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 >

Rashid Ali^1,*, Alaa Omran Almagrabi^2,3

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070224 - 12 January 2026

Abstract Wi-Fi technology has evolved significantly since its introduction in 1997, advancing to Wi-Fi 6 as the latest standard, with Wi-Fi 7 currently under development. Despite these advancements, integrating machine learning into Wi-Fi networks remains challenging, especially in decentralized environments with multiple access points (mAPs). This paper is a short review that summarizes the potential applications of federated reinforcement learning (FRL) across eight key areas of Wi-Fi functionality, including channel access, link adaptation, beamforming, multi-user transmissions, channel bonding, multi-link operation, spatial reuse, and multi-basic servic set (multi-BSS) coordination. FRL is highlighted as a promising framework for More >

Le Wang¹, Bing Xu^1,*, Peng Liu², En Yuan¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-14, 2026, DOI:10.32604/cmc.2025.070794 - 09 December 2025

Abstract Deep learning has been recognized as an effective method for indoor positioning. However, most existing real-valued neural networks (RVNNs) treat the two constituent components of complex-valued channel state information (CSI) as real-valued inputs, potentially discarding useful information embedded in the original CSI. In addition, existing positioning models generally face the contradiction between computational complexity and positioning accuracy. To address these issues, we combine graph neural network (GNN) with complex-valued neural network (CVNN) to construct a lightweight indoor positioning model named CGNet. CGNet employs complex-valued convolution operation to directly process the original CSI data, fully exploiting… More >

Xin Ma^1,2, Jin Lei^3,4,*, Chenying Pei⁴, Chunming Wu⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069353 - 09 December 2025

Abstract This study proposes a lightweight apple detection method employing cascaded knowledge distillation (KD) to address the critical challenges of excessive parameters and high deployment costs in existing models. We introduce a Lightweight Feature Pyramid Network (LFPN) integrated with Lightweight Downsampling Convolutions (LDConv) to substantially reduce model complexity without compromising accuracy. A Lightweight Multi-channel Attention (LMCA) mechanism is incorporated between the backbone and neck networks to effectively suppress complex background interference in orchard environments. Furthermore, model size is compressed via Group_Slim channel pruning combined with a cascaded distillation strategy. Experimental results demonstrate that the proposed model More >

Binglei Yue, Aili Jiang, Chun Yang, Junwei Lei, Heng Liu, Yin Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.071047 - 10 November 2025

Abstract With the growing advancement of wireless communication technologies, WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution. Among the available signal types, Channel State Information (CSI) offers fine-grained temporal, frequency, and spatial insights into multipath propagation, making it a crucial data source for human-centric sensing. Recently, the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments. This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI. We first outline mainstream CSI acquisition tools and their hardware specifications, More >

Seyong Jin¹, Muhammad Fayaz², L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667 - 10 November 2025

Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… 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 >

Omar Darwish¹, Shorouq Al-Eidi², Abdallah Al-Shorman¹, Majdi Maabreh³, Anas Alsobeh⁴, Plamen Zahariev⁵, Yahya Tashtoush^6,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.068266 - 10 November 2025

Abstract Covert timing channels (CTC) exploit network resources to establish hidden communication pathways, posing significant risks to data security and policy compliance. Therefore, detecting such hidden and dangerous threats remains one of the security challenges. This paper proposes LinguTimeX, a new framework that combines natural language processing with artificial intelligence, along with explainable Artificial Intelligence (AI) not only to detect CTC but also to provide insights into the decision process. LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely. LinguTimeX demonstrates strong effectiveness in detecting CTC across… More >

Shuo Wang^1,2,*, Huiming Wang^1,2, Ying Zhang^1,2, Zhiqiang Zhang^1,3, Li Jia^1,2

Frontiers in Heat and Mass Transfer, Vol.23, No.6, pp. 1721-1740, 2025, DOI:10.32604/fhmt.2025.073226 - 31 December 2025

Abstract This experimental investigation was conducted on the flow boiling performance of refrigerant R134a in two types of parallel microchannels: sintered porous microchannels (PP-MCs) and smooth parallel microchannels (SP-MCs). The tests were performed under controlled conditions including an inlet subcooling of 5 ± 0.2°C, saturation temperature of 33°C, mass fluxes of 346 and 485 kg/m²·s, and a range of heat fluxes. Key findings reveal that the sintered porous microstructure significantly enhances bubble nucleation, reducing the wall superheat required for the onset of nucleate boiling (ONB) to only 0.13°C compared to 2.2°C in smooth channels. The porous structure… More >

Ghinwa Al Mimar¹, Natrah Kamaruzaman^1,*, Kamil Talib Alkhateeb²

Frontiers in Heat and Mass Transfer, Vol.23, No.6, pp. 1933-1956, 2025, DOI:10.32604/fhmt.2025.070118 - 31 December 2025

Abstract The pivotal role microchannels play in the thermal management of electronic components has, in recent decades, prompted extensive research into methods for enhancing their heat transfer performance. Among these methods, surface wettability modification was found to be highly effective owing to its significant influence on boiling dynamics and heat transfer mechanisms. In this study, we modified surface wettability using a nanocomposite coating composed of graphene nano plate (GNPs) and multi-walled carbon nanotubes (MWCNT) and then examined how the modification affected the transfer of boiling heat in microchannels. The resultant heat transfer coefficients for hydrophilic and… More >