Haydar Abdulameer Marhoon^1,2,*, Rafid Sagban^3,4, Atheer Y. Oudah^1,5, Saadaldeen Rashid Ahmed^6,7

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4181-4218, 2025, DOI:10.32604/cmc.2025.058822 - 06 March 2025

Abstract In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, healthcare monitoring, and environmental surveillance increasingly deploy WSNs, recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity. The proposed method innovatively partitions the network into logical segments or virtual barriers, allowing for targeted monitoring and data collection that aligns with specific traffic patterns. This approach not only improves the diversit. There are more types of data in the training set,… More >

Ramadan Abdul-Rashid¹, Mohd Amiruddin Abd Rahman^1,*, Kar Tim Chan¹, Arun Kumar Sangaiah^2,3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2585-2616, 2025, DOI:10.32604/cmes.2025.060548 - 03 March 2025

Abstract This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms. The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different stochastic gradient algorithms can be adopted for adaptive clock frequency adjustments. The study analyzes the pairwise synchronization behavior of the protocol and proves the generalized convergence of the synchronization error and clock frequency. A novel closed-form expression is also derived for a generalized asymptotic error variance steady state. Steady and convergence analyses are then presented for the synchronization, with frequency adaptations done using least More >

Shenghua Li¹, Kehan Liu², Bangxing Qian², Ziwei Wen², Bo Shi^2,*

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 231-248, 2025, DOI:10.32604/fhmt.2025.059963 - 26 February 2025

Abstract Phase change heat transfer devices like heat pipes are widely utilized in temperature control and heat transfer. However, the traditional single uniform wick makes it hard to meet the requirements of capillary pressure and permeability for high-performance heat pipes, thus limiting the improvement of heat transfer performance. In this paper, a gradient structure wick sintered by 316 L stainless steel powder is designed. The capillary performance is tested and characterized through permeability test experiments and capillary rise infrared test experiments. Moreover, the influence of different particle sizes of sintered powder on the capillary performance of More >

Kai Liu¹, Meizhao Liu¹, Ming Tang¹, Chen Zhang^2,*, Junwu Zhu^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2893-2908, 2025, DOI:10.32604/cmc.2024.057074 - 17 February 2025

Abstract The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to More >

Nada Mohammed Murad¹, Adnan Yousif Dawod², Saadaldeen Rashid Ahmed^3,4,*, Ravi Sekhar⁵, Pritesh Shah⁵

Intelligent Automation & Soft Computing, Vol.40, pp. 1-27, 2025, DOI:10.32604/iasc.2024.058624 - 10 January 2025

Abstract This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity, focusing on network intrusion detection systems (NIDS). The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing a more flexible and robust security architecture. We use seven unique machine learning models to improve detection skills, emphasizing data quality, traceability, and transparency, facilitated by a blockchain layer that safeguards against data modification and ensures auditability. Our technique employs the Synthetic Minority Oversampling Technique (SMOTE) to equilibrate the dataset, therefore mitigating prevalent class imbalance difficulties… More >

Syed Tasnimul Karim Ayon^1,#, Farhan Md. Siraj^1,#, Jia Uddin^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 499-520, 2025, DOI:10.32604/cmc.2025.058361 - 03 January 2025

Abstract This study investigates the application of Learnable Memory Vision Transformers (LMViT) for detecting metal surface flaws, comparing their performance with traditional CNNs, specifically ResNet18 and ResNet50, as well as other transformer-based models including Token to Token ViT, ViT without memory, and Parallel ViT. Leveraging a widely-used steel surface defect dataset, the research applies data augmentation and t-distributed stochastic neighbor embedding (t-SNE) to enhance feature extraction and understanding. These techniques mitigated overfitting, stabilized training, and improved generalization capabilities. The LMViT model achieved a test accuracy of 97.22%, significantly outperforming ResNet18 (88.89%) and ResNet50 (88.90%), as well… More >

Adnan Ahmad, Zhao Li^*, Irfan Tariq, Zhengran He

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 729-749, 2025, DOI:10.32604/cmc.2024.055768 - 03 January 2025

Abstract Micro-expressions (ME) recognition is a complex task that requires advanced techniques to extract informative features from facial expressions. Numerous deep neural networks (DNNs) with convolutional structures have been proposed. However, unlike DNNs, shallow convolutional neural networks often outperform deeper models in mitigating overfitting, particularly with small datasets. Still, many of these methods rely on a single feature for recognition, resulting in an insufficient ability to extract highly effective features. To address this limitation, in this paper, an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm (IDSSCNN-XgBoost) is introduced for ME… More >

Umapathi Krishnamoorthy^1,*, Shanmugam Jagan², Mohammed Zakariah³, Abdulaziz S. Almazyad^4,*, K. Gurunathan⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3903-3926, 2024, DOI:10.32604/cmc.2024.055910 - 19 December 2024

Abstract Brain signal analysis from electroencephalogram (EEG) recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings. In the current seizure detection and classification landscape, most models primarily focus on binary classification—distinguishing between seizure and non-seizure states. While effective for basic detection, these models fail to address the nuanced stages of seizures and the intervals between them. Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure… More >

Taha Yacine Trad^1,*, Kheireddine Choutri¹, Mohand Lagha¹, Souham Meshoul², Fouad Khenfri³, Raouf Fareh⁴, Hadil Shaiba⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4757-4786, 2024, DOI:10.32604/cmc.2024.055634 - 19 December 2024

Abstract The popularity of quadrotor Unmanned Aerial Vehicles (UAVs) stems from their simple propulsion systems and structural design. However, their complex and nonlinear dynamic behavior presents a significant challenge for control, necessitating sophisticated algorithms to ensure stability and accuracy in flight. Various strategies have been explored by researchers and control engineers, with learning-based methods like reinforcement learning, deep learning, and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control systems. This paper investigates a Reinforcement Learning (RL) approach for both high and low-level quadrotor control systems, focusing on attitude stabilization and position… More >

Jiaming Zhan^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.011189

Abstract Calcium fluoride (CaF₂) exhibits excellent optical properties, making it a promising candidate for preparing optical components. The actual applications underscore the importance of enhancing the ductile machining of such a difficult-to-machine material. This study starts by investigating the influence of thermal gradient fields on the mechanical behaviors of CaF₂ single crystal experimentally and theoretically, revealing the potential deformation mechanisms under various thermal additions. On this basis, a novel laser-assisted machining (LAM) scheme was proposed to enhance the deformability and machinability of CaF₂ single crystal by tailoring local thermal fields. The laser heating spot within the work material… More >