Jincheng Cai¹, Li Feng^1,*, Ni Zhao²

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086183 - 15 September 2026

Abstract Federated Variational Autoencoders (VAEs) keep interaction data local, but existing federated VAE recommenders typically apply uniform KL regularization and do not adapt dimension-wise penalties to unreliable posteriors in sparse interaction scenarios. We propose $\alpha$-FedVAE, which uses a detached, clipped normalized signal-to-noise ratio as a local confidence gate for each KL dimension of a fused user posterior, without extra communication. Across MovieLens-100K, MovieLens-1M, and Amazon Video, $\alpha$-FedVAE improves mean HR@20 by 7.8%–55.3% and NDCG@20 by 8.0%–65.8% over FedDAE. These results indicate that $\alpha$-FedVAE improves personalized recommendation under sparse and decentralized settings while preserving the communication More >

Rania Al-Ali¹, Mustafa Al-Fayoumi^1,2, Saleem Alsaraireh^3,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085806 - 15 September 2026

Abstract The rapid growth of the Internet of Things (IoT) and edge computing has increased the demand for secure and lightweight image encryption suitable for resource-constrained environments. This paper proposes a hybrid framework combining a residual-based pretrained autoencoder with a novel Cylinder XOR-Cascade (CXC) encryption scheme. The autoencoder compresses images into a compact latent representation while a residual branch preserves fine spatial details for accurate reconstruction. Both representations are encrypted using CXC, a two-pass column-wise stream cipher that enhances confusion and diffusion through sequential SHA3-256-based chaining and a cylinder-like feedback mechanism. Experiments on the USC-SIPI dataset More >

Mohammed Saad Javeed¹, Jannatul Maua², Muhammad Firoz Mridha³, Hashibul Ahsan Shoaib⁴, Taro Suzuki⁵, Jungpil Shin^5,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.082491 - 15 September 2026

Abstract As artificial intelligence becomes increasingly embedded in financial systems, ensuring the security and robustness of these models is critical, particularly in sensitive tasks like credit card fraud detection. Despite their predictive success, deep learning models remain vulnerable to adversarial examples: subtly manipulated inputs that can mislead classification outcomes. Unlike existing approaches that typically rely on either adversarial training or standalone input filtering, this paper proposes a unified dual-defense framework that jointly integrates adversarial training with a denoising autoencoder (DAE)-based filtering mechanism, specifically designed for imbalanced tabular financial data under adversarial conditions. Using a real-world, imbalanced… More >

Ziyi Ju¹, Ping Yu¹, Rui Song¹, Tonglin Chen^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082060 - 23 July 2026

Abstract In modern high-performance chip design, achieving timing closure is essential to design success. With the increasing scale and complexity of modern chips, timing-driven placement has become increasingly important. Traditional placement methods primarily focus on minimizing wirelength, but lack timing optimization, making it difficult to meet the strict timing closure requirements of modern designs. Therefore, developing an efficient timing-driven placement method has become a critical challenge in modern chip design. This paper presents a novel timing-driven placement framework that integrates a variational graph autoencoder (VGAE) with a nonlinear mixed-size placement optimizer. The framework identifies timing-violation paths More >

Junchen Huo¹, Song Wang^1,*, Enqing Chen¹, Yingqiang Ding¹, Shouyi Yang²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081658 - 23 July 2026

Abstract Multi-modal 3D object detection, which leverages the complementary strengths of LiDAR point clouds and camera RGB images, has emerged as a critical component of 3D perception in autonomous driving. As a critical challenge in multi-modal learning, modality alignment aims to establish accurate semantic correspondences across distinct modalities. However, existing methods encounter significant difficulties in achieving robust alignment when data from one modality is obscured, such as in the presence of object occlusion or adverse environmental conditions, including illumination variations and inclement weather. To alleviate this issue, we present CG-MAE, a dual-branch Bird’s-Eye-View (BEV) masked autoencoder… More >

Edward Danso Ansong¹, David Adlai Nettey^1,*, Sarika S², Simon Bonsu Osei¹

Journal on Big Data, Vol.8, pp. 1-10, 2026, DOI:10.32604/jbd.2026.065126 - 12 June 2026

Abstract Credit card fraud has emerged as a pervasive threat, impacting financial institutions and individuals as online banking and payment methods become increasingly integral to daily life. Despite efforts to mitigate this problem through measures like passwords and two-factor authentication, financial institutions continue to suffer substantial losses, often amounting to millions of dollars. Traditional machine learning solutions, developed and trained as supervised learning models, have failed to address this issue effectively. In anomaly detection, such as credit card fraud detection, the available training datasets are vast but inherently imbalanced, posing a formidable obstacle for supervised learning… More >

Zafer Serin^1,*, Cihan Karakuzu², Uğur Yüzgeç²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079254 - 27 May 2026

Abstract This study proposes SWAGE-3D (Spectral Wasserstein Attention Generative Ensemble), an enhanced 3D-VAE-GAN framework for single-view 3D object reconstruction using voxel-based representations. The proposed model integrates RGB-D encoding, Wasserstein adversarial learning with hybrid Lipschitz regularization, and a self-attention–augmented generator to improve structural coherence and training stability. By combining variational latent modeling with stabilized Wasserstein optimization, the framework aims to address common challenges in 3D generative modeling, including mode collapse, unstable convergence, and insufficient global consistency. The encoder employs a depth-aware feature extraction strategy, while the discriminator utilizes a hybrid spectral normalization and gradient penalty mechanism to More > Graphic Abstract

Akash Garg^*

Journal on Artificial Intelligence, Vol.8, pp. 273-298, 2026, DOI:10.32604/jai.2026.079401 - 12 May 2026

Abstract As cyber threats continue to evolve in scale and sophistication, the need for intelligent and adaptive security mechanisms has become increasingly urgent. Intrusion Detection Systems (IDS) are critical components in safeguarding computer networks from malicious activities. This review paper presents a comprehensive analysis of recent advancements in deep learning-based IDS, examining various architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs). The study compares traditional intrusion detection techniques with modern deep learning approaches, highlighting their strengths, limitations, and suitability for real-world deployment. Special attention is given to… More >

Liping Wang¹, Jiang Wu^1,2,*, Liang Wang³

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078591 - 08 May 2026

Abstract The rapid growth of networked systems and the increasing diversity of cyberattack behaviors have posed significant challenges to intrusion detection, particularly in scenarios characterized by high-dimensional features and severe class imbalance. Conventional detection approaches based on handcrafted rules or shallow representations often exhibit limited robustness under such conditions. To address these issues, this paper presents a hybrid deep learning framework for network intrusion detection that integrates complementary feature learning mechanisms within a dual-branch architecture. Specifically, a Transformer branch is employed to model long-range temporal dependencies in network traffic, while a convolutional neural network branch (CNN)… More >

Nader Karmous¹, Leila Bousbia¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077454 - 08 May 2026

Abstract This study proposes an intelligent Intrusion Detection and Prevention System (IDPS) integrated into a centralized Ryu Software-Defined Networking (SDN) controller to mitigate replay attacks within Internet of Things (IoT) environments. To address the scarcity of specialized datasets, a comprehensive dataset was generated using a real-time SDN-IoT testbed encompassing Mininet, multiple OpenFlow 1.3 switches, and a single Ryu controller. The experimental setup featured the exchange of legitimate and malicious Message Queuing Telemetry Transport (MQTT) traffic between hosts and IoT devices to simulate realistic network behaviors and attack vectors. Our methodology introduces a novel feature engineering framework… More >