Ruida Liu, Dan Wang^*, Jiaming Chen, Meng Xu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074554 - 09 April 2026

Abstract Accurate prediction of shoulder joint angles based on surface electromyography (sEMG) signals is critical in human–machine interaction and rehabilitation engineering. However, due to the shoulder joint’s complex degrees of freedom, dynamically varying muscle coordination patterns, and the susceptibility of sEMG signals to cross-talk and noise interference, achieving high-precision prediction remains challenging. In this study, LASENet (BiLSTM–Attention–SE Network) is proposed as an end-to-end deep learning framework that integrates a bidirectional long short-term memory network (BiLSTM), a multi-head self-attention (MHSA) mechanism, and a squeeze-and-excitation (SE) block to predict shoulder joint angles across three degrees of freedom directly More >

Swetha K¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077293 - 30 March 2026

Abstract Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes, self-occlusions, and the complete loss of important structural and textural components, depressing recognition accuracy and visual fidelity. This paper introduces a new deep generative framework, Modified Multi-Scale Fused CycleGAN (MMF-CycleGAN), for robust and photo-realistic profile-to-frontal face synthesis. The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder. The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions, leading to sharper textures and improved More > Graphic Abstract

Samia Allaoua Chelloug^1,*, Ahmed A. Abd El-Latif^2,3,*, Samah AlShathri¹, Mohamed Hammad^2,4

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074485 - 12 March 2026

Abstract Scene recognition is a critical component of computer vision, powering applications from autonomous vehicles to surveillance systems. However, its development is often constrained by a heavy reliance on large, expensively annotated datasets. This research presents a novel, efficient approach that leverages multi-model transfer learning from pre-trained deep neural networks—specifically DenseNet201 and Visual Geometry Group (VGG)—to overcome this limitation. Our method significantly reduces dependency on vast labeled data while achieving high accuracy. Evaluated on the Aerial Image Dataset (AID) dataset, the model attained a validation accuracy of 93.6% with a loss of 0.35, demonstrating robust performance More >

Seerat Singla¹, Gunjan Shandilya¹, Ayman Altameem², Ruby Pant³, Ajay Kumar⁴, Ateeq Ur Rehman^5,*, Ahmad Almogren^6,*

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 4021-4057, 2025, DOI:10.32604/phyton.2025.073354 - 29 December 2025

Abstract Turmeric Leaf diseases pose a major threat to turmeric cultivation, causing significant yield loss and economic impact. Early and accurate identification of these diseases is essential for effective crop management and timely intervention. This study proposes DenseSwinGNNNet, a hybrid deep learning framework that integrates DenseNet-121, the Swin Transformer, and a Graph Neural Network (GNN) to enhance the classification of turmeric leaf conditions. DenseNet121 extracts discriminative low-level features, the Swin Transformer captures long-range contextual relationships through hierarchical self-attention, and the GNN models inter-feature dependencies to refine the final representation. A total of 4361 images from the… More >

Abdu Salam¹, Mohammad Abrar², Raja Waseem Anwer³, Farhan Amin^4,*, Faizan Ullah⁵, Isabel de la Torre^6,*, Gerardo Mendez Mezquita⁷, Henry Fabian Gongora⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2457-2479, 2025, DOI:10.32604/cmes.2025.072765 - 26 November 2025

Abstract Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations. Due to the increase in precision image-based diagnostic tools, driven by advancements in artificial intelligence (AI) and deep learning, there has been potential to improve diagnostic accuracy, especially with Magnetic Resonance Imaging (MRI). However, traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation. Thus, our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model. The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification. The proposed model… More >

Zhenxiang He^*, Rulin Wu, Xinyuan Wang

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1631-1651, 2025, DOI:10.32604/cmc.2025.067252 - 29 August 2025

Abstract With the continuous advancement of steganographic techniques, the task of image steganalysis has become increasingly challenging, posing significant obstacles to the fields of information security and digital forensics. Although existing deep learning methods have achieved certain progress in steganography detection, they still encounter several difficulties in real-world applications. Specifically, current methods often struggle to accurately focus on steganography sensitive regions, leading to limited detection accuracy. Moreover, feature information is frequently lost during transmission, which further reduces the model’s generalization ability. These issues not only compromise the reliability of steganography detection but also hinder its applicability… 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 >

Smita Khairnar^1,2, Shilpa Gite^1,3,*, Biswajeet Pradhan^4,*, Sudeep D. Thepade^2,5, Abdullah Alamri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3677-3707, 2025, DOI:10.32604/cmes.2025.058855 - 30 June 2025

Abstract Face liveness detection is essential for securing biometric authentication systems against spoofing attacks, including printed photos, replay videos, and 3D masks. This study systematically evaluates pre-trained CNN models— DenseNet201, VGG16, InceptionV3, ResNet50, VGG19, MobileNetV2, Xception, and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance. The models were trained and tested on NUAA and Replay-Attack datasets, with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability. Performance was evaluated using accuracy, precision, recall, FAR, FRR, HTER, and specialized spoof detection metrics (APCER, NPCER, ACER). Fine-tuning significantly improved detection accuracy, with DenseNet201 achieving the highest… More > Graphic Abstract

Sameera V Mohd Sagheer¹, Orwel P V², P M Ameer³, Amal BaQais⁴, Shaeen Kalathil^5,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1329-1349, 2025, DOI:10.32604/cmc.2025.063961 - 09 June 2025

Abstract The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods. Conventional approaches rely on visual examination by experts to detect infected palm leaves, which is time intensive and susceptible to mistakes. This study proposes an automated leaf classification system that uses deep learning algorithms to identify and categorize diseases in date palm tree leaves with high precision and dependability. The system leverages pretrained convolutional neural network architectures (InceptionV3, DenseNet, and MobileNet) to extract and examine leaf characteristics for classification purposes. A publicly accessible dataset comprising multiple… More >

Tajinder Kumar¹, Sarbjit Kaur², Purushottam Sharma^3,*, Ankita Chhikara⁴, Xiaochun Cheng^5,*, Sachin Lalar⁶, Vikram Verma⁷

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5219-5234, 2025, DOI:10.32604/cmc.2025.062010 - 19 May 2025

Abstract During its growth stage, the plant is exposed to various diseases. Detection and early detection of crop diseases is a major challenge in the horticulture industry. Crop infections can harm total crop yield and reduce farmers’ income if not identified early. Today’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant leaves. This is an excellent use case for Community Assessment and Treatment Services (CATS) due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of pathologists.… More >