Amit Pimpalkar^1,*, Kapil N. Vhatkar², Rachna K. Somkunwar³, Shweta Koparde⁴, Dalia H. Elkamchouchi⁵, Ateeq Ur Rehman^6,*, Pooja Verma⁷, Salil Bharany⁸

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084384 - 23 July 2026

Abstract Plants are fundamental to global food security; however, plant diseases significantly reduce agricultural productivity, making early and accurate detection essential. Traditional inspection approaches rely heavily on manual observation, which is labor-intensive, subjective, difficult to scale, and susceptible to human error. In contrast, artificial intelligence (AI) combined with computer vision (CV) offers an effective solution for early-stage disease detection, minimizing yield losses while overcoming the limitations of manual monitoring systems. In this study, a novel deep learning architecture, the Swin Transformer with Harmonic Densely Connected Network (STHarDNet), is proposed. The framework integrates a Swin Transformer (ST)… More >

Muhammad Asim^1,#, Muhammad Amin Shahid^1,#, Abdullah Khan¹, Muhammad Ishaq¹, Jawad Khan^2,*, Syed Qamrun Nisa³, Muhammad Amir Khan⁴, Ines Hilali Jaghdam⁵

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083596 - 23 July 2026

Abstract Indoor object detection presents unique challenges such as occlusions, varying lighting conditions, and cluttered environments. While several object detection frameworks, including RetinaNet, Faster R-CNN, SSD, and EfficientDet, have been proposed, they often suffer from high computational cost, reduced inference speed, and limited accuracy in terms of mean Average Precision (mAP), particularly in real-time scenarios. In this study, lightweight YOLO variants, namely YOLOv7, YOLOv8s, YOLOv9s, and a fine-tuned YOLOv9s which considers the optimized training strategy based on albumentations. All the models are evaluated for indoor object detection using the RGB TUT Indoor dataset. The models are… More >

Suchang Yang, Hongtao Yu^*, Ruiyang Huang, Huansha Wang, Ran Li, Junzheng Li

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082651 - 23 July 2026

Abstract Modeling dynamic graphs in continuous time is critical for applications such as user behavior prediction and recommendation systems. These models can effectively capture fine-grained and long-term temporal dependencies. However, existing approaches often suffer from high computational costs and optimization difficulties, especially when handling time-sorted neighborhood sequences over long horizons. In this work, we propose DyG-Hyena, a novel continuous-time dynamic graph learning framework that combines conditional variational autoencoder (CVAE)-assisted temporal modeling with efficient feature fusion. Our approach has two main innovations: (i) Efficient temporal fusion—we replace the Transformer with an improved, lightweight Hyena module to model More >

Laura Baitenova¹, Gulnar Mukhamejanova², Gauhar Munaitbas^3,*, Saken Mambetov¹, Zhanna Mukanova¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081004 - 23 July 2026

Abstract Morphological parsing is a fundamental task in natural language processing, particularly for morphologically rich languages where words encode complex grammatical and semantic information. This paper proposes a multi-branch Transformer-enhanced neural framework for joint morphological representation learning, designed to improve segmentation and classification accuracy by integrating complementary feature extraction mechanisms. The proposed architecture combines convolutional layers for capturing local morphological patterns, recurrent layers for modeling sequential dependencies, and Transformer-based self-attention for learning global contextual relationships. This hybrid design enables the model to generate robust and context-aware representations that enhance morphological understanding. The framework is trained using… More >

Xing Wen¹, Huan Chen¹, Ning Wang^1,*, Yu Song¹, Zhuqiao Qiao², Bin Zhang¹

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2025.073567 - 12 July 2026

Abstract High voltage direct current (HVDC) systems play a pivotal role in long-distance, high-capacity, and cross-regional power transmission. However, their complex structure, wide-ranging impact of faults, and stringent safety requirements pose significant challenges to operational stability. Conventional model-based and data-driven methods for tasks such as text classification, fault diagnosis, and operation and maintenance support suffer from limited scalability and interpretability. Recent advances in large language model (LLM) provide new opportunities to address these issues. This paper provides a systematic review of LLM applications in HVDC systems. Firstly, it introduces the core architecture and training mechanisms of… More >

Jaffar Hussain¹, Tahira Nazir¹, Junaid Rashid^2,*, Jungeun Kim^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081617 - 30 June 2026

Abstract Early detection of diabetic retinopathy (DR), media haze (MH), optic disc cupping (ODC), and glaucoma is crucial for preventing vision loss. However, timely diagnosis is often constrained by limited specialist availability and high diagnostic costs. This study proposes a You Only Look Once (YOLO)-based deep learning (DL) framework for the automated classification of fundus images into disease-specific categories. We unified diverse annotations from the Retinal Fundus Multi-Disease image Dataset (RFMiD), RFMiD2.0, and the DR Fundus Image Dataset (DR-FID) by standardizing annotation files and class labels. A custom filtering module was used to isolate single-pathology cases,… More >

Gaoteng Yuan^1,*, Ping Qiu², Qika Lin³, Jianchu Lin¹, Xiang Li¹, Dongping Gao⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081152 - 30 June 2026

Abstract Epilepsy is a chronic neurological disorder characterized by recurrent seizures, posing significant challenges to patients’ quality of life. Accurate classification of seizure states is crucial for effective intervention. This paper presents a deep learning-based approach for epileptic seizure classification by integrating multi-feature analysis of electroencephalogram (EEG) signals. The proposed method begins with signal preprocessing, including denoising, segmentation, and label construction. Subsequently, a comprehensive set of temporal, spectral, and wavelet-based features—such as signal mean, power, heart rate, and wavelet coefficients—is extracted. Feature selection is then performed using the Maximal Information Coefficient (MIC) to identify the most… More > Graphic Abstract

Anis Elgarduh¹, Anazida Zainal¹, Fuad A. Ghaleb², Sultan Noman Qasem^3,*, Abdullah M. Albarrak³, Faisal Saeed²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080862 - 30 June 2026

Abstract The rapid growth and increasing sophistication of malware pose significant challenges to traditional detection methods. Convolutional neural network (CNN)-based malware image classification methods have emerged as a promising approach by transforming binary files into visual representations and enabling automated feature extraction. To enhance discriminative learning, recent studies have incorporated attention mechanisms originally developed for natural image and natural language processing tasks. However, these mechanisms embed inductive biases that assume spatial coherence and visually salient semantics, assumptions that do not necessarily hold in malware image representations, where informative patterns may be subtle, structurally encoded, and globally… More > Graphic Abstract

Lin Fang^1,2, Razi Abdul-Rahman^1,*, Cheng-Fu Yang^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080387 - 30 June 2026

Abstract Parallel mechanisms are extensively employed in industrial logistics, food processing, and medical applications. Due to the strong nonlinearity and cross-axis coupling inherent in closed-chain kinematics, fault diagnostic performance is highly sensitive to signal perturbations and class imbalance under noisy measurement conditions. Furthermore, diagnostic models trained under single-fault scenarios often exhibit notable performance degradation when transferred to compound fault conditions as a result of distribution shift. In this study, a Delta 3D printer, as a representative parallel mechanism, is adopted as the experimental platform. An interpretable three-stage diagnostic framework is proposed, in which compound fault diagnosis… More >

Xianfeng Zhou^1,2,3, Shaogang Lei^1,*, Xinfeng Li², Zhaojie Zhang², Lijiao Jin², Jingcheng Zhang³, Dongmei Chen^3,*

Phyton-International Journal of Experimental Botany, Vol.95, No.6, 2026, DOI:10.32604/phyton.2026.080299 - 29 June 2026

Abstract Accurate recognition of visually similar pest species remains a major challenge in agricultural vision, given that existing datasets often lack sufficient taxonomic structure, confusable categories, and quantitative analysis of class-level visual difficulty. To address these limitations, we present AP60, a taxonomy-guided benchmark dataset for fine-grained pest recognition, comprising 62,091 images from 60 pest categories and organized according to insect taxonomy. A distinctive characteristic of AP60 is the deliberate inclusion of morphologically confusable taxa, which enables more realistic evaluation of recognition models under biologically meaningful fine-grained settings. Beyond dataset construction, we introduce a feature-level confusion analysis… More >