Abdul Rahaman Wahab Sait^1,*, Yazeed Alkhurayyif²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072481 - 12 January 2026

Abstract Malware poses a significant threat to the Internet of Things (IoT). It enables unauthorized access to devices in the IoT environment. The lack of unique architectural standards causes challenges in developing robust malware detection (MD) models. The existing models demand substantial computational resources. This study intends to build a lightweight MD model to detect anomalies in IoT networks. The authors develop a transformation technique, converting the malware binaries into images. MobileNet V2 is fine-tuned using improved grey wolf optimization (IGWO) to extract crucial features of malicious and benign samples. The ResNeXt model is combined with… More >

Qian Yu^1,2, Gui Zhang^2,*, Ying Wang¹, Xin Wu², Jiangshu Xiao², Wenbing Kuang¹, Juan Zhang²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072172 - 12 January 2026

Abstract Detecting small forest fire targets in unmanned aerial vehicle (UAV) images is difficult, as flames typically cover only a very limited portion of the visual scene. This study proposes Context-guided Compact Lightweight Network (CCLNet), an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources. CCLNet employs a three-stage network architecture. Its key components include three modules. C3F-Convolutional Gated Linear Unit (C3F-CGLU) performs selective local feature extraction while preserving fine-grained high-frequency flame details. Context-Guided Feature Fusion Module (CGFM) replaces plain concatenation with triplet-attention interactions to… More >

Jalees Ur Rahman¹, Muhammad Hanif¹, Usman Haider^2,*, Saeed Mian Qaisar^3,*, Sarra Ayouni⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071251 - 12 January 2026

Abstract Deep neural networks have achieved excellent classification results on several computer vision benchmarks. This has led to the popularity of machine learning as a service, where trained algorithms are hosted on the cloud and inference can be obtained on real-world data. In most applications, it is important to compress the vision data due to the enormous bandwidth and memory requirements. Video codecs exploit spatial and temporal correlations to achieve high compression ratios, but they are computationally expensive. This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos. However, contrary… More >

Chee Chin Lim^1,2,*, Hui Wen Tiu¹, Qi Wei Oung^1,3, Chiew Chea Lau⁴, Xiao Jian Tan^2,5

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070468 - 12 January 2026

Abstract Breast cancer remains one of the leading causes of cancer mortality world-wide, with accurate molecular subtyping is critical for guiding treatment and improving patient outcomes. Traditional molecular subtyping via immuno-histochemistry (IHC) test is invasive, time-consuming, and may not fully represent tumor heterogeneity. This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes. Four pretrained models, including two Convolutional Neural Networks (MobileNet_V3_Large and VGG-16) and two Vision Transformers (ViT_B_16 and ViT_Base_Patch16_Clip_224) were fine-tuned to classify images into HER2-enriched, Luminal, Normal-like, and Triple Negative subtypes. Hyperparameter tuning,… More >

Haoran Wang¹, Shuhong Yang², Kuan Shao², Tao Xiao², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069404 - 10 November 2025

Abstract With the rapid development of the Artificial Intelligence of Things (AIoT), convolutional neural networks (CNNs) have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks. However, the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices. Therefore, this paper proposes an efficient privacy-preserving CNN framework (i.e., EPPA) based on the Fully Homomorphic Encryption (FHE) scheme for AIoT application scenarios. In the plaintext domain, we verify schemes with different activation structures to determine the… More >

Kemahyanto Exaudi^1,2, Deris Stiawan^3,*, Bhakti Yudho Suprapto¹, Hanif Fakhrurroja⁴, Mohd. Yazid Idris⁵, Tami A. Alghamdi⁶, Rahmat Budiarto⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.069377 - 10 November 2025

Abstract Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by… More >

Kwok Tai Chui^1,*, Varsha Arya¹, Brij B. Gupta^2,3,4,*, Miguel Torres-Ruiz⁵, Razaz Waheeb Attar⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068842 - 10 November 2025

Abstract Parkinson’s disease (PD) is a debilitating neurological disorder affecting over 10 million people worldwide. PD classification models using voice signals as input are common in the literature. It is believed that using deep learning algorithms further enhances performance; nevertheless, it is challenging due to the nature of small-scale and imbalanced PD datasets. This paper proposed a convolutional neural network-based deep support vector machine (CNN-DSVM) to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets. A customized kernel function reduces the impact… More >

Yan Li^1,2,*, Yibin Wu^1,2, Bo Zhang^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 2905-2925, 2025, DOI:10.32604/fdmp.2025.072979 - 31 December 2025

Abstract Traditional Computational Fluid Dynamics (CFD) simulations are computationally expensive when applied to complex fluid–structure interaction problems and often struggle to capture the essential flow features governing vortex-induced vibrations (VIV) of floating structures. To overcome these limitations, this study develops a hybrid framework that integrates high-fidelity CFD modeling with deep learning techniques to enhance the accuracy and efficiency of VIV response prediction. First, an unstructured finite-volume fluid–structure coupling model is established to generate high-resolution flow field data and extract multi-component time-series feature tensors. These tensors serve as inputs to a Squeeze-and-Excitation Convolutional Neural Network (SE-CNN), which… More >

Sarfaraz Abdul Sattar Natha^1,*, Mohammad Siraj^2,*, Majid Altamimi², Adamali Shah², Maqsood Mahmud³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4179-4201, 2025, DOI:10.32604/cmes.2025.072529 - 23 December 2025

Abstract A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network… More >

Zheng Yin, Zhao Zhang^*

Journal on Artificial Intelligence, Vol.7, pp. 633-645, 2025, DOI:10.32604/jai.2025.074450 - 24 December 2025

Abstract Power load forecasting load forecasting is a core task in power system scheduling, operation, and planning. To enhance forecasting performance, this paper proposes a dual-input deep learning model that integrates Convolutional Neural Networks, Gated Recurrent Units, and a self-attention mechanism. Based on standardized data cleaning and normalization, the method performs convolutional feature extraction and recurrent modeling on load and meteorological time series separately. The self-attention mechanism is then applied to assign weights to key time steps, after which the two feature streams are flattened and concatenated. Finally, a fully connected layer is used to generate More >