Nikhil S. Mane¹, Sheetal Kumar Dewangan^2,*, Sayantan Mukherjee³, Pradnyavati Mane⁴, Deepak Kumar Singh¹, Ravindra Singh Saluja⁵

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.072090 - 10 November 2025

Abstract The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids. Researchers rely on experimental investigations to explore nanofluid properties, as it is a necessary step before their practical application. As these investigations are time and resource-consuming undertakings, an effective prediction model can significantly improve the efficiency of research operations. In this work, an Artificial Neural Network (ANN) model is developed to predict the thermal conductivity of metal oxide water-based nanofluid. For this, a comprehensive set of 691 data points was collected from the literature. This dataset is split More >

Lijuan Huang¹, Xianyi Liu², Jinping Liu^2,*, Pengfei Xu^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.068818 - 10 November 2025

Abstract The ubiquity of mobile devices has driven advancements in mobile object detection. However, challenges in multi-scale object detection in open, complex environments persist due to limited computational resources. Traditional approaches like network compression, quantization, and lightweight design often sacrifice accuracy or feature representation robustness. This article introduces the Fast Multi-scale Channel Shuffling Network (FMCSNet), a novel lightweight detection model optimized for mobile devices. FMCSNet integrates a fully convolutional Multilayer Perceptron (MLP) module, offering global perception without significantly increasing parameters, effectively bridging the gap between CNNs and Vision Transformers. FMCSNet achieves a delicate balance between computation… More >

Abdelouahab Benseddik^1,*, Djamel Daoud¹, Ahmed Badji^1,2, Hocine Bensaha¹, Tarik Hadibi^3,5, Yunfeng Wang⁴, Li Ming⁴

Energy Engineering, Vol.122, No.12, pp. 5021-5037, 2025, DOI:10.32604/ee.2025.072991 - 27 November 2025

Abstract In Saharan climates, greenhouses face extreme diurnal temperature fluctuations that generate thermal stress, reduce crop productivity, and hinder sustainable agricultural practices. Passive thermal storage using Phase Change Materials (PCM) is a promising solution to stabilize microclimatic conditions. This study aims to evaluate experimentally and numerically the effectiveness of PCM integration for moderating greenhouse temperature fluctuations under Saharan climatic conditions. Two identical greenhouse prototypes were constructed in Ghardaïa, Algeria: a reference greenhouse and a PCM-integrated greenhouse using calcium chloride hexahydrate (CaCl₂·6H₂O). Thermal performance was assessed during a five-day experimental period (7–11 May 2025) under severe ambient conditions.… More > Graphic Abstract

Ashwini Bhat¹, Nagaraj N. Katagi¹, M. C. Gowrishankar², Manjunath Shettar^2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2715-2728, 2025, DOI:10.32604/cmc.2025.069842 - 23 September 2025

Abstract This research explores the water uptake behavior of glass fiber/epoxy composites filled with nanoclay and establishes an Artificial Neural Network (ANN) to predict water uptake percentage from experimental parameters. Composite laminates are fabricated with varying glass fiber and nanoclay contents. Water absorption is evaluated for 70 days of immersion following ASTM D570-98 standards. The inclusion of nanoclay reduces water uptake by creating a tortuous path for moisture diffusion due to its high aspect ratio and platelet morphology, thereby enhancing the composite’s barrier properties. The ANN model is developed with a 3–4–1 feedforward structure and learned… More >

Tamara Zhukabayeva^1,2,*, Vasily Desnitsky³, Assel Abdildayeva^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2591-2625, 2025, DOI:10.32604/cmes.2025.067142 - 31 August 2025

Abstract Wireless Sensor Networks (WSN) have gained significant attention over recent years due to their extensive applications in various domains such as environmental monitoring, healthcare systems, industrial automation, and smart cities. However, such networks are inherently vulnerable to different types of attacks because they operate in open environments with limited resources and constrained communication capabilities. The paper addresses challenges related to modeling and analysis of wireless sensor networks and their susceptibility to attacks. Its objective is to create versatile modeling tools capable of detecting attacks against network devices and identifying anomalies caused either by legitimate user… More >

Xinchao Han^1,2, Aojun Zhang^1,2, Runchuan Li^1,2,*, Shengya Shen³, Di Zhang^1,2, Bo Jin^1,2, Longfei Mao^1,2, Linqi Yang^1,2, Shuqin Zhang^1,2

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3443-3465, 2025, DOI:10.32604/cmc.2024.059403 - 17 February 2025

Abstract Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, More >

Qi Fei^1,2,*, Haojun Hu³, Guisheng Yin¹, Zhian Sun²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3251-3279, 2025, DOI:10.32604/cmc.2024.058931 - 17 February 2025

Abstract Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy… More >

Shiren Ye, Qi Meng, Shuo Zhang, Hui Wang^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2415-2429, 2025, DOI:10.32604/cmc.2024.058250 - 17 February 2025

Abstract WCE (Wireless Capsule Endoscopy) is a new technology that combines computer vision and medicine, allowing doctors to visualize the conditions inside the intestines, achieving good diagnostic results. However, due to the complex intestinal environment and limited pixel resolution of WCE videos, lesions are not easily detectable, and it takes an experienced doctor 1–2 h to analyze a complete WCE video. The use of computer-aided diagnostic methods, assisting or even replacing manual WCE diagnosis, has significant application value. In response to the issue of intestinal lesion detection in WCE videos, this paper proposes a multi-scale feature… More >

Jiachang Liu^1,*, Zhengwei Huang², Junfeng Xiang¹, Lu Liu¹, Manlin Hu¹

Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

Xionglve Li, Chengyu Wang, Yifan Yang, Changsheng Hou, Bingnan Hou, Zhiping Cai^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 53-78, 2024, DOI:10.32604/cmc.2024.055186 - 15 October 2024

Abstract The global Internet is a complex network of interconnected autonomous systems (ASes). Understanding Internet inter-domain path information is crucial for understanding, managing, and improving the Internet. The path information can also help protect user privacy and security. However, due to the complicated and heterogeneous structure of the Internet, path information is not publicly available. Obtaining path information is challenging due to the limited measurement probes and collectors. Therefore, inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths. The purpose of this survey is to provide an overview… More >