Shounak Mandal¹, Subhadip Pati^1,#, Nirmallyadeb Ray^1,#, Bipasha Guha Roy^2,#, Priyanka Saha³, Deepsubhra Guha Roy^2,*

Digital Engineering and Digital Twin, Vol.4, pp. 27-47, 2026, DOI:10.32604/dedt.2026.081859 - 14 August 2026

Abstract HealthyBrain is a scalable, interoperable, and intelligent Remote Patient Monitoring (RPM) platform built on Internet of Things (IoT) technologies and a modular microservices architecture. The system integrates wearable IoT devices, MQTT (Message Queuing Telemetry Transport)-based lightweight messaging, and high-throughput real-time data streaming via Apache Kafka. Edge-side preprocessing enables low-latency analytics, while machine learning-based anomaly detection models facilitate early identification of critical health events. To ensure clinical interoperability, the platform adheres to the HL7 FHIR (Fast Healthcare Interoperability Resources) standard for electronic health record exchange. The system’s novel contribution lies in the unified integration of edge… More >

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 >

Bayan Alabdullah¹, Muhammad Waqas Ahmed², Mohammad Shorfuzzaman^3,*, Jasem Almotiri⁴, Mohammed Alonazi⁵, Ahmad Jalal^6,7,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086985 - 13 August 2026

Abstract Multimodal data fusion and deep learning have opened new frontiers in the analysis of complex visual data acquired from heterogeneous sensing systems. Flood inundation mapping represents one of the most demanding applications in this domain, requiring robust interpretation of complementary but conflicting image modalities under severe real-world constraints. This paper presents CAG-Transformer, a novel multimodal AI architecture for bi-temporal flood change detection through intelligent fusion of Sentinel-1 SAR and Sentinel-2 multispectral imagery. Three tightly integrated contributions address the core challenges of heterogeneous multimodal image analysis. A Change Attention Gate (CAG) performs adaptive channel-wise representation learning,… More >

Caiyun Li^1,#, Xiaowen Liu^1,#, Binhui Tang^1,*, Tingting Lu², Daibo Xiao³, Li Chen³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086522 - 13 August 2026

Abstract Unmanned aerial vehicle (UAV) communication links in low-altitude operations support mission control, status feedback, and data transmission. Abnormal communication states may affect mission continuity and risk response. However, conventional anomaly detection usually determines only whether a communication anomaly exists, making it difficult to support risk-state assessment and alert-threshold decision-making. To address this problem, this paper proposes Risk Stratification and Task-cost-aware Decision via Knowledge Distillation (RSTD-KD), a task-cost-aware UAV communication risk warning approach that integrates risk stratification, probability calibration, and threshold decision-making. RSTD-KD aggregates fine-grained communication records into window-level behavior samples, constructs low-, medium-, and high-risk… More >

Hantian Zhang¹, Wentai Wu^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086343 - 13 August 2026

Abstract Large language models (LLMs) are increasingly used in retrieval-augmented generation (RAG) systems, where they are expected to answer questions based on retrieved evidence. In many cases, however, the right behavior is not to answer. A model should abstain when the evidence is insufficient, irrelevant, or contradictory. Existing evaluations mainly focus on final-answer accuracy, and they often pay less attention to whether models can recognize evidence quality before responding. To study this problem, we propose the Evidence Sufficiency Benchmark, a five-level benchmark for evaluating answer-abstention calibration. The benchmark covers evidence conditions from L1 Full Support to… More >

Xiruo Chen, Qi Ouyang^*, Sihong Meng, Yuke Meng

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086341 - 13 August 2026

Abstract Visual simultaneous localization and mapping (VSLAM) is a key technology for mobile robotics, autonomous driving, and embodied intelligence, enabling self-localization, environment reconstruction, and scene understanding. Although conventional geometric methods have achieved notable success, their performance often degrades in challenging conditions, such as low-texture scenes, severe illumination changes, dynamic interference, and long-term environmental variations. Recent advances in deep learning have created new opportunities to improve VSLAM through stronger feature representations, learned priors, semantic perception, and emerging map representations. At the same time, the increasing adoption of learning-based modules has raised important questions about integration strategies, generalization,… More >

Mukesh Dalal^1,*, Anterpreet Kaur Bedi¹, Payal Mittal²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086270 - 13 August 2026

Abstract Autonomous navigation poses a key challenge in Artificial Intelligence (AI), necessitating agents to plan and execute actions in complex, partially visible surroundings. Simultaneous Localization and Mapping (SLAM) facilitates autonomous navigation of robots and vehicle objects to construct an unfamiliar environment map while concurrently monitoring their inside position. This systematic review investigates the nascent convergence of agentic AI, defined by goal-oriented autonomy, with adaptive decision-making and reasoning, with SLAM-based navigation systems. This paper utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, which concentrated on peer-reviewed articles published in (2017–2026), particularly in SLAM-based intelligent… More >

Yuqing Cao, Xiliang Chen^*, Legui Zhang^*, Jun Lai, Haoyang Dong, Xuefei Sun, Xiaoyan Wang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086261 - 13 August 2026

Abstract The design of reward functions is crucial to the success of reinforcement learning, yet the process often relies on expert experience and is difficult to debug. Although large language models (LLMs) offer new opportunities for automated reward design, existing methods still face challenges such as poor interpretability, inability to reuse knowledge, and optimization blindness. To address these issues, this paper proposes a method for structural decoupling and knowledge reuse evolution, referred to as SD-KRE. Its core lies in treating the reward function as a composition of multiple structured units with clear semantics and functionally decoupled… More >

Milad Moradi^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086229 - 13 August 2026

Abstract Large Language Models (LLMs) have rapidly evolved into general-purpose systems with broad applicability across information access, reasoning, decision support, and human-computer interaction. Their growing deployment, however, has intensified concerns regarding safety, alignment, and robustness, especially as these models become integrated with external tools, retrieval systems, and increasingly agentic workflows. This review provides an analytical overview of the principal risks, technical advances, evaluation practices, and future directions in this area. It first clarifies the conceptual foundations of safety, alignment, robustness, and reliability in the context of LLMs. It then examines the major risk categories associated with More >

Jia Chen¹, Zhicheng Liao¹, Mengdi Hou², Jianbo Huang^1,3,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086222 - 13 August 2026

Abstract Fly ash (FA) blended 3D printed concrete (3DPC) offers improved sustainability but requires strength prediction models validated at the mix-composition level rather than within familiar formulations. This study applies leave-one-mix-out (LOMO) cross-validation to benchmark eight machine learning algorithms on 126 experimental records spanning seven FA-blended 3DPC compositions (FA 0–15 wt.%, W/B 0.30–0.35, age 1–28 days). ExtraTrees and ElasticNet achieve the highest composition-level generalisation for compressive strength (CS, R2=0.786±0.253) and flexural strength (FS, R2=0.915±0.061, RMSE =0.301 MPa), respectively. SHAP analysis identifies FA replacement percentage as the dominant CS predictor (mean |SHAP|=3.92 MPa, negative… More >