Sam Rahimzadeh Holagh¹, Bugao Xu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2383-2399, 2025, DOI:10.32604/cmes.2025.072259 - 26 November 2025

Abstract Cardiovascular diseases (CVD) remain a leading cause of mortality worldwide, highlighting the need for precise risk assessment tools to support clinical decision-making. This study introduces a meta-learning model for predicting mortality risk in patients with CVD, classifying them into high-risk and low-risk groups. Data were collected from 868 patients at Tabriz Heart Hospital (THH) in Iran, along with two open-access datasets—the Cleveland Heart Disease (CHD) and Faisalabad Institute of Cardiology (FIC) datasets. Data preprocessing involved class balancing via the Synthetic Minority Over-Sampling Technique (SMOTE). Each dataset was then split into training and test sets, and… More >

Youlong Lyu^1,2,*, Ying Chu³, Qingpeng Qiu³, Jie Zhang^1,2, Jutao Guo⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3393-3418, 2025, DOI:10.32604/cmc.2025.068157 - 23 September 2025

Abstract In intelligent manufacturing processes such as aerospace production, computer numerical control (CNC) machine tools require real-time optimization of process parameters to meet precision machining demands. These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings, highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime. However, varying conditions induce feature distribution shifts, and scarce fault samples limit model generalization. Therefore, this paper proposes a causal-Transformer-based meta-learning (CTML) method for bearing fault diagnosis in CNC machine tools, comprising three core modules:… More >

Yujiao Tang¹, Yadong Wu^1,*, Yuanmei He², Jilin Liu¹, Weihan Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2331-2352, 2025, DOI:10.32604/cmc.2024.059115 - 17 February 2025

Abstract Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion… More >

Irfan Khan¹, Xianchao Zhang^1,*, Ramesh Kumar Ayyasamy^2,*, Saadat M. Alhashmi³, Azizur Rahim⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1611-1636, 2025, DOI:10.32604/cmes.2025.058566 - 27 January 2025

Abstract The rapid growth of machine learning (ML) across fields has intensified the challenge of selecting the right algorithm for specific tasks, known as the Algorithm Selection Problem (ASP). Traditional trial-and-error methods have become impractical due to their resource demands. Automated Machine Learning (AutoML) systems automate this process, but often neglect the group structures and sparsity in meta-features, leading to inefficiencies in algorithm recommendations for classification tasks. This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso (MSGL) to address these limitations. Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional More >

Musulmon Lolaev, Anand Paul^*, Jeonghong Kim

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4707-4726, 2024, DOI:10.32604/cmc.2024.058675 - 19 December 2024

Abstract Evaluating artificial intelligence (AI) systems is crucial for their successful deployment and safe operation in real-world applications. The assessor meta-learning model has been recently introduced to assess AI system behaviors developed from emergent characteristics of AI systems and their responses on a test set. The original approach lacks covering continuous ranges, for example, regression problems, and it produces only the probability of success. In this work, to address existing limitations and enhance practical applicability, we propose an assessor feedback mechanism designed to identify and learn from AI system errors, enabling the system to perform the More >

Yasir Maqsood¹, Syed Muhammad Usman^1,*, Musaed Alhussein², Khursheed Aurangzeb^2,*, Shehzad Khalid³, Muhammad Zubair⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2795-2811, 2024, DOI:10.32604/cmc.2024.049410 - 15 May 2024

Abstract Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing… More >

Tao Yi^1,3, Xingshu Chen^1,2,*, Mingdong Yang³, Qindong Li¹, Yi Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 929-955, 2024, DOI:10.32604/cmes.2024.048793 - 16 April 2024

Abstract Due to the rapid evolution of Advanced Persistent Threats (APTs) attacks, the emergence of new and rare attack samples, and even those never seen before, make it challenging for traditional rule-based detection methods to extract universal rules for effective detection. With the progress in techniques such as transfer learning and meta-learning, few-shot network attack detection has progressed. However, challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning, difficulties in capturing rich information from original flow in the case… More >

Zekai Li¹, Wenfeng Wang^2,3,4,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2881-2899, 2023, DOI:10.32604/cmes.2023.028670 - 03 August 2023

Abstract We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge— to learn damaged objects in aerial videos. A meta-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated learning. Both the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning system. The study results indicated that the object classification accuracy is up to 90% and the average time cost in damage detection is only 0.277 s. Consequently, the broad federated More >

Xiuli Si¹, Biao Hong¹, Yuanhui Hu¹, Lidong Chu^2,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.036829 - 29 April 2023

Abstract Currently, one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development. Therefore, further research in the field of crop disease and pest detection is necessary to address the mentioned problem. Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses, this article proposes a Model-Agnostic Meta-Learning (MAML) attention model based on the meta-learning paradigm. The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention (ECA)… More >

Liwei Deng¹, Yuanzhi Zhang¹, Xin Yang^2,*, Sijuan Huang², Jing Wang^3,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2671-2684, 2023, DOI:10.32604/cmc.2023.036642 - 31 March 2023

Abstract High-resolution medical images have important medical value, but are difficult to obtain directly. Limited by hardware equipment and patient’s physical condition, the resolution of directly acquired medical images is often not high. Therefore, many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images. However, current super-resolution algorithms only work on a single scale, and multiple networks need to be trained when super-resolution images of different scales are needed. This definitely raises the cost of acquiring high-resolution medical images. Thus, we propose a multi-scale super-resolution algorithm using meta-learning. The algorithm… More >