Leonidas Theodorakopoulos, Alexandra Theodoropoulou^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082270 - 15 June 2026

Abstract Auditable autonomy is becoming a practical requirement for deploying large language model (LLM) agents in operational workflows where recommendations can trigger consequential actions. Many autonomy claims remain hard to evaluate because studies emphasize task completion or fluent explanations while underreporting tool privileges, verification conditions, rollback feasibility, and trace completeness. This review develops a decision-making–centered framework that treats autonomy as an auditable engineering property. It introduces a three-plane big data foundation: an evidence plane with provenance and freshness constraints; a decision-trace plane that records retrieval identifiers, tool invocations, intermediate checks, and policy evaluations; and an outcomes More >

Ahmad Almufarreh¹, Rogaia Hassan Osman Hassan^2,3, Ashfaq Ahmad⁴, Muhammad Arshad^2,5,*, Choo Wou Onn⁶

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082264 - 15 June 2026

Abstract The explosive increase in connectivity has multiplied the volume and speed of network traffic, putting the world at greater risk from sophisticated and emerging cyber-attacks. Smart learning environments, which rely on cloud-based learning management systems, virtual classrooms, and interconnected educational devices, generate large volumes of dynamic network traffic that must be continuously monitored to protect sensitive academic data and ensure uninterrupted learning services. In this study, three supervised machine learning classifiers, namely Random Forest, Logistic Regression, and k-Nearest Neighbours (kNN), are designed and evaluated for anomaly detection using the UNSW-NB15 benchmark. Models are trained and… More >

Yu Zhang^1,2,#, Shihan Tan^1,#, Guangyao Lian², Congying Dun³, Qiwei Hu^1,*, Chiming Guo^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081931 - 15 June 2026

Abstract Gearboxes are critical components in the transmission systems of various mechanical equipment. Subjected to complex and harsh operating conditions for a long time, they suffer from a high failure rate and potentially severe consequences. Traditional fault diagnosis methods are limited by problems such as noise interference, and can hardly meet the requirements in terms of diagnostic accuracy, generalization ability, and reliability. To tackle the deficiencies of traditional gearbox fault diagnosis methods, including insufficient utilization of features, poor generalization under small-sample conditions, and weak model interpretability, this paper proposes a fault diagnosis method based on multi-dimensional… More >

Zhihao Zhang^1,#, Zhuodong Liu^1,#, Xiangyu Li², Lei Zhang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081922 - 15 June 2026

Abstract Financial fraud detection across institutions faces a fundamental tension between the need for diverse training data and regulatory prohibitions on sharing sensitive records. Existing federated learning approaches suffer from performance degradation under non-IID distributions and substantial utility losses when uniform differential privacy is applied to inherently sparse fraud signals. To this end, this paper proposes HiFraud, a hierarchical federated framework featuring three key components: fraud-aware dynamic clustering with complementarity regularization to group institutions by fraud pattern similarity while preserving rare-type representation; star-chain knowledge transfer augmented by not-true-class distillation to propagate novel fraud patterns rapidly within… More >

Vipin Kumar (विपिन कुमार)^1,*, Pushpendra Kumar²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081791 - 15 June 2026

Abstract In this paper, we report the effects of point defects and doping on the physical properties of the two-dimensional OsI₂ monolayer. A point defect was created by removing a single Os/I atom from the perfect crystal lattice of the OsI₂ monolayer. For doping, we use an isovalent Ru element from the transition-metal family. Point defects and doping alter the band structure by creating new localized electronic states within the gap. Moreover, the electronic bands show a shift due to point defects. However, changes in the bandgap due to point defects and doping are not remarkable. This… More >

Anping Wan^1,2,3, Yingchang Gao^1,3, Weikang Liu¹, Rui Yin¹, Khalil Al-Bukhaiti^1,3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081719 - 15 June 2026

Abstract Vertical roller mills are essential for energy-intensive grinding in cement, minerals, and metallurgy industries, consuming up to 50% of plant electricity and frequently experiencing operational instabilities (including excessive vibration and main motor current fluctuations) that drive unplanned downtime, increased wear, and reduced throughput. Despite their importance, real-time autonomous optimization remains challenging due to the nonlinear interactions among grinding pressure, feed rate, separator speed, and aerodynamic factors, which limit traditional control strategies under varying loads. This paper presents a real-time operational optimization system for large-scale vertical roller mills using big industrial data and artificial intelligence (AI).… More >

Haotian Cao^1,2, Qingsheng Zhu^1,2,3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081695 - 15 June 2026

Abstract Point Cloud Registration (PCR) is a basic task in computer vision, mobile robotics, and autonomous driving. PCR primarily faces challenges, including insufficient registration performance in low-overlap scenarios and high computational resource consumption in large-scale point cloud scenarios. Most recent PCR methods are transformer-based. Methods like transformers have quadratic computational complexity More >

Kang-Woo Lee, Dong-Hee Lee^*, Dae-Il Kwon^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081667 - 15 June 2026

Abstract Early-life cycle-life prediction for lithium-ion batteries—estimating end-of-life from initial cycles—is valuable for rapid cell screening and battery health management. We investigate whether an explicit correlation-structure descriptor can complement physics-informed ΔQ-based indicators and generic early-cycle statistical features on the Severson 124-cell benchmark. We develop a lightweight hybrid framework that combines ΔQ-based health indicators, data-driven statistical features, and Laplacian Eigenmaps embeddings derived from a Pearson-correlation feature graph, with XGBoost used as the predictor. Across five feature configurations (ΔQ Only, ΔQ + Statistics, Hybrid Append, VIF + Laplacian, and Integrated Laplacian), we evaluate pointwise regression accuracy using RMSE and R² together… More > Graphic Abstract

Taghreed Alkhodaidi^1,*, Wadee Alhalabi¹, Miada Almasre²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081665 - 15 June 2026

Abstract The rapid growth of the IoMT has resulted in critical security threats to healthcare infrastructure, which require highly sophisticated IDSs that can detect a wide range of and unbalanced attack patterns. This study has addressed a critical challenge faced by network security data, which is class imbalance, by presenting a comprehensive evaluation of data balancing techniques on both a real-world standard data set, CICIoMT2024, and a synthetic data set, SynIoMT2026, which we generated to mimic the characteristics of the standard data set for developing a highly controlled data set. Three data balancing techniques, ADASYN, Sample… More >

Geunhwi Park¹, Juneyoung Park^2,*, Chunjoo Yoon³, Jaehong Park³

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081652 - 15 June 2026

Abstract Urban flooding caused by extreme rainfall events disrupts transportation systems, yet generating realistic flood-traffic scenarios for disaster preparedness remains a labor-intensive manual process. This study proposes a Knowledge Graph (KG)-driven pipeline that automatically generates domain-specific training data for fine-tuning small language models (sLLMs) to synthesize urban flood-traffic scenarios. A domain KG comprising 58 entities and 285 relationships was constructed for Jinju City, South Korea, integrating empirical flood data from 112 local documents with quantitative rainfall-traffic impact values from 14 international studies. Nine domain constraint rules, including a novel spatial consistency rule, ensure the physical plausibility… More >