Amal Al-Rasheed¹, Rahim Khan^2,*, Fahad Alturise³, Salem Alkhalaf⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064102

Abstract The Internet of Things (IoT) is a smart infrastructure where devices share captured data with the respective server or edge modules. However, secure and reliable communication is among the challenging tasks in these networks, as shared channels are used to transmit packets. In this paper, a decision tree is integrated with other metrics to form a secure distributed communication strategy for IoT. Initially, every device works collaboratively to form a distributed network. In this model, if a device is deployed outside the coverage area of the nearest server, it communicates indirectly through the neighboring devices.… More >

Xiaoguang Lv^1,2, Tao Liu^1,2,*, Han Qin^1,2, Ying Guo^1,2, Jingshan Pan^1,2, Dawei Zhao^1,2, Xiaoming Wu^1,2, Meihong Yang^1,2

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063852

Abstract The Dynamical Density Functional Theory (DDFT) algorithm, derived by associating classical Density Functional Theory (DFT) with the fundamental Smoluchowski dynamical equation, describes the evolution of inhomogeneous fluid density distributions over time. It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems. The Sunway Bluelight II supercomputer, as a new generation of China’s developed supercomputer, possesses powerful computational capabilities. Porting and optimizing industrial software on this platform holds significant importance. For the optimization of the DDFT algorithm, based on the Sunway Bluelight II supercomputer and the unique hardware architecture… More >

Ruibo Wei^1,2, Yao Kong³, Mengzhao Li^1,2, Feng Liu^1,2,*, Fang Cheng^4,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063722

Abstract To address uncertainties in satellite orbit error prediction, this study proposes a novel ensemble learning-based orbit prediction method specifically designed for the BeiDou navigation satellite system (BDS). Building on ephemeris data and perturbation corrections, two new models are proposed: attention-enhanced BPNN (AEBP) and Transformer-ResNet-BiLSTM (TR-BiLSTM). These models effectively capture both local and global dependencies in satellite orbit data. To further enhance prediction accuracy and stability, the outputs of these two models were integrated using the gradient boosting decision tree (GBDT) ensemble learning method, which was optimized through a grid search. The main contribution of this More >

Longgang Zhao¹, Seok-Won Lee^1,2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063691

Abstract This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities, and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency. The method in this paper adopts the event sequence segmentation technique, combines location awareness with time interval reasoning, and improves human activity recognition through ontology reasoning. Compared with the existing methods, the framework performs better when dealing with uncertain data and complex scenes, and the experimental results show that its recognition accuracy is improved by 15.6% and processing time is reduced by 22.4%. More >

Ahmed Ben Atitallah^1,*, Jannet Kamoun^2,3, Meshari D. Alanazi¹, Turki M. Alanazi⁴, Mohammed Albekairi¹, Khaled Kaaniche¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063634

Abstract Breast Cancer (BC) remains a leading malignancy among women, resulting in high mortality rates. Early and accurate detection is crucial for improving patient outcomes. Traditional diagnostic tools, while effective, have limitations that reduce their accessibility and accuracy. This study investigates the use of Convolutional Neural Networks (CNNs) to enhance the diagnostic process of BC histopathology. Utilizing the BreakHis dataset, which contains thousands of histopathological images, we developed a CNN model designed to improve the speed and accuracy of image analysis. Our CNN architecture was designed with multiple convolutional layers, max-pooling layers, and a fully connected… More >

Taher Alzahrani^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063448

Abstract The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,… More >

Lingyun Xiang¹, Hang Fu¹, Chunfang Yang^2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062834

Abstract In the Visual Place Recognition (VPR) task, existing research has leveraged large-scale pre-trained models to improve the performance of place recognition. However, when there are significant environmental differences between query images and reference images, a large number of ineffective local features will interfere with the extraction of key landmark features, leading to the retrieval of visually similar but geographically different images. To address this perceptual aliasing problem caused by environmental condition changes, we propose a novel Visual Place Recognition method with Cross-Environment Robust Feature Enhancement (CerfeVPR). This method uses the GAN network to generate similar… More >

Jun Wang^1,2, Chaoren Ge^1,2, Yihong Li^1,2, Huimin Zhao^1,2, Qiang Fu^1,2,*, Kerang Cao^1,2, Hoekyung Jung^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062094

Abstract Network Intrusion Detection System (NIDS) detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments. To improve the detection capability of minority-class attacks, this study proposes an intrusion detection method based on a two-layer structure. The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic, majority class attacks, and merged minority class attacks. The second layer further segments the minority class attacks through Stacking ensemble learning. The datasets are selected from the generic network dataset CIC-IDS2017, NSL-KDD, and the… More >

Prashanth B. S^1,*, Manoj Kumar M. V.^2,*, Nasser Almuraqab³, Puneetha B. H⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060881

Abstract Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models are built on the assumption of a static learning environment, but in practical situations, the data generated by the process is dynamic. This evolution of the data is termed concept drift. This research paper presents an approach for predicting mechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment. The method proposed here is applicable… More >

Nila Cecília Faria Lopes Medeiros^1,2, Gisele Amaral-Labat¹, Leonardo Iusuti de Medeiros^1,2, Alan Fernando Ney Boss¹, Beatriz Carvalho da Silva Fonseca¹, Manuella Gobbo de Castro Munhoz³, Guilherme F. B. Lenz e Silva³, Mauricio Ribeiro Baldan¹, Flavia Lega Braghiroli^4,*

Journal of Renewable Materials, DOI:10.32604/jrm.2025.02025-0017

Abstract Biochar, a carbon-rich material derived from the thermochemical conversion of biomass under oxygen-free conditions, has emerged as a sustainable resource for radar-absorbing technologies. This study explores the production of activated biochars from end-of-life wood panels using a scalable and sustainable physical activation method with CO₂ at different temperatures, avoiding the extensive use of corrosive chemicals and complex procedures associated with chemical or vacuum activation. Compared to conventional chemically or vacuum-activated biochars, the physically activated biochar demonstrated competitive performance while minimizing environmental impact, operational complexity, and energy consumption. Furthermore, activation at 750°C reduces energy consumption by 14%… More > Graphic Abstract