Ovidijus Grigas^*, Rytis Maskeliūnas

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077324 - 27 April 2026

Abstract Early detection of Mild Cognitive Impairment (MCI) with FDG-PET is essential for timely Alzheimer’s disease intervention. However, PET image quality is limited by low spatial resolution, partial volume effects, and Poisson noise. Standard enhancement methods, such as Bilateral filtering or Contrast Limited Adaptive Histogram Equalization (CLAHE), can increase contrast but often introduce heavy noise or distort image texture, while deep learning methods may produce hallucinated structures. We propose a fully data-adaptive, non-learned 3D enhancement framework whose output is deterministic for a given input volume, that combines Laplacian-based local contrast modulation with a gradient-gated Difference-of-Gaussians (DoG)… More >

K. Mithra^1,*, Prem Kumar Santhanam²

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 87-97, 2026, DOI:10.32604/jimh.2026.080288 - 24 April 2026

Abstract Background: Retinal fundus imaging is central to early diagnosis of sight-threatening conditions, including diabetic retinopathy, glaucoma, and retinal vein occlusion. Clinical utility is compromised by non-uniform illumination, motion blur, and low contrast—artefacts that reduce diagnostic accuracy. Effective image enhancement is a prerequisite for reliable computer-aided ophthalmic diagnosis. Methods: This paper proposes a two-stage enhancement pipeline combining luminosity correction via HSV colour space decomposition with Contrast Limited Adaptive Histogram Equalization (CLAHE) on the Value (V) channel. Validation is conducted on three publicly available benchmarks: DRIVE (40 images), STARE (20 images), and CHASEDB1 (28 images). Quantitative metrics… More >

Devita Amelia¹, R. A. Ilyas^1,2,*, Hairul Abral^2,3, Mochamad Asrofi⁴, Muhammad Asyraf Muhammad Rizal^2,5, Mohamad Zaki Hassan⁶, Mohamad Haafiz Mohamad Kassim^2,7,8, Nurul Fazita Mohammad Rawi^2,7,8, Nasrullah Razali⁹, Melbi Mahardika^2,7,8,10,*

Journal of Renewable Materials, Vol.14, No.4, 2026, DOI:10.32604/jrm.2025.02025-0147 - 24 April 2026

Abstract This study characterizes biocomposites derived from jicama starch and reinforced with microfibers obtained from jicama bagasse (JB). The incorporation of jicama bagasse microfibers into the jicama matrix was systematically varied at concentrations of 1, 2, 3, 4, and 5 wt%. The starch film and biocomposite were prepared using solution casting methodologies, employing glycerol as a plasticizing agent. The biocomposites were characterized using Fourier-transform infrared spectroscopy, X-ray diffraction analysis, and scanning electron microscopy. In addition, the moisture absorption and tensile properties were evaluated. The jicama starch contained 44% w/w amylose, whereas the jicama bagasse microfiber contained… More >

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*, Changheun Oh⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077510 - 09 April 2026

Abstract The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence (AI) assistive reasoning with the incorporation of context-awareness. Context-awareness plays a significant role in fulfilling users’ needs whenever and wherever needed. Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems, perform reasoning using reinforcement learning (RL) or other reasoning techniques, and then adapt behavior. The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place. Generally, agents are rewarded for… More >

Kiseok Kim^#, Taehoon Yoo^#, Sangmin Lee, Hwangnam Kim^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077415 - 09 April 2026

Abstract Conventional Low-Rank Adaptation (LoRA) constrains weight updates to a static linear low-rank manifold, which is inherently limited when applied to Reinforcement Learning (RL) tasks for Unmanned Aerial Vehicle (UAV) applications. UAVs operate in highly dynamic and nonstationary environments where rapid variations in sensing and state transitions lead to complex, nonlinear input–output relationships. Such environmental complexity cannot be adequately modeled by a static Low-rank approximation, making conventional LoRA approaches insufficient for the high-dimensional dynamics required in UAV applications. To overcome these limitations, we propose an attention-enhanced LoRA that constructs an input-dependent and intrinsically nonlinear adaptation manifold.… More >

Kexin Jiang^1,2, Yong Fan², Liang Wen¹, Zhigang Xie¹, Enzhi Dong¹, Bo Zhu¹, Zhonghua Cheng^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077392 - 09 April 2026

Abstract With the growing deployment of unmanned aerial vehicles (UAVs), reliable engine health state assessment (HSA) requires methods that are interpretable, auditable, and transferable under noisy data and varying operating conditions. This paper proposes an AHP-enhanced, data-driven HSA framework that builds a unified health vector from four indicators—remaining useful life (RUL) health, absolute state, relative degradation, and condition health. Indicator weights are derived using AHP with consistency checking, and the resulting continuous health index is mapped through nonlinear stretching and four-level thresholds to produce actionable health grades. Experiments on the NASA CMAPSS benchmark (FD001) evaluate conventional More >

Yang Lei^1,2, Kangshuo Zhu^3,4,*, Bo Jiang¹, Yaodong Wang^3,4, Feiyu Jia¹, Zhaoning Wang¹, Falin Qi¹, Qiming Qu¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077314 - 09 April 2026

Abstract The safe operation of railway systems necessitates efficient and automated inspection of tunnel defects. While deep learning offers solutions, a clear pathway for selecting and optimizing the latest object detectors for distinct defects under strict speed constraints is lacking. This paper presents a two-stage, task-specific framework for high-speed tunnel defect detection. First, this study conducts a comprehensive comparative analysis of state-of-the-art YOLO models (YOLOv5s, YOLOv8s, YOLOv10s, YOLOv11s) on self-constructed datasets. This systematic comparison identifies YOLOv5s as the optimal model for crack detection, achieving an mAP@0.5 of 0.939 at 77.5 FPS, sufficient for inspection at 50… More >

Hitesh Mohapatra^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076726 - 09 April 2026

Abstract This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge. Existing literature shows that edge-based distributed intelligence reduces cloud dependency. It addresses transmission latency, device energy use, and bandwidth limits. Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination. Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8% and energy efficiency gains of 27.4% compared to conventional cloud-centric models.… More >

Udit Mamodiya^1,*, Indra Kishor², P. Satish Reddy³, K. Lakshmi Kalpana³, Radha Seelaboyina⁴, Harish Reddy Gantla⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076464 - 09 April 2026

Abstract The direct conversion of solid-state heat to electricity using thermoelectric materials has attracted attention; however, their effective application is limited because of the challenge of ensuring a balance between the microstructural features at the quantum, mesoscale, and continuum scales. Current computational and machine-learning methods have a small design space, wherein few to no interactions between the electronic structure, phonon transport, and device-level are considered. This makes it difficult to discover stable high-figure of merit (ZT) settings that are manufacturable and strong in the actual working environment. This study presents a multiscale hybrid optimization framework that… More >

Mingfang Li, Damin Zhang^*, Qing He, Chenglong Zhou, Mingrong Li, Xiaobo Zhou

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076433 - 09 April 2026

Abstract Traffic sign detection is a critical task in autonomous driving environmental perception. However, models often suffer from degraded detection performance in complex real-world scenarios due to variable target scales, blurred fine-grained features, and complex background interference. This paper proposes an improved YOLOv8n detection model, MSD-YOLO, to address these challenges. First, a Multi-scale Detail Enhancement Module (MDEM) is designed, which achieves targeted enhancement of edge features through high-frequency residual modulation and multi-scale cooperative attention. Second, an enhanced feature pyramid network termed SG-FPN is constructed. It introduces soft nearest neighbor interpolation (SNI) for semantic-spatial aligned feature fusion… More >