Baofeng Duan¹, Xinghai Yu¹, Peng Wang², Tao Feng¹, Yongbo Jiang^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086743 - 15 September 2026

Abstract With the sustained expansion of complex Internet of Things (IoT) ecosystems, malicious traffic detection has become critical for maintaining both cyber security and operational continuity. Modern IoT deployments contain heterogeneous devices, ubiquitous sensing layers, edge services, and autonomous assets, so abnormal communication may affect not only data confidentiality but also physical operations. To address the limitations of independent flow-level detection and heavy graph propagation, this paper proposes a Lightweight Graph-Attentive Network for Traffic Detection (LGNT). LGNT constructs a directed traffic-interaction graph from NetFlow records, where communication entities are represented as nodes and traffic sessions are… More >

Rongfa Chen^1,*, Liping Chen², Xiuzhe Meng¹, Daniel Zeng^1,2,3

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086702 - 15 September 2026

Abstract Existing fake news detection methods largely rely on single-source datasets, leading models to overfit platform-specific features and perform poorly on heterogeneous multi-source data. Even with the emergence of Large Language Models (LLMs), our benchmarks show that general-purpose LLMs still struggle to identify deceptive intent when source-specific context is unavailable. To address unseen-source-group generalization, we propose SHIELD (Social Heterogeneous Interaction Embedding for Latent Deception). SHIELD models interaction patterns shared across sources rather than relying only on isolated text features or semantic inference. Specifically, we construct a Social Reaction-Aware Heterogeneous Interaction Graph to capture consistencies and discrepancies… More >

Rihem Farkh¹, Ghislain Oudinet², Alaeddine Moussa³, Yasser Fouad^4,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086607 - 15 September 2026

Abstract Autonomous driving systems must reason not only about the current scene but also about how the environment may evolve under alternative actions. Although predictive world models can generate future latent rollouts, these rollouts are often consumed directly by planners or explanation modules without an explicit and auditable interpretation stage. This paper presents a predictive and explainable driving framework centered on a Future Interpretation Module (FIM), which transforms action-conditioned future rollouts into structured descriptors, including risk trend, peak risk, time-to-critical, minimum clearance, predicted collision, dominant predicted event, and confidence. An aligned latent interface, trained with feature-alignment… More >

Fen Liu^1,*, Jinghua Zhang², Weijie Tan³

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086571 - 15 September 2026

Abstract Multi-modal information fusion integrates data from various sensors, distinct sources, or different modalities, such as audio, images, and text, to achieve a more comprehensive and accurate understanding and analysis. This paper proposes a Semi-Tensor Product-based Block Term Decomposition of Multilinear (STP-BTDM) pooling method and applies it to sentiment analysis and emotion recognition. Unlike prior factorized multilinear approaches, STP-BTDM introduces block-term decomposition with a block-diagonal core tensor, yielding a globally sparse yet locally dense structure and enabling modality-specific independent subspace learning. The technique first introduces the Semi-Tensor Product-based Block Term Decomposition (STP-BTD) model to obtain globally… More >

Mohammed Alonazi¹, Muhammad Adeel Ahmed Tahir², Adnan Ahmed Rafique², Maha Abdelhaq³, Raed Alsaqour⁴, Ahmad Jalal^5,6, Jeongmin Park^7,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086550 - 15 September 2026

Abstract Accurate vehicle detection, tracking, and counting are fundamental components of Intelligent Transportation Systems (ITS) and urban traffic surveillance. However, real-world deployment remains challenging due to domain shifts, illumination variations, occlusions, dense traffic conditions, and heterogeneous data distributions. Existing studies often address detection, tracking, and counting as independent tasks, resulting in limited cross-domain generalization and inconsistent performance in complex traffic environments. To overcome these limitations, this paper proposes a unified cross-domain framework that jointly integrates vehicle detection, tracking, and lane-aware counting within a single intelligent traffic analytics pipeline. The proposed framework begins with image enhancement using… More >

Shengxuan Dong, Xiongwei Li^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086535 - 15 September 2026

Abstract To address the conflict between navigation performance and safety constraints in safe reinforcement learning, this paper proposes Dual Stream-Analytic Hierarchy Process-Safe Soft Actor (DS-AHP-SAC), a safe soft actor-critic algorithm based on a dual-stream Q-network and dynamic Analytic Hierarchy Process (AHP) stratified experience replay. The algorithm achieves a balance between reward maximization and constraint satisfaction through three synergistic designs: (1) decoupling the Q-network into independent navigation and safety value streams to eliminate gradient interference at the Critic level and mitigate gradient competition at the Actor level; (2) constructing a three-criterion dynamic sampling strategy based on AHP, More >

Haipeng Hu¹, Tao Hong², Junjie Zhu³, Xinjie Yao^4,*, Zhoupeng Guo^5,*, Dahai Xia^6,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086475 - 15 September 2026

Abstract Cold-start alloy-design campaigns often have too few labeled compositions to reliably locate phase boundaries for tree-structured composition-space Gaussian process regression (TCGPR). We study a controlled way to incorporate external CALPHAD-like boundary information into this partitioning step. The proposed MP-TCGPR method adds a Gaussian MAP penalty centered on a thermodynamic boundary estimate and uses an adaptive width ${\sigma }_j(N)={\sigma }_0\sqrt{1+N/N_{\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{s}\mathrm{s}}}$ to reduce prior influence as node-level data accumulate. The revised theory distinguishes asymptotic convergence from convergence rate: a fixed-width prior is also asymptotically negligible under local regularity, whereas the adaptive schedule accelerates finite-sample prior More >

Chuanlin Jian¹, Yuanchen Sun², Xiangcheng Liu¹, Xuming Huang³, Samaneh Beheshti Kashi⁴, Xing Hu^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086441 - 15 September 2026

Abstract Heterogeneous clusters now carry most artificial-intelligence, scientific-computing, cloud, and edge-assisted workloads, yet scheduling them well remains difficult: workload semantics, hardware capability, queue state, and energy behavior are tightly coupled. Existing schedulers, whether heuristic, learning-based, or built on reinforcement learning, tend to rely on coarse resource requests, overlook the compatibility between task types and node classes, or carry a heavy training and deployment cost. This paper presents Classification-Aware Scheduling for Heterogeneous clusters (CASH), a confidence-calibrated, classification-aware scheduling framework that integrates workload profiling, deep feature crossing, gradient-boosted boundary modeling, and risk-aware heterogeneous resource mapping. CASH constructs a… More >

Yalong Liang¹, Xiaohui Yuan¹, Yuning Han², Pei Li^3,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086403 - 15 September 2026

Abstract NBT-based lead-free piezoceramics are promising alternatives to Pb-containing materials, yet their functional properties arise from complex and coupled composition–processing–structure–property relationships. Here, we develop a structure-informed machine learning framework to predict and interpret the piezoelectric coefficient d₃₃, depolarization temperature T_d, and relative dielectric permittivity ε_r. A database of 214 records from 34 publications was compiled, including 204 records for model development and 10 records for independent literature validation. The correlation analysis involved 38 variables, including 35 candidate input descriptors and three target properties. After Pearson correlation-based redundancy filtering, 30 nonredundant input descriptors were retained for model development. To… More >

Hui Zhang¹, Mangang Xie^1,*, Baozhen An², Jing Wei¹

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086401 - 15 September 2026

Abstract Peak age of information (PAoI) and energy consumption (EC) are conflicting yet critical metrics in mobile edge computing (MEC)-assisted vehicular networks. Most existing studies overlook the joint effects of sensing, transmission, and computation. The main contributions of this work are threefold. First, we derive novel analytical expressions for the average PAoI and average EC under all three strategies, explicitly accounting for the energy and delay costs across the entire data processing chain. Second, we demonstrate that the partial computation offloading strategy is superior, effectively balancing the low latency of local processing with the high power More >