Munthir Qasaimeh¹, Mostafa Ali¹, Qasem Abu Al-Haija^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080864 - 27 May 2026

Abstract Vision Transformers (ViTs) have recently achieved high performance in retinal Optical Coherence Tomography (OCT) classification studies. However, ViT models continue to face significant challenges, including high computational cost, vulnerability to adversarial attacks, and pronounced sensitivity to preprocessing techniques. This study introduces GreenShield, a unified framework designed to produce an efficient and robust ViT model, referred to as GreenShield-ViT, which outperforms existing lightweight ViT variants in terms of adversarial robustness for retinal OCT classification. The framework integrates a gradient-based block-importance pruning strategy to compress the ViT/B-16 architecture, and adversarial training with proper ImageNet normalization and anti-saturation… More >

Shuvodeep De^1,*, Agnivo Gosai^2,#, Karun Thankachan^3,#, Ramadan A. ZeinEldin⁴, Abdulaziz T. Almaktoom⁵, Mustafa Bayram⁶, Ali Wagdy Mohamed^7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080601 - 27 May 2026

Abstract Sentiment analysis (SA) has evolved from a niche text-classification task into a central problem in natural language processing, spanning multiple domains, modalities, and languages. This survey provides a comprehensive review of sentiment analysis methods from their origins in lexicon-based approaches through classical machine learning, deep learning architectures, pre-trained transformers, and the current era of large language models (LLMs). We formalize the SA problem across multiple granularity levels (document, sentence, and aspect) and present a taxonomy that encompasses classification, regression, aspect-based sentiment analysis (ABSA), emotion detection, and stance detection tasks across diverse domains including movie reviews,… More >

Natalia Prieto-Fernández¹, Martín Bayón-Gutiérrez^1,*, Sergio Fernández-Blanco¹, Álvaro Fernández-Blanco¹, Francisco Carro-De-Lorenzo², José Alberto Benítez-Andrades¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080540 - 27 May 2026

Abstract Feature-based Simultaneous Localization and Mapping (SLAM) using 2D Light Detection and Ranging (LiDAR) in structured indoor environments commonly relies on the extraction of straight segments and corners from raw scan data. The quality of these landmarks depends not only on the fitting algorithm, but also on how uncertainty is modeled and propagated from line estimates to derived corner features. Although the magnitude of LiDAR uncertainty has been widely studied, the influence of line parameterization and geometric conditioning on uncertainty propagation has received less attention. In particular, the scale ambiguity inherent to implicit line representations can… More >

Qilong Wang¹, Ning Wang¹, Shuhan Luo¹, Xiang Gao², Yuqian Lu³, Min He^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080372 - 27 May 2026

Abstract Tunnel environments often suffer from GPS denial, uneven illumination, and structural uniformity, which lead to feature degradation, loop closure failure, and long-distance drift in SLAM systems. To solve these problems, this study aims to propose a high-precision SLAM method suitable for tunnel structural health monitoring. Firstly, an ABA-CLAHE image enhancement algorithm is proposed, which adopts cascaded processing of nonlinear brightness adjustment in HSV space and CLAHE local contrast optimization to improve low-light image quality and enhance feature stability. Then, SURF feature matching combined with the RANSAC algorithm is used to ensure feature matching accuracy. Finally, More >

Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026

Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >

Abdulrahman Dira Khalaf^1,2,*, Hazlina Hamdan^1,*, Alfian Abdul Halin¹, Noridayu Manshor¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.075537 - 27 May 2026

Abstract Current automated lesion segmentation methods have limited success, particularly for segmenting small, irregular, or heterogeneous lesions. Moreover, such models require significant computational power, which restricts their scalability and clinical application. To overcome these limitations, a lightweight LANET, which is a layer-attention network based on an encoder–decoder deep-learning architecture, has the explicit goal of increasing the segmentation performance and computational efficiency. The LANET is coupled with three new modules: (i) an attention module that includes a depthwise separable convolution operator to reduce the number of parameters, (ii) a custom attention mechanism, and (iii) an atrous spatial… More > Graphic Abstract

Abdul Ghafoor^1,*, Muhammad Munir^2,*, Khalid Turk¹, Muhammad Tahir³, Umair Riaz⁴, Adnan Mustafa⁵

Phyton-International Journal of Experimental Botany, Vol.95, No.5, 2026, DOI:10.32604/phyton.2026.080990 - 27 May 2026

Abstract Agricultural systems increasingly face interacting abiotic and biotic stresses driven by climate change and soil degradation. Plant performance under such conditions is determined by coordinated networks of functional traits governing resource acquisition, allocation, and defense. These traits also structure plant-associated microbiomes, whose activities influence nutrient cycling, stress buffering, and disease suppression. This review synthesizes current evidence that agricultural nanomaterials enhance crop stress resilience primarily by reprogramming plant functional trait networks and, through them, modulating microbiome dynamics. We analyze how nanomaterial physicochemical properties including size, surface chemistry, dissolution behavior, and redox activity determine their bioavailability and… More >

Emerson de Medeiros de Sousa^1,#, Salvador Barros Torres^2,#, Marciana Bizerra de Morais^3,#, Clarisse Pereira Benedito², Kleane Targino Oliveira Pereira², Moadir de Sousa Leite², Maria Valdiglezia de Mesquita Arruda², Jéssica Christie Dantas de Oliveira Costa², Roseane Rodrigues de Oliveira², Giovanna Dias de Sousa², Cynthia Cavalcanti de Albuquerque³, Marco Porceddu⁴, Gianluigi Bacchetta⁴, Francisco Vanies da Silva Sá^5,*

Phyton-International Journal of Experimental Botany, Vol.95, No.5, 2026, DOI:10.32604/phyton.2026.078410 - 27 May 2026

Abstract Semiarid regions are frequently affected by low water availability, which hinders the development of horticultural species such as melon (Cucumis melo L.). In this context, techniques that enhance drought tolerance are essential for more effective crop management. This study aimed to evaluate the tolerance and antioxidant activity of different melon cultivars using seed pre-treatment with stress-attenuating agents. The experiment was conducted in two stages, both arranged in a completely randomized design with four replicates of 50 seeds. In the first stage, a 3 × 5 factorial scheme was used, combining three levels of water deficit (0.0,… More >

Fayaz Ullah Shinwari¹, Mumtaz Ali Khan^2,*, Saad Khan^3,4, Rizwan Niaz⁵, Mansour Almazroui^6,7

Revue Internationale de Géomatique, Vol.35, pp. 273-290, 2026, DOI:10.32604/rig.2026.080586 - 21 May 2026

Abstract Groundwater is an essential resource contributing substantially to the annual total water supply. It enables agricultural irrigation and provides billions of people with their main source of drinking water. But overuse of groundwater has decreased its supply and, in certain places, resulted in soil subsidence. In the complex hydrogeological terrain of Timergara, traditional groundwater exploration is challenging and costly, requiring more efficient mapping approaches. Groundwater recharge potential zones must be assessed in order to guarantee sustainable groundwater management. This study uses Remote Sensing (RS) and Geographic Information System (GIS) methodologies to evaluate groundwater potential sites… More >

Meerim Park¹, Seungman Park², Ensel Oh³, Jongmun Choi⁴, Mi Mi Kwon¹, Seog-Yun Park⁵, Jun Ah Lee¹, Hyeon Jin Park^1,*

Oncology Research, Vol.34, No.6, 2026, DOI:10.32604/or.2026.079120 - 21 May 2026

Abstract Objectives: Germline variants in cancer predisposition genes have been increasingly recognized in pediatric cancers. However, their spectrum in East Asian children with central nervous system (CNS) tumors remains insufficiently defined. This study investigated the prevalence and clinical significance of pathogenic or likely pathogenic (P/LP) germline mutations in Korean children, adolescents, and young adults (AYAs) with CNS tumors. Methods: We performed targeted next-generation sequencing of 358 cancer-associated genes using peripheral blood DNA from 108 patients. Germline variants were classified according to ACMG/AMP guidelines and curated using ClinVar and relevant literature. Results: Among 108 patients, 17 (15.7%) carried P/LP… More >