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  • Open Access

    ARTICLE

    Painted Wolf Optimization: A Novel Nature-Inspired Metaheuristic Algorithm for Real-World Optimization Problems

    Saeid Sheikhi*

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077788 - 12 March 2026

    Abstract Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems, yet existing methods often struggle with balancing exploration and exploitation across diverse problem landscapes. This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization (PWO) algorithm. The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves, also known as African wild dogs in the wild, particularly their unique consensus-based voting rally mechanism, a behavior fundamentally distinct from the social dynamics of grey wolves. In this innovative process, pack members explore different areas… More >

  • Open Access

    ARTICLE

    Retrieval-Augmented Large Language Model for AWS Cloud Threat Detection and Modelling: Cloudtrail Mitre ATT&CK Mapping

    Goodness Adediran1, Kenny Awuson-David2, Yussuf Ahmed1,*

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077606 - 12 March 2026

    Abstract Amazon Web Services (AWS) CloudTrail auditing service provides detailed records of operational and security events, enabling cloud administrators to monitor user activity and manage compliance. Although signature-based threat detection methods have been enhanced with machine learning and Large Language Models (LLMs), these approaches remain limited in addressing emerging threats. This study evaluates a two-step Retrieval Augmented Generation (RAG) approach using Gemini 2.5 Pro to enhance threat detection accuracy and contextual relevance. The RAG system integrates external cybersecurity knowledge sources including the MITRE ATT&CK framework, AWS Threat Technique Catalogue, and threat reports to overcome limitations of… More >

  • Open Access

    ARTICLE

    In-Mig: Geographically Dispersed Agentic LLMs for Privacy-Preserving Artificial Intelligence

    Mohammad Nauman*

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077259 - 12 March 2026

    Abstract Large Language Models (LLMs) are increasingly utilized for semantic understanding and reasoning, yet their use in sensitive settings is limited by privacy concerns. This paper presents In-Mig, a mobile-agent architecture that integrates LLM reasoning within agents that can migrate across organizational venues. Unlike centralized approaches, In-Mig performs reasoning in situ, ensuring that raw data remains within institutional boundaries while allowing for cross-venue synthesis. The architecture features a policy-scoped memory model, utility-driven route planning, and cryptographic trust enforcement. A prototype using JADE for mobility and quantized Mistral-7B demonstrates practical feasibility. Evaluation across various scenarios shows that In-Mig achieves More >

  • Open Access

    ARTICLE

    Optimizing Routing Algorithms for Next-Generation Networks: A Resilience-Driven Framework for Space-Air-Ground Integrated Networks

    Peiying Zhang1,2, Yihong Yu1,2, Jia Luo3,4,*, Nguyen Gia Ba5, Lizhuang Tan6,7, Lei Shi8

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076690 - 12 March 2026

    Abstract Next-Generation Networks (NGNs) demand high resilience, dynamic adaptability, and efficient resource utilization to enable ubiquitous connectivity. In this context, the Space-Air-Ground Integrated Network (SAGIN) architecture is uniquely positioned to meet these requirements. However, conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics, such as its heterogeneous structure, dynamic topology, and constrained resources, leading to suboptimal performance under disruptions such as node failures or cyberattacks. To meet these demands for SAGIN, this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation. Methodologically, we define three core routing performance metrics,… More >

  • Open Access

    ARTICLE

    ECSA-Net: A Lightweight Attention-Based Deep Learning Model for Eye Disease Detection

    Sara Tehsin1,*, Muhammad John Abbas2, Inzamam Mashood Nasir1, Fadwa Alrowais3, Reham Abualhamayel4, Abdulsamad Ebrahim Yahya5, Radwa Marzouk6

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076515 - 12 March 2026

    Abstract Globally, diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness. To treat these vision disorders effectively, proper diagnosis must occur in a timely manner, and with conventional methods such as fundus photography, optical coherence tomography (OCT), and slit-lamp imaging, much depends on an expert’s interpretation of the images, making the systems very labor-intensive to operate. Moreover, clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices. To solve these problems, we have developed the Efficient Channel-Spatial Attention Network (ECSA-Net), a new deep learning-based… More >

  • Open Access

    ARTICLE

    Q-ALIGNer: A Quantum Entanglement-Driven Multimodal Framework for Robust Fake News Detection

    Sara Tehsin1,*, Inzamam Mashood Nasir1, Wiem Abdelbaki2, Fadwa Alrowais3, Reham Abualhamayel4, Abdulsamad Ebrahim Yahya5, Radwa Marzouk6

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076514 - 12 March 2026

    Abstract The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise, adversarial manipulation, and semantic inconsistency between modalities. Existing multimodal fake news detection approaches often rely on deterministic fusion strategies, which limits their ability to model uncertainty and complex cross-modal dependencies. To address these challenges, we propose Q-ALIGNer, a quantum-inspired multimodal framework that integrates classical feature extraction with quantum state encoding, learnable cross-modal entanglement, and robustness-aware training objectives. The proposed framework adopts quantum formalism as a representational abstraction, enabling probabilistic modeling of multimodal alignment… More >

  • Open Access

    ARTICLE

    Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer’s Disease

    Pablo Guillén1, Enrique Frias-Martinez2,*

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076400 - 12 March 2026

    Abstract Alzheimer’s disease (AD) diagnosis and prognosis increasingly rely on machine learning (ML) models. Although these models provide good results, clinical adoption is limited by the need for technical expertise and the lack of trustworthy and consistent model explanations. SHAP (SHapley Additive exPlanations) is commonly used to interpret AD models, but existing studies tend to focus on explanations for isolated tasks, providing little evidence about their robustness across disease stages, model architectures, or prediction objectives. This paper proposes a multi-level explainability framework that measures the coherence, stability and consistency of explanations by integrating: (1) within-model coherence… More >

  • Open Access

    ARTICLE

    Multi-Scale Modelling and Simulation of Graphene–PDMS and CNT–PDMS Flexible Capacitive Pressure Sensors for Enhanced Sensitivity

    Rama Gautam1,*, Nikhil Marriwala1, Reeta Devi1, Dhariya Singh Arya2

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076136 - 12 March 2026

    Abstract In this study, the multi-scale (meso and macro) modelling was used to predict the electric response of the material. Porosity was introduced through a sugar-templating process to enhance compressibility and sensitivity. Mean-field homogenization was employed to predict the electrical conductivity of the nanocomposites, which was validated experimentally through IV characterisation, confirming stable Ohmic behavior. The homogenised material parameters were incorporated into COMSOL Multiphysics to simulate diaphragm deflection and capacitance variation under applied pressure. Experimental results showed a linear and stable capacitance response at the force magnitude of 0–7 N. The Graphene nanoplatelets (GnP)–Polydimethylsiloxane (PDMS) sensor demonstrated More >

  • Open Access

    ARTICLE

    Automating the Initial Development of Intent-Based Task-Oriented Dialog Systems Using Large Language Models: Experiences and Challenges

    Ksenia Kharitonova1, David Pérez-Fernández2, Zoraida Callejas1,3, David Griol1,3,*

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075777 - 12 March 2026

    Abstract Building reliable intent-based, task-oriented dialog systems typically requires substantial manual effort: designers must derive intents, entities, responses, and control logic from raw conversational data, then iterate until the assistant behaves consistently. This paper investigates how far large language models (LLMs) can automate this development. In this paper, we use two reference corpora, Let’s Go (English, public transport) and MEDIA (French, hotel booking), to prompt four LLM families (GPT-4o, Claude, Gemini, Mistral Small) and generate the core specifications required by the rasa platform. These include intent sets with example utterances, entity definitions with slot mappings, response templates,… More >

  • Open Access

    ARTICLE

    GaitMAFF: Adaptive Multi-Modal Fusion of Skeleton Maps and Silhouettes for Robust Gait Recognition in Complex Scenarios

    Zhongbin Luo1,2, Zhaoyang Guan3, Wenxing You2, Yunteng Wang2, Yanqiu Bi4,5,*

    CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075704 - 12 March 2026

    Abstract Gait recognition is a key biometric for long-distance identification, yet its performance is severely degraded by real-world challenges such as varying clothing, carrying conditions, and changing viewpoints. While combining silhouette and skeleton data is a promising direction, effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem. This paper introduces GaitMAFF, a novel Multi-modal Adaptive Feature Fusion Network, to address this challenge. Our approach first transforms discrete skeleton joints into a dense Skeleton Map representation to align with silhouettes, then employs an attention-based module to dynamically More >

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