Adeel Iqbal^1,#, Tahir Khurshaid^2,#, Syed Abdul Mannan Kirmani³, Mohammad Arif^4,*, Muhammad Faisal Siddiqui^5,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075819 - 12 March 2026

Abstract Vehicular Internet of Things (V-IoT) networks need intelligent and adaptive spectrum access methods for ensuring ultra-reliable and low-latency communication (URLLC) in highly dynamic environments. Traditional reinforcement learning (RL)-based algorithms, such as Q-Learning and Double Q-Learning, are often characterized by unstable convergence and inefficient exploration in the presence of stochastic vehicular traffic and interference. This paper proposes Adaptive Reinforcement Q-learning with Upper Confidence Bound (ARQ-UCB), a lightweight and reliability-aware RL framework, which explicitly reduces interruption and blocking probabilities while improving throughput and delay across diverse vehicular traffic conditions. This proposed ARQ-UCB algorithm extends the basic Q-updates More >

Nurbek Sissenov^1,*, Gulden Ulyukova^1,*, Dina Satybaldina², Nikolaj Goranin³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075504 - 12 March 2026

Abstract The reliability of information systems (IS) is a key factor in the sustainable operation of modern digital services. However, existing assessment methods remain fragmented and are often limited to individual indicators or expert judgments. This paper proposes a hybrid methodology for a comprehensive assessment of IS reliability based on the integration of the international standard ISO/IEC 25010:2023, multicriteria analysis methods (ARAS, CoCoSo, and TOPSIS), and the XGBoost machine learning algorithm for missing data imputation. The structure of the ISO/IEC 25010 standard is used to formalize reliability criteria and subcriteria, while the AHP method allows for… More >

Cuihong Wen¹, Jingjing Wen¹, Qinyue Zhang¹, Yeting Wen², Fanyong Cheng^3,*

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071911 - 27 February 2026

Abstract Air conditioning is a major energy-consuming component in buildings, and accurate air conditioning load forecasting is of great significance for maximizing energy utilization efficiency. However, the deep learning models currently used in the field of air conditioning load forecasting often suffer from issues such as distribution bias in load data and insufficient expression ability of nonlinear features in the model, which affect the accuracy of load forecasting. To address this, this paper proposes a novel load forecasting model. Firstly, the model employs the Dish-TS (DS) module to standardize the input window data through self-learning standardized… More >

Menwa Alshammeri^1,2,*, Noshina Tariq³, NZ Jhanji^4,5, Mamoona Humayun⁶, Muhammad Attique Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074627 - 29 January 2026

Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >

Hongsheng Su, Zhensheng Teng^*, Zihan Zhou

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.069495 - 27 January 2026

Abstract Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy, replacement-based maintenance practices that deviate from actual operational conditions, and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization, this study proposes a Time-Based Incomplete Maintenance (TBIM) strategy incorporating reliability constraints through stochastic differential equations (SDE). By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions, a high-precision SDE degradation model is constructed, achieving 16% residual reduction compared to… More >

Jiale Hu, Fan Chen^*, Yue Yang, Man Wang

Journal on Artificial Intelligence, Vol.8, pp. 51-64, 2026, DOI:10.32604/jai.2026.075257 - 22 January 2026

Abstract To enhance the operational performance of energy storage systems (ESS), this paper proposes an optimal dispatch strategy that jointly considers reliability and economic efficiency. First, we formulate a cost-minimization model that includes ESS dispatch costs, wind and photovoltaic (PV) curtailment costs, and load loss costs, while explicitly enforcing power supply reliability constraints. Next, we develop a comprehensive evaluation indicator system that integrates reliability, economic performance, renewable-energy utilization, and ESS technical indicators, thereby addressing the limitations of single-indicator assessments. Finally, a case study using real data from a region in China shows that the proposed strategy More >

Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >

Zhenhao Zhang^1,*, Yong Tian^1,2, Yuanzhi Cao¹, Tao Chen¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010846

Abstract Accurate calculation of the failure probability of structural components was crucial for full-probability level structural design. However, current design codes typically use uniform design factors, which fail to accurately reflect the true failure probability of structures. In this paper, based on the direct probability integral method (DPIM) and combining different design parameter iterative calculation strategies, the full-probabilistic design methods for single-parameter and multi-parameter were proposed, and their accuracy advantages in structural reliability design were verified by engineering examples. Furthermore, this study compares the partial factor method, the design value method, the direct probability design method,… More >

Sewwandi Tennakoon¹, Eric Danso¹, Zhenjie Zhao^2,*

Journal on Artificial Intelligence, Vol.7, pp. 517-547, 2025, DOI:10.32604/jai.2025.072628 - 01 December 2025

Abstract Generative Artificial Intelligence (GenAI) systems have achieved remarkable capabilities across text, code, and image generation; however, their outputs remain prone to errors, hallucinations, and biases. Users often overtrust these outputs due to limited transparency, which can lead to misuse and decision errors. This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability. We introduce a methodology that adjusts explanations dynamically according to user expertise, model output confidence, and contextual risk factors, providing guidance that is informative but not overwhelming. The framework was evaluated using outputs… More >

Qiuyu Lu¹, Yunqi Yan², Yang Liu¹, Ying Chen^2,*, Yinguo Yang¹, Tannan Xiao³, Guobing Wu¹

Energy Engineering, Vol.122, No.12, pp. 4799-4814, 2025, DOI:10.32604/ee.2025.069131 - 27 November 2025

Abstract The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system (ECS) designs that prioritize lifetime operational reliability. Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints. This paper introduces a novel, two-stage optimization framework for offshore wind farm (OWF) ECS planning that systematically incorporates reliability. The first stage employs Mixed-Integer Linear Programming (MILP) to determine an optimal radial network topology, considering linearized reliability approximations and geographical constraints. The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained More >