Monalisa Jena¹, Noman Khan^2,*, Mi Young Lee^3,*, Seungmin Rho³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075119 - 29 January 2026

Abstract Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises. When such risks go undetected, consequences can escalate to self-harm, long-term disability, reduced productivity, and significant societal and economic burden. Despite recent advances, detecting risk from online text remains challenging due to heterogeneous language, evolving semantics, and the sequential emergence of new datasets. Effective solutions must encode clinically meaningful cues, reason about causal relations, and adapt to new domains without forgetting prior knowledge. To address these challenges, this paper presents a Continual Neuro-Symbolic Graph… More >

Afnan Alhindi^*, Saad Al-Ahmadi, Mohamed Maher Ben Ismail

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072625 - 12 January 2026

Abstract Split Learning (SL) has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency. Specifically, neural networks are divided into client and server sub-networks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices, thereby making SL particularly suitable for resource-constrained devices. Although SL prevents the direct transmission of raw data, it does not alleviate entirely the risk of privacy breaches. In fact, the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data. Moreover,… 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 >

Haoran Wang¹, Shuhong Yang², Kuan Shao², Tao Xiao², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069404 - 10 November 2025

Abstract With the rapid development of the Artificial Intelligence of Things (AIoT), convolutional neural networks (CNNs) have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks. However, the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices. Therefore, this paper proposes an efficient privacy-preserving CNN framework (i.e., EPPA) based on the Fully Homomorphic Encryption (FHE) scheme for AIoT application scenarios. In the plaintext domain, we verify schemes with different activation structures to determine the… More >

Fatemeh Mobasheri¹, Masoud Hosseinpoor^1,*, Ammar Yahia^1,2, Farhad Pourkamali-Anaraki³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3309-3347, 2025, DOI:10.32604/cmes.2025.072271 - 23 December 2025

Abstract Self-consolidating concrete (SCC) is an important innovation in concrete technology due to its superior properties. However, predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes. This study combines machine learning (ML) models with conformal prediction (CP) to address these issues, offering prediction intervals that quantify uncertainty and reliability. A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods, including Random Forest (RF), Gradient Boosting Regressor (GBR), Extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM), along with CP techniques, More >

Binu Sudhakaran Pillai¹, Raghavendra Kulkarni², Venkata Satya Suresh kumar Kondeti², Surendran Rajendran^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1141-1166, 2025, DOI:10.32604/cmes.2025.070348 - 30 October 2025

Abstract Future 6G communications will open up opportunities for innovative applications, including Cyber-Physical Systems, edge computing, supporting Industry 5.0, and digital agriculture. While automation is creating efficiencies, it can also create new cyber threats, such as vulnerabilities in trust and malicious node injection. Denial-of-Service (DoS) attacks can stop many forms of operations by overwhelming networks and systems with data noise. Current anomaly detection methods require extensive software changes and only detect static threats. Data collection is important for being accurate, but it is often a slow, tedious, and sometimes inefficient process. This paper proposes a new… More >

Shuoshuo Shen^1,2, Jin Cheng^1,2,*, Zhenyu Liu², Jianrong Tan^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-2, 2025, DOI:10.32604/icces.2025.011752

Abstract Motion stability assessment of industrial robots subject to complex dynamic properties and multi-source uncertainties in open environments registers an important yet challenging task [1–5]. To tackle this task, this study proposes a new reliability-based motion stability analysis method for industrial robots, which incorporates the moment-based method and Bayesian inference-guided probabilistic model updating strategy. To start with, the comprehensive motion system model of industrial robots is established by integrating the control, drive, and multi-body motion models. The reliability-based stability model of industrial robots is presented considering the uncertainty of parameters. Subsequently, the fractional exponential moments are… More >

Lingqiu Zeng¹, Peibing Sa¹, Qingwen Han², Lei Ye^2,*, Letian Yang¹, Cheng Zhang¹, Jiqiang Cheng²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2059-2086, 2025, DOI:10.32604/cmc.2025.060392 - 16 April 2025

Abstract Congestion control is an inherent challenge of V2X (Vehicle to Everything) technologies. Due to the use of a broadcasting mechanism, channel congestion becomes severe with the increase in vehicle density. The researchers suggested reducing the frequency of packet dissemination to relieve congestion, which caused a rise in road driving risk. Obviously, high-risk vehicles should be able to send messages timely to alarm surrounding vehicles. Therefore, packet dissemination frequency should be set according to the corresponding vehicle’s risk level, which is hard to evaluate. In this paper, a two-stage fuzzy inference model is constructed to evaluate More >

Magdy Nagy^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 185-223, 2025, DOI:10.32604/cmes.2025.061865 - 11 April 2025

Abstract In this present work, we propose the expected Bayesian and hierarchical Bayesian approaches to estimate the shape parameter and hazard rate under a generalized progressive hybrid censoring scheme for the Kumaraswamy distribution. These estimates have been obtained using gamma priors based on various loss functions such as squared error, entropy, weighted balance, and minimum expected loss functions. An investigation is carried out using Monte Carlo simulation to evaluate the effectiveness of the suggested estimators. The simulation provides a quantitative assessment of the estimates accuracy and efficiency under various conditions by comparing them in terms of More >

Muhammad Arif^1,*, Muhammad Rashid²

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 13-64, 2025, DOI:10.32604/cmc.2025.062819 - 26 March 2025

Abstract Edge Machine Learning (EdgeML) and Tiny Machine Learning (TinyML) are fast-growing fields that bring machine learning to resource-constrained devices, allowing real-time data processing and decision-making at the network’s edge. However, the complexity of model conversion techniques, diverse inference mechanisms, and varied learning strategies make designing and deploying these models challenging. Additionally, deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various sectors. These factors underscore the necessity for a comprehensive literature review, as current reviews do not systematically encompass the most recent findings on these topics. Consequently, it provides… More >