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 >

Weiqi Mao^1,2,3, Enbo Yu^1,*, Guoji Xu³, Xiaozhen Li³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.069733 - 29 January 2026

Abstract Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration. This study presents a novel machine learning model that integrates clustering, deep learning, and transfer learning to mitigate accuracy degradation in 24-h forecasting. Initially, an optimized DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters wind fields based on wind direction, probability density, and spectral features, enhancing physical interpretability and reducing training complexity. Subsequently, a ResNet (Residual Network) extracts multi-scale patterns from decomposed wind signals, while transfer learning adapts the backbone network across clusters, cutting training time by over… More >

Williams Kyei^*, Chunyong Yin, Kelvin Amos Nicodemas, Khagendra Darlami

Journal on Artificial Intelligence, Vol.8, pp. 19-38, 2026, DOI:10.32604/jai.2026.074988 - 20 January 2026

Abstract The COVID-19 pandemic has underscored the need for rapid and accurate diagnostic tools to differentiate respiratory infections from normal cases using chest X-rays (CXRs). Manual interpretation of CXRs is time-consuming and prone to errors, particularly in distinguishing COVID-19 from viral pneumonia. This research addresses these challenges by proposing a customized EfficientNet-B0 model for ternary classification (COVID-19, Viral Pneumonia, Normal) on the COVID-19 Radiography Database. Employing transfer learning with architectural modifications, including a tailored classification head and regularization techniques, the model achieves superior performance. Evaluated via accuracy, F1-score (macro-averaged), AUROC (macro-averaged), precision (macro-averaged), recall (macro-averaged), inference… More >

Faisal Mahmud¹, Fahim Mahmud², Rashedur M. Rahman^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073519 - 12 January 2026

Abstract Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of… More >

Nour El Houda Kaiber¹, Tahar Mekhaznia¹, Akram Bennour^1,*, Mohammed Al-Sarem^2,3,*, Zakaria Lakhdara⁴, Fahad Ghaban², Mohammad Nassef^5,6

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070337 - 12 January 2026

Abstract The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness. Deep learning has emerged as a promising solution, offering new avenues for improvements. However, building models from scratch is computationally expensive and requires large datasets. This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image. The core idea is to fine-tune a pre-trained model on specific object categories using new, unseen data, resulting in specialized versions of the model that are better adapted to reconstruct particular objects. The proposed approach utilizes a… More >

Kirubavathi Ganapathiyappan¹, Heba G. Mohamed², Abhishek Yadav¹, Guru Akshya Chinnaswamy¹, Ateeq Ur Rehman^3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069951 - 09 December 2025

Abstract The escalating complexity of modern malware continues to undermine the effectiveness of traditional signature-based detection techniques, which are often unable to adapt to rapidly evolving attack patterns. To address these challenges, this study proposes X-MalNet, a lightweight Convolutional Neural Network (CNN) framework designed for static malware classification through image-based representations of binary executables. By converting malware binaries into grayscale images, the model extracts distinctive structural and texture-level features that signify malicious intent, thereby eliminating the dependence on manual feature engineering or dynamic behavioral analysis. Built upon a modified AlexNet architecture, X-MalNet employs transfer learning to… More >

Chaonan Xin^*, Keqing Xu

Journal of Cyber Security, Vol.7, pp. 483-503, 2025, DOI:10.32604/jcs.2025.071627 - 28 November 2025

Abstract Intrusion Detection Systems (IDS) have achieved high accuracy on benchmark datasets, yet models often fail to generalize across different network environments. In this paper, we propose Transformer-IDS, a transformer-based network intrusion detection model designed for cross-dataset generalization. The model incorporates a classification token, multi-head self-attention, and embedding layers to learn versatile features, and it introduces a calibration module and an AUC-oriented optimization objective to improve reliability and ranking performance. We evaluate Transformer-IDS on three prominent datasets (NSL-KDD, UNSW-NB15, CIC-IDS2017) in both within-dataset and cross-dataset scenarios. Results demonstrate that while conventional deep IDS models (e.g., CNN-LSTM More >

Adeel Akram¹, Tallha Akram², Ghada Atteia^3,*, Ayman Qahmash⁴, Sultan Alanazi⁵, Faisal Mohammad Alotaibi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2311-2337, 2025, DOI:10.32604/cmes.2025.069697 - 26 November 2025

Abstract Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics, particularly in dermatology. This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks (DNNs) with transfer learning, a customized DNN, and an optimized self-learning binary differential evolution (SLBDE) algorithm for feature selection and fusion. Leveraging computational techniques alongside medical imaging modalities, the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness. The methodology is evaluated on benchmark datasets, including ISIC 2017 and the Argentina Skin Lesion dataset, demonstrating superior accuracy, precision, and F1-score… More >

Qiang Ma^1,2,3,4, Zepeng Li^1,2, Kai Yang^1,2,*, Shaofeng Zhang^1,2, Zhuopei Wei^1,2

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1607-1634, 2025, DOI:10.32604/sdhm.2025.069876 - 17 November 2025

Abstract Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL,… More >

Amit Prakash Singh¹, Kajal Kaul^1,*, Anuradha Chug¹, Ravinder Kumar², Veerubommu Shanmugam²

Journal on Artificial Intelligence, Vol.7, pp. 451-468, 2025, DOI:10.32604/jai.2025.070773 - 06 November 2025

Abstract In agricultural farms in India where the staple diet for most of the households is potato, plant leaf diseases, namely Potato Early Blight (PEB) and Potato Late Blight (PLB), are quite common. The class label Plant Healthy (PH) is also used. If these diseases are not identified early, they can cause massive crop loss and thereby incur huge economic losses to the farmers in the agricultural domain and can impact the gross domestic product of the nation. This paper presents a hybrid approach for potato plant disease severity estimation and classification of diseased and healthy… More >