Hashim Ali^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.087214 - 13 August 2026

Abstract Intensive care unit (ICU) time series are irregular, incomplete, and computationally demanding to model at high temporal resolution. Dense Transformer attention captures long-range dependencies but evaluates all pairwise interactions, including many stable or clinically weak measurements. This study presents Sparse Physio-Attention, a physiology-guided Transformer that retains critical-range violations, patient-relative deviations, informative missingness patterns, and task-relevant variables before sparse attention is computed. Dynamic routing subsequently removes weak attention edges, and a late-fusion adapter incorporates static electronic health record context. The analysis included 25,368 eligible MIMIC-IV ICU stays, of which 2740 were sepsis positive. On the held-out… More >

Mian Muhammad Kamal^1,*, Tianjun Ma^1,*, Mohammed K. Alzaylaee², Husam S. Samkari^3,4, Mohammed F. Allehyani³, Omar Almomani⁵, Heba G. Mohamed^6,7

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085412 - 13 August 2026

Abstract The cyber-physical network (CPS) combines sensing, communication, and control in physical processes, making them very susceptible to sophisticated cyber-attacks that may cause safety-critical effects. There are two core shortcomings to existing intrusion detection systems (IDS): generative-only models have little transparency of decision-making, while explainable-only models have low robustness in the presence of imbalanced and zero-day attacks. This paper presents a sequentially integrated trustworthy intrusion detection (ID) framework that combines generative learning and explainable AI (XAI) to boost robustness and transparency. The generative module enhances training data diversity, while the explainability module provides post-hoc interpretations during… More >

Bareera Anam, Muhammad Asim, Muhammad Nadeem Ali, Byung-Seo Kim^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084949 - 13 August 2026

Abstract The rapid evolution of communication networks, driven by the expansion of heterogeneous environments such as 6G, Internet of Things (IoT), and edge computing, has exposed a critical research gap in the lack of unified frameworks that jointly address intelligent network control and quantum-resilient security. Existing networking protocols were originally designed under static configurations and classical security assumptions, making them increasingly inadequate for dynamic, large-scale, and intelligent infrastructures exposed to quantum-enabled threats. At the same time, the emergence of Quantum Computing (QC) introduces severe security risks, as widely used cryptographic mechanisms supporting protocols such as Transport… More >

Ali Hassan¹, Syed Rizwan Hassan^2,*, Ammar Rafiq³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084672 - 13 August 2026

Abstract Artificial Intelligence of Things (AIoT) systems have emerged through the rapid integration of artificial intelligence (AI) and the Internet of Things (IoT), enabling intelligent sensing, distributed learning, and real-time decision-making across diverse application domains. However, this convergence also introduces a significantly expanded adversarial attack surface spanning sensing devices, communication networks, learning pipelines, and actuation environments. This paper presents a comprehensive systematic review of adversarial threats and defence mechanisms in AIoT systems using a novel 3D-AIoT-TT (Three-Dimensional AIoT Threat Taxonomy) framework. The proposed taxonomy jointly models three fundamental dimensions: (i) AI pipeline stages, (ii) IoT architectural… 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 >

Yi Wen Tan¹, Jun Meng Woh¹, Ee Sin Yong¹, Wai Leong Pang¹, Hui Hwang Goh¹, Kah Yoong Chan², Ari Happonen^3,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083418 - 13 August 2026

Abstract Along with the rapid advancement of Artificial Intelligence (AI), humanoid robots are foreseen to have great potential in the service industry, where human interaction is unavoidable. However, current systems face significant hurdles, including Field of View (FoV) problems, markerless real-time mimicry capabilities for humanoid’s fingers and arms. This study addresses these hurdles by developing an integrated hardware and software pipeline for the Unitree G1 Edu humanoid robot. A custom 3D-printed helmet and stabiliser interface were designed using FreeCAD and fabricated to house an external Orbbec Gemini 2 RGB-D sensor, optimising the FoV for frontal human-robot… More >

Navruzbek Habibullayev^1,*, Ilya Yurev^1,2, Maxim Molokeev^1,3,4, Aleksandr Aleksandrovsky^3,5, Olga Trofimova¹, Nikita Shulaev¹, Svetlana Volkova¹, Oleg Andreev^1,6

Chalcogenide Letters, Vol.23, No.7, 2026, DOI:10.32604/cl.2026.085815 - 07 August 2026

Abstract A novel quaternary selenide, EuErAgSe₃, has been synthesized for the first time. The phase forms upon annealing in the 1120–1800 K range and retains its crystal structure upon cooling to ambient conditions. The phase was prepared by the ampoule method from EuSe and AgErSe₂ powders, followed by annealing at 1270 ± 10 K for up to 300 h. Powder X-ray diffraction revealed that EuErAgSe₃ crystallizes in the AgBiS₂ structure type (space group Fm-3m) with the lattice parameter a = 5.95322(14) Å. The microhardness of the phase is 340 ± 15 HV. The optical band gap for direct transitions More >

Guangyu Xu^1,2, Yuxi Ban¹, Legend Zhang³, Junmin Lyu³, Feng Bao⁴, Wenfeng Zheng^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085484 - 27 July 2026

Abstract High-resolution remote sensing semantic segmentation is a fundamental task in Geospatial Artificial Intelligence (GeoAI). Existing CNN-based methods are effective for local and multiscale feature extraction but often lack progressive cross-scale semantic propagation, while attention- and Transformer-based methods improve global spatial modeling but generally ignore frequency-domain regularities. To address these limitations, this study proposes a Multiscale Long-Distance Feature Aggregation Network (MLFANet), a unified spatial-frequency segmentation framework for high-resolution remote sensing imagery. MLFANet introduces three key components: a Multiscale Global Dependency Extraction module for cascaded cross-scale contextual refinement, an FFT-based frequency-domain branch with learnable global filtering for… More >

Tanzila Saba¹, Muhammad Mujahid¹, Faten S. Alamri^2,*, Roaa Khalil Mohamed Ali Abed³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084816 - 27 July 2026

Abstract In the era of artificial intelligence, pattern recognition techniques have become fundamental in advancing medical image processing, diagnosis, and automated disease classification systems. Among various clinical challenges, breast cancer is the second most dangerous leading cause of death in women worldwide. Early and accurate detection of breast cancer is crucial to develop advanced diagnostic methods to control further loss or reduce mortality rates. This study proposes a dynamic graph multi-scale network for breast cancer diagnosis, integrated with multi-scale convolutional feature extraction, a squeeze-and-excitation block, and a graph convolutional network to jointly model local spatial features… More > Graphic Abstract

Imran Ahsan¹, Muhammad Waseem Anwar², JungYoon Kim³, Mucheol Kim^1,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.080382 - 27 July 2026

Abstract Graph neural networks (GNNs) have demonstrated promising results in enhancing machine learning and artificial intelligence techniques by addressing graph-related problems, including graph categorization, edge prediction, and node prediction. Despite the rapid growth of the field, a structured synthesis of recent application-oriented GNN research is necessary. This systematic literature review analyzes 363 peer-reviewed, application-oriented GNN studies published from 2019 through February 2026 from four scientific repositories. These studies are organized by GNN model variant, (including single and multimodel approaches), and four classification task categories: graph, link, node, and node and edge combined. In addition, the review… More >