PBM-DLBCL cases from our institutional database and published literature (2001–2022) to characterize disease features and identify prognostic factors, with particular focus on assessing how different treatment regimens influence therapeutic efficacy and long-term outcomes. Results: The data suggested a potential link between international prognostic index (IPI) scores and poorer survival, albeit without conclusive statistical evidence (p = More >

Zhun Yu^1,2, Jie Wang^1,2, Guoping Xu^1,2,*

Oncology Research, Vol.33, No.8, pp. 2013-2035, 2025, DOI:10.32604/or.2025.061757 - 18 July 2025

Abstract Objectives: Triple-negative breast cancer (TNBC) presents a major treatment challenge due to its aggressive behavior. The dysfunction of the Golgi apparatus (GA) contributes to the development of various cancers. This study aimed to utilize GA-related genes (GARGs) to forecast the prognosis and immune profile of TNBC. Methods: The data were downloaded from The Cancer Genome Atlas (TCGA) database, including 175 TNBC and 99 healthy samples. The differentially expressed GARGs (DEGARGs) were analyzed using the TCGA biolinks package. The patients with TNBC were classified into two clusters utilizing the ConsensusClusterPlus package according to prognosis-related DEGARGs, followed by… More >

Min Ji^*, Shucheng Yang

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3075-3103, 2025, DOI:10.32604/cmc.2025.064166 - 03 July 2025

Abstract In agricultural production, crop images are commonly used for the classification and identification of various crops. However, several challenges arise, including low image clarity, elevated noise levels, low accuracy, and poor robustness of existing classification models. To address these issues, this research proposes an innovative crop image classification model named Lap-FEHRNet, which integrates a Laplacian Pyramid Super Resolution Network (LapSRN) with a feature enhancement high-resolution network based on attention mechanisms (FEHRNet). To mitigate noise interference, this research incorporates the LapSRN network, which utilizes a Laplacian pyramid structure to extract multi-level feature details from low-resolution images… More >

Mohammed Alshahrani¹, Mohammed Al-Jabbar^1,*, Ebrahim Mohammed Senan^2,3, Fatima Ali Amer jid Almahri⁴, Sultan Ahmed Almalki¹, Eman A. Alshari^3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3639-3675, 2025, DOI:10.32604/cmes.2025.064668 - 30 June 2025

Abstract Multiple Sclerosis (MS) poses significant health risks. Patients may face neurodegeneration, mobility issues, cognitive decline, and a reduced quality of life. Manual diagnosis by neurologists is prone to limitations, making AI-based classification crucial for early detection. Therefore, automated classification using Artificial Intelligence (AI) techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages. This study developed hybrid systems integrating XGBoost (eXtreme Gradient Boosting) with multi-CNN (Convolutional Neural Networks) features based on Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS) algorithms for early… More >

Mahmoud Bady¹, Fitrian Imaduddin^1,2, Iskander Tlili^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.6, pp. 1339-1352, 2025, DOI:10.32604/fdmp.2025.065295 - 30 June 2025

Abstract This study explores the bioconvective behavior of a Reiner-Rivlin nanofluid, accounting for spatially varying thermal properties. The flow is considered over a porous, stretching surface with mass suction effects incorporated into the transport analysis. The Reiner-Rivlin nanofluid model includes variable thermal conductivity, mass diffusivity, and motile microorganism density to accurately reflect realistic biological conditions. Radiative heat transfer and internal heat generation are considered in the thermal energy equation, while the Cattaneo-Christov theory is employed to model non-Fourier heat and mass fluxes. The governing equations are non-dimensionalized to reduce complexity, and a numerical solution is obtained More >

Ya-Jie Sun¹, Li-Wei Qiao¹, Sai Ji^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1769-1785, 2025, DOI:10.32604/cmc.2025.062950 - 09 June 2025

Abstract Vehicle re-identification involves matching images of vehicles across varying camera views. The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images, which increases the complexity of re-identification tasks. To tackle these challenges, this study proposes AG-GCN (Attention-Guided Graph Convolutional Network), a novel framework integrating several pivotal components. Initially, AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically, thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones. Moreover, AG-GCN adopts More >

Tengyue Li^1,*, Shuangli Song¹, Jiaming Zhou², Simon Fong^2,3, Geyue Li⁴, Qun Song³, Sabah Mohammed⁵, Weiwei Lin⁶, Juntao Gao⁷

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1711-1730, 2025, DOI:10.32604/cmc.2025.062489 - 09 June 2025

Abstract Hematoxylin and Eosin (H&E) images, popularly used in the field of digital pathology, often pose challenges due to their limited color richness, hindering the differentiation of subtle cell features crucial for accurate classification. Enhancing the visibility of these elusive cell features helps train robust deep-learning models. However, the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community. To address this challenge, we introduce Salient Features Guided Augmentation (SFGA), an approach that strategically integrates machine learning and image processing. SFGA utilizes machine learning algorithms to identify… More >