Li-Woei Chen¹, Kun-Lin Tsai^2,*, Fang-Yie Leu³, Chao-Tung Yang^3,4,5, Wei-Zong Liang²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083880 - 27 July 2026

Abstract Unmanned aerial vehicle (UAV) swarm networks are increasingly deployed in surveillance, disaster response, and intelligent transportation systems, where secure and efficient communication is critical under resource-constrained environments. However, conventional public-key-based security mechanisms introduce excessive computational overhead, while standalone intrusion detection systems are insufficient to defend against dynamic and multi-vector attacks in swarm networks. To address these challenges, in this paper, a lightweight time-indexed secure communication framework with intrusion detection modeling (TSCID) is proposed for resource-constrained UAV swarm networks. The proposed TSCID integrates a time-indexed session key derivation mechanism with lightweight authenticated encryption to ensure confidentiality,… More >

Abeer S. Hadad¹, Fahman Saeed², Adeeb A. Ahmed^3,*, Jiangbin Zheng¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083873 - 27 July 2026

Abstract Loop invariant inference is fundamental to program verification, yet it remains particularly challenging for multipath loops, where different execution paths may exhibit incompatible behaviors across feasible executions. In such settings, invariants that are both sound and sufficiently precise often require disjunctive forms, whose automatic inference remains difficult. This paper presents an efficient, path-sensitive, counterexample-guided framework for automated loop invariant inference. Our approach leverages a Path Dependency Automaton (PDA) to systematically decompose the semantics of multipath loops by modeling feasible execution paths independently. Building on this decomposition, we introduce a localized, path-guided Counterexample-Guided Invariant Refinement (CEGIR) More >

Alireza Norouzpour Shahrbejari¹, Nafiseh Pishbin², Mohammad Reza Maghami^3,*, Mazlan Mohamed^4,*, Mohammad Golmohammad¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083763 - 27 July 2026

Abstract This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting, sizing, and scenario-based operation of energy storage systems (ESSs) in renewable-integrated distribution networks. The proposed model concurrently addresses technical, economic, and reliability objectives—minimizing active power losses (PL), voltage deviation (VD), expected energy not supplied (EENS), and short-circuit level (SCL), while maximizing voltage sensitivity index (VSI) and power-loss sensitivity factor (PLSF). A Particle Swarm Optimization (PSO) algorithm with weighted-sum scalarization is employed to solve this complex, nonlinear optimization problem and effectively balance the conflicting operational goals. The framework is validated using IEEE… More >

Sahil Tripathi¹, Manaswi Kulahara², Abdul Khader Jilani Saudagar³, Hatoon S. AlSagri^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083755 - 27 July 2026

Abstract Spatial reasoning, defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding. However, existing works such as Bidirectional Encoder Representations from Transformers (BERT)-based spatial Question Answering (QA) models and neuro-symbolic models rely on dataset-specific patterns, leading to shortcut learning, where reliance on superficial lexical cues rather than true relational understanding. Recent Large Language Models (LLMs)-based works, including fine-tuning and Chain-of-Thought (CoT) prompting, partially alleviate shortcut learning but remain limited by non-causal reasoning, where predictions depend on spurious correlations rather than stable relational structure. To address these… More >

Yeog Kim, Changhoon Lee, Kiwook Sohn^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083669 - 27 July 2026

Abstract With the widespread adoption of encryption protocols, payload-based traffic analysis has become increasingly infeasible, posing significant challenges for intrusion detection systems (IDS). Consequently, AI-based approaches for encrypted traffic analysis have gained substantial attention. However, existing studies are often evaluated using inconsistent criteria, including heterogeneous attack labels, behavioral representations, and model architectures, making systematic comparison difficult. To address this limitation, this paper proposes a three-level analytical taxonomy for encrypted traffic analysis, structured around attack objectives (Level 1), observable network behaviors (Level 2), and detection models (Level 3). The proposed framework provides a structured perspective for analyzing… More >

Weiqiang Jin^1,2,#, Yang Liu^2,#, Yang Gao^1,#, Shixiang Tang², Yanghao Zhou³, Jinhu Qi⁴, Wentao Zhang⁴, Junli Wang⁵, Jing Gao², Yue Ma⁴, Ziwei Zhang^1,*, Biao Zhao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083586 - 27 July 2026

Abstract Large Vision-Language Models (LVLMs) have achieved strong performance in multimodal perception, understanding, and generation, but their ability to perform complex logical reasoning remains insufficiently understood. In particular, it is still unclear whether current LVLMs can reliably conduct explicit logical operations, multi-step inference, abstract relational reasoning, and cross-modal evidence integration. Reasoning abilities such as deductive, inductive, abductive, multi-hop, and causal inference are fundamental to robust decision making, trustworthy interaction, and real-world deployment, yet they have not been systematically examined in the LVLM literature. Existing surveys mainly discuss mathematical reasoning, general multimodal intelligence, or benchmark progress, but… More >

Kiran Asma, Muhammad Asif Zahoor Raja^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083532 - 27 July 2026

Abstract The accelerated digital transformation of critical infrastructure has yielded unprecedented system interconnectivity, enhancing operational efficiency, simultaneously expanding the epidemiological propagation surface in heterogeneous networks. A novel machine learning-driven neuroarchitecture is designed in the present study, leveraging multilayer autoregressive exogenous neural networks (ARXNNs) iteratively trained with the Levenberg Marquardt (LM) algorithm, i.e., ARXNNs-LM, to address the intricate temporal dynamics of nonlinear fractional epidemiological computer virus propagation in the networks. The proposed ARXNNs-LM methodology effectively models the dynamic state transitions between susceptible, infected, and recovered systems. The dataset is synthesized through the application of the Grünwald–Letnikov (GL)… More >

Changwei Song¹, Jiaqi Qiang², Hongjun Liu¹, Jianqiang Li¹, Hui Pan², Qing Zhao^1,*, Jiuzuo Huang³, Shi Chen³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083525 - 27 July 2026

Abstract Cushing’s syndrome (CS) is a rare endocrine disorder characterized by chronic hypercortisolism, and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach. However, existing diagnostic models suffer from two core bottlenecks: inefficient fusion of deep semantic features and clinical prior features, and insufficient multi-view facial feature disentanglement without CS-specific pathophysiological constraints. To address these limitations, we propose a novel Multi-View Facial Feature Disentanglement Network (MVFFD-Net) for high-precision automatic CS diagnosis. The network takes five standard facial views (frontal, bilateral 45∘ oblique, and bilateral 90∘ lateral views) as input, with three key innovations:… More >

Emrah Altun^1,*, Christophe Chesneau², Atacan Erdis¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083459 - 27 July 2026

Abstract Modeling bounded response variables is an important problem in computational statistics, especially in applications involving skewed, heavy-tailed data. In such cases, the modal regression is a robust alternative to traditional mean-based modeling approaches. In this study, a new bounded distribution, called the extended Bradford distribution, is proposed as a flexible extension of the classical Bradford distribution. By incorporating an additional shape parameter, the corresponding model can capture various shape structures, such as left and right skewness, increasing, and bathtub hazard shapes. The new distribution provides an explicit expression for the mode, making it suitable for More >

Ki-Young Shin¹, Soonmo Kwon², Kyudong Park^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083410 - 27 July 2026

Abstract Multimodal AI systems often suffer from “over-informing”, where excessive raw visual input introduces noise that distracts from task-relevant decisions. Motivated by selective human attention strategies, we propose ARS-MMT (Attention and Reasoning through Source Sentences for Multimodal Machine Translation), an architecture that operationalizes a “look-and-think” pipeline: a source-language encoder first builds contextualized linguistic representations, a relation reasoning network then produces a query-conditioned visual channel, and a multimodal decoder generates the translation conditioned in parallel on the encoded text and on this visual channel. We quantify the contribution of the visual modality through a controlled ablation: zeroing… More >