Shunran Duan, Meijuan Yin^*, Xiangyang Luo, Lunchong Cui, Chenyu Wang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084290 - 13 August 2026

Abstract Few-shot relation classification aims to identify semantic relations between entity pairs under limited annotated data. Although recent prompt learning-based methods have achieved promising performance, they often rely on manually crafted, domain-specific prompt templates, which restrict their transferability across domains. In this paper, building upon the multi-task prompt transfer paradigm of MPT, we propose a Confidence-guided Orthogonality-constraint Prompt Adaptation framework for few-shot relation classification, named COPA. The proposed framework learns a shared domain-invariant prompt matrix together with domain-specific low-rank prompt matrices via multi-domain soft prompt tuning, enabling the transfer of domain-invariant relational knowledge across domains. Unlike… More >

Safa Alsafari¹, Ayman Yafoz^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084403 - 27 July 2026

Abstract Bearing fault diagnosis in industrial deployment must contend with two simultaneous distributional shifts: fault severity increases as damage progresses, and motors operate at loads unseen during training. We define this compound setting as the double domain shift and present a rigorous few-shot benchmark on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets. Six architectures spanning distinct learning paradigms—a multilayer perceptron (MLP), a capsule network (CapsNet), a residual capsule network (ResCaps), a prototypical network (ProtoNet), a modified residual convolutional network (MRCN), and Deep Correlation Alignment (Deep CORAL)—are evaluated under a strict three-way split… 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 >

Mustafa Qaid Khamisi¹, Zuping Zhang^1,*, Mohammed Al-Habib¹, Muhammad Asim², Sajid Shah²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077556 - 08 May 2026

Abstract Large Vision-Language models (VLMs) such as Contrastive Language-Image Pretraining (CLIP) have transformed open world image recognition. Nevertheless, few-shot classification, particularly in the extremely low-shot regime, requires not only high accuracy but also reliably calibrated uncertainty for decisions with high confidence. Existing training-free CLIP adapters are primarily designed to increase accuracy and efficiency; integrate the zero-shot text logits with the few-shot feature caches, but not definitely model predictive uncertainty and therefore often exhibit considerable miscalibration and weak selective performance. Bayesian adapters move in the direction of probabilistic modeling by placing priors over adapter parameters and employing… More >

Yuxiao Shi¹, Jinglin Zhang², Yuxia Li^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078601 - 27 April 2026

Abstract Traffic light detection and fault identification using images from road traffic cameras are important for intelligent traffic management and urban safety monitoring. However, images collected in real traffic environments show clear differences in camera view, lighting conditions, weather, and background complexity. As a result, traffic lights vary greatly in scale, spatial location, and appearance, which reduces detection accuracy in complex scenes. To deal with this problem, this paper presents a multi-scene traffic light detection and fault identification framework based on dual-attention image fusion. Large-scale road camera data from the Chengdu Traffic Management Bureau are used,… More >

Liam Revell¹, Hyunjae Kang^1,*, Jung Taek Seo², Dan Dongseong Kim¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078467 - 27 April 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices has increased the importance of network intrusion detection systems (NIDS) for protecting modern networks. However, many machine learning and deep learning based NIDS rely on large volumes of labeled attack data, which is often impractical to obtain for newly emerging or rare attacks. This paper presents a benchmark-style systematic evaluation of meta-learning-based Few-Shot Learning (FSL) classifiers for detecting previously unseen intrusions with limited labeled data. We investigate three representative FSL models, namely Prototypical Networks, Relation Networks, and MetaOptNet, and further examine two decision-level ensemble strategies based… More >

Chia-Hui Liu^*, Hao-Feng Liu

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077837 - 30 March 2026

Abstract Artificial Intelligence (AI)-native sixth-generation (6G) wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments. In such networks, massive device heterogeneity and time-varying channel conditions pose significant challenges, as reliable authentication must be achieved with limited labeled data and constrained edge resources. To address this challenge, this paper proposes an Artificial Intelligence (AI)-assisted few-shot physical-layer modeling framework for channel robust device identification, formulated within the paradigm of Specific Emitter Identification (SEI) based on radio… More >

Yong Hu¹, Weifan Xu², Xiangtong Du^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074437 - 12 March 2026

Abstract Deep learning-based methods have shown great potential in intelligent bearing fault diagnosis. However, most existing approaches suffer from the scarcity of labeled data, which often results in insufficient robustness under complex working conditions and a general lack of interpretability. To address these challenges, we propose a physics-informed multimodal fault diagnosis framework based on few-shot learning, which integrates a 2D time-frequency image encoder and a 1D vibration signal encoder. Specifically, we embed prior knowledge of multi-resolution analysis from signal processing into the model by designing a Laplace Wavelet Convolution (LWC) module, which enhances interpretability since wavelet More >

Zuyi Huang¹, Yue Wang¹, Jia Liu², Haodong Yi¹, Lejun Ai¹, Min Chen^1,3,*, Salman A. AlQahtani⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074034 - 10 February 2026

Abstract The personalized fine-tuning of large language models (LLMs) on edge devices is severely constrained by limited computation resources. Although split federated learning alleviates on-device burdens, its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning, which leads to excessive communication overhead. To address this, we propose Language-Empowered Split Fine-Tuning (LESFT), a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm. LESFT simultaneously learns from multiple logically equivalent but linguistically diverse reasoning chains, providing richer supervisory signals and improving data efficiency. This process-oriented training allows more effective reasoning More >

Jihoon Moon^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071052 - 29 January 2026

Abstract Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined… More >