Zulfiqar Ali, Azhar Mahmood^*, Shaheen Khatoon, Seyed Ali Ghorashi

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084386 - 13 August 2026

Abstract Smart city systems increasingly depend on data analytics and visualization to support informed and timely decision making in complex urban environments. However, existing middleware solutions predominantly focus on data acquisition and communication, while analytical processing and visualization are typically delegated to external applications, resulting in increased development complexity, reduced reusability, and fragmented system architectures. This study presents analytics and visualization-centric middleware named “Service-Oriented Middleware for Smart City Applications” (SOMSCA), in which these capabilities are embedded directly within the middleware layer. SOMSCA adopts a service-oriented approach, exposing analytics and visualization functionalities as reusable platform services, and… More >

Carlos Rosa-Remedios^*, Pino Caballero-Gil^*, Jezabel Molina-Gil

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084352 - 13 August 2026

Abstract Increasing digitalization exposes critical infrastructure to sophisticated cyber threats, requiring new approaches to improving security and resilience. While classical machine learning techniques have shown promise in anomaly detection and threat mitigation, emerging quantum-inspired methods offer new opportunities to enhance detection capabilities by leveraging principles derived from quantum computing. The objective of this work is to propose a model for the early detection of Telephony Denial of Service attacks using a combination of classical algorithms and quantum computing-based techniques. Call records are embedded into a low-dimensional quantum feature space using spatial and temporal attributes, mapped through… More >

Jian Zhang¹, Seong-Min Cho², Seung-Hyun Seo^3,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084319 - 13 August 2026

Abstract Classical digital signature schemes such as RSA and ECDSA are threatened by the development of quantum computers, prompting the need for post-quantum cryptography (PQC). Among the various PQC candidates, multivariate quadratic (MQ) signature schemes have attracted significant attention due to their small signature size and efficient signing and verification. Evaluating these schemes is challenging because compactness, computational efficiency, and effective security are objectives that often conflict with one another and cannot usually be optimized simultaneously. In this work, we adapt a systematic multi-objective evaluation framework to MQ-based signature schemes in the NIST additional digital signature… More >

Tian Liu¹, Xichao Wang^1,*, Jun Wang², Song Gao², Hang Gao¹, Yuxi Liu¹, Yitao Zhuang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084300 - 13 August 2026

Abstract In unmanned aerial vehicle (UAV) flight behaviour understanding, the lack of a unified semantic representation for continuous multi-source temporal data makes it difficult to model flight events, behavioural relationships, and composite behaviours in an interpretable manner. To address this issue, this paper proposes a knowledge graph construction method for UAV flight behaviour understanding. The proposed method integrates atomic event detection, temporal knowledge modelling, and composite behaviour reasoning, thereby enabling the automatic transformation of continuous flight logs into structured behavioural knowledge. First, local statistical features, including smoothed velocity and robust climb rate, are extracted from multi-source… More >

Shunran Duan, Meijuan Yin^*, Xiangyang Luo, Lunchong Cui, Chenyu Wang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084290 - 13 August 2026

Abstract Few-shot relation classification aims to identify semantic relations between entity pairs under limited annotated data. Although recent prompt learning-based methods have achieved promising performance, they often rely on manually crafted, domain-specific prompt templates, which restrict their transferability across domains. In this paper, building upon the multi-task prompt transfer paradigm of MPT, we propose a Confidence-guided Orthogonality-constraint Prompt Adaptation framework for few-shot relation classification, named COPA. The proposed framework learns a shared domain-invariant prompt matrix together with domain-specific low-rank prompt matrices via multi-domain soft prompt tuning, enabling the transfer of domain-invariant relational knowledge across domains. Unlike… More >

Muhammad Mujahid¹, Fatima Alshannaq¹, Shaha Al-Otaibi², Tanzila Saba^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084273 - 13 August 2026

Abstract Cybersecurity threats in Internet of Things (IoT) networks have escalated, enabled by rapid advancements in wireless communication and edge computing technologies. These advancements expose networks to a wide range of sophisticated and evolving threats and increasingly complex research challenges. Traditional Intrusion Detection and Prevention Systems (IDS/IPS) often fail to provide reliable performance regarding the flexibility and scalability required to handle evolving attack patterns. This study proposes an APENet approach to detect cyberattacks from a real-world cybersecurity dataset, and incorporated a contextual dependency mechanism. The approach captures both local transition dependencies and global relational interactions within… More >

Nourah Fahad Janbi^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084270 - 13 August 2026

Abstract Automatic pain assessment from speech is an emerging approach for non-invasive and objective healthcare monitoring. However, many existing methods are evaluated on single datasets or under controlled conditions, which limits their robustness under diverse real-world conditions. This paper presents a lightweight, end-to-end deep learning framework for speech-based pain level classification that explicitly targets multi-dataset robustness assessment. The proposed approach uses log-Mel spectrograms with an EfficientNetV2B0 backbone to learn discriminative acoustic features without relying on handcrafted feature engineering or multi-stage pipelines. The model is evaluated on heterogeneous datasets, including clinical recordings, controlled experimental data, and a More >

Tuan Nguyen Kim^1,*, Son Doan Trung¹, Nguyen Minh Nhut Pham²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084268 - 13 August 2026

Abstract Static Windows Portable Executable (PE) malware detection remains a significant challenge due to the growing use of packing, obfuscation, and code reuse techniques, which gradually reduce the effectiveness of signature-based and manually engineered feature approaches. Recent deep learning models that operate directly on binary code or static features have achieved encouraging results; however, most still rely on global file-level representations. Such approaches are susceptible to noise introduced by padding or obfuscation and may overlook localized malicious regions. Moreover, many multi-view methods process different feature sources independently, lacking mechanisms to enforce semantic consistency across views. This… More >

Yunrui Bi^1,*, Qiliang Yang¹, Qinglin Ding¹, Bin Ran², Kun Liu¹, Mingjie Zhang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084267 - 13 August 2026

Abstract To improve regional traffic signal coordination under uncertain and dynamic traffic conditions, this paper proposes a hybrid Type-2 fuzzy reinforcement learning framework integrated with Beetle Antennae Search (BAS) and Deep Q-Network (DQN), named Type-2 fuzzy Beetle Antennae Search and Deep Q-Network (T2-BAS-DQN). In this framework, DQN remains active during online signal control, while the Type-2 fuzzy module provides uncertainty-aware correction for phase selection and green-time adjustment. BAS is used only in the offline training stage to optimize a low-dimensional parameter vector related to fuzzy correction, reward adjustment, and coordination pressure. A 3 × 3 Simulation… More >

Hsiao-Chun Han¹, Der-Chen Huang^2,*, Chin-Ling Chen^3,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084216 - 13 August 2026

Abstract Semiconductors represent the most complex production activities and stand at the forefront of smart manufacturing. In particular, the yield of advanced processes is strongly influenced by coupling among engineering systems, material behavior, and computational infrastructure. Consequently, the integration of deep learning (DL) and digital twins has become essential for driving the next-generation transformation of smart manufacturing. However, existing reviews predominantly organize literature through algorithm-oriented taxonomies, while isolated AI paradigms alone remain insufficient to effectively capture system-level interactions and industry-driven technological evolution. Therefore, this study proposes a system-oriented and industry-driven review framework, termed the System-under-Industry Guided… More >