Yongmei Zhang^*, Tianxin Zhang, Linghua Tian

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073159 - 12 January 2026

Abstract Marine forecasting is critical for navigation safety and disaster prevention. However, traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features. To address the limitations, the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism. The model treats target forecast values as internal variables, and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer. Using a self-attention structure, the model captures correlations between exogenous variables and target sequences, explores intrinsic More >

Jixin Wu^1,2, Mingtao Zhou^2,3, Di Wu^2,3, Wenqi Ren⁴, Jiatian Mei^2,3, Shu Zhang^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072964 - 12 January 2026

Abstract End-to-end Temporal Action Detection (TAD) has achieved remarkable progress in recent years, driven by innovations in model architectures and the emergence of Video Foundation Models (VFMs). However, existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs, which become particularly pronounced when processing long video sequences. Moreover, the need for precise temporal boundary annotations makes data labeling extremely expensive. In low-resource settings where annotated samples are scarce, direct fine-tuning tends to cause overfitting. To address these challenges, we introduce Dynamic Low-Rank Adapter (DyLoRA), a lightweight fine-tuning framework tailored specifically… More >

Afnan Alhindi^*, Saad Al-Ahmadi, Mohamed Maher Ben Ismail

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072625 - 12 January 2026

Abstract Split Learning (SL) has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency. Specifically, neural networks are divided into client and server sub-networks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices, thereby making SL particularly suitable for resource-constrained devices. Although SL prevents the direct transmission of raw data, it does not alleviate entirely the risk of privacy breaches. In fact, the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data. Moreover,… More >

Tahir Mahmood^1,*, Muhammad Waseem Ashraf^1,*, Shahzadi Tayyaba², Muhammad Munir³, Babiker M. A. Abdel-Banat³, Hassan Ali Dinar³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072436 - 12 January 2026

Abstract Artificial intelligence (AI) based models have been used to predict the structural, optical, mechanical, and electrochemical properties of zinc oxide/graphene oxide nanocomposites. Machine learning (ML) models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and hybrid, along with fuzzy logic tools, were applied to predict the different properties like wavelength at maximum intensity (444 nm), crystallite size (17.50 nm), and optical bandgap (2.85 eV). While some other properties, such as energy density, power density, and charge transfer resistance, were also predicted with the help of datasets of 1000 (80:20). In… More >

Ishaani Priyadarshini^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072343 - 12 January 2026

Abstract Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes. This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) architecture, optimized using the Firefly Optimization algorithm (FO). The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data, capturing both local patterns and sequential dependencies in diagnostic features, while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance. The approach is evaluated on two benchmark biomedical datasets: one comprising diagnostic data… More >

Haohui Su¹, Xuan Zhang^1,*, Lvjun Zheng¹, Xiaojie Shen², Hua Liao¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072093 - 12 January 2026

Abstract Distributed Denial-of-Service (DDoS) attacks pose severe threats to Industrial Control Networks (ICNs), where service disruption can cause significant economic losses and operational risks. Existing signature-based methods are ineffective against novel attacks, and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments. To address these challenges, this study proposes a deep feature-driven hybrid framework that integrates Transformer, BiLSTM, and KNN to achieve accurate and robust DDoS detection. The Transformer component extracts global temporal dependencies from network traffic flows, while BiLSTM captures fine-grained sequential dynamics. The learned… More >

Georgia Garani^1,*, George Pramantiotis², Francisco Javier Moreno Arboleda³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071509 - 12 January 2026

Abstract Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation. Modern seismological research produces vast volumes of heterogeneous data from seismic networks, satellite observations, and geospatial repositories, creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making. Data warehousing technologies provide a robust foundation for this purpose; however, existing earthquake-oriented data warehouses remain limited, often relying on simplified schemas, domain-specific analytics, or cataloguing efforts. This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity. The framework integrates… More >

Sophort Siet¹, Sony Peng², Ilkhomjon Sadriddinov³, Kyuwon Park^4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071192 - 12 January 2026

Abstract Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories. The collaborative filtering (CF) model, which depends exclusively on user-item interactions, commonly encounters challenges, including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior. This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking (BPR) optimization to address these limitations. With the strong support of Long Short-Term Memory (LSTM) networks, we apply it to identify sequential dependencies of user behavior and then incorporate… More >

Yang Liu, Qi Lu, Junjie Wu, Huaichang Yin, Shiwei Cheng^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070273 - 12 January 2026

Abstract The development of brain-computer interfaces (BCI) based on motor imagery (MI) has greatly improved patients’ quality of life with movement disorders. The classification of upper limb MI has been widely studied and applied in many fields, including rehabilitation. However, the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex, making it difficult to distinguish their features. Therefore, classifying lower limbs motor imagery is more challenging. In this study, we propose a feature extraction method based on functional connectivity, which utilizes phase-locked values to construct a… More >

Tsu-Yang Wu¹, Chengyuan Yu¹, Yanan Zhao², Saru Kumari³, Chien-Ming Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069139 - 12 January 2026

Abstract With the increasing complexity of logistics operations, traditional static vehicle routing models are no longer sufficient. In practice, customer demands often arise dynamically, and multi-depot systems are commonly used to improve efficiency. This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand. New customers appear in the delivery process at any time and are periodically optimized according to time slices. Then, we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem. The classical ant colony More >