Yongli Liu^1,2, Weihao Li^1,2,*, Haitao Wang^1,2,3, Taoren Du⁴

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.064179

Abstract Coal dust explosions are severe safety accidents in coal mine production, posing significant threats to life and property. Predicting the maximum explosion pressure (P_m) of coal dust using deep learning models can effectively assess potential risks and provide a scientific basis for preventing coal dust explosions. In this study, a 20-L explosion sphere apparatus was used to test the maximum explosion pressure of coal dust under seven different particle sizes and ten mass concentrations (C_dust), resulting in a dataset of 70 experimental groups. Through Spearman correlation analysis and random forest feature selection methods, particle size (D₁₀, D₂₀, D₅₀)… More >

Pu Yang^1,*, Wanning Yan¹, Rong Li¹, Lei Chen², Lijie Guo²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064947

Abstract Lithium-ion batteries (LIBs) have been widely used in mobile energy storage systems because of their high energy density, long life, and strong environmental adaptability. Accurately estimating the state of health (SOH) for LIBs is promising and has been extensively studied for many years. However, the current prediction methods are susceptible to noise interference, and the estimation accuracy has room for improvement. Motivated by this, this paper proposes a novel battery SOH estimation method, the Beluga Whale Optimization (BWO) and Noise-Input Gaussian Process (NIGP) Stacked Model (BGNSM). This method integrates the BWO-optimized Gaussian Process Regression (GPR)… More >

Takanobu Hirosawa^*, Taro Shimizu

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063803

Abstract Artificial Intelligence (AI) is fundamentally transforming medical diagnostics, driving advancements that enhance accuracy, efficiency, and personalized patient care. This narrative review explores AI integration across various diagnostic domains, emphasizing its role in improving clinical decision-making. The evolution of medical diagnostics from traditional observational methods to sophisticated imaging, laboratory tests, and molecular diagnostics lays the foundation for understanding AI’s impact. Modern diagnostics are inherently complex, influenced by multifactorial disease presentations, patient variability, cognitive biases, and systemic factors like data overload and interdisciplinary collaboration. AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches, employing machine… More >

Hemn Barzan Abdalla^1,*, Yulia Kumar², Jose Marchena², Stephany Guzman², Ardalan Awlla³, Mehdi Gheisari⁴, Maryam Cheraghy¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063551

Abstract Dealing with data scarcity is the biggest challenge faced by Artificial Intelligence (AI), and it will be interesting to see how we overcome this obstacle in the future, but for now, “THE SHOW MUST GO ON!!!” As AI spreads and transforms more industries, the lack of data is a significant obstacle: the best methods for teaching machines how real-world processes work. This paper explores the considerable implications of data scarcity for the AI industry, which threatens to restrict its growth and potential, and proposes plausible solutions and perspectives. In addition, this article focuses highly on… More >

Qiang Guo^1,2,*, Rubo Zhang¹, Bingbing Zhang³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063288

Abstract Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications, and the major challenge is false positives that occur during pedestrian detection. The emergence of various Convolutional Neural Network-based detection strategies substantially enhances pedestrian detection accuracy but still does not solve this problem well. This paper deeply analyzes the detection framework of the two-stage CNN detection methods and finds out false positives in detection results are due to its training strategy misclassifying some false proposals, thus weakening the classification capability of the following… More >

Zhigao Zeng¹, Jun Liu¹, Bing Zheng², Shengqiu Yi¹, Xinpan Yuan¹, Qiang Liu^1,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063257

Abstract To solve the problems of complex lesion region morphology, blurred edges, and limited hardware resources for deploying the recognition model in pneumonia image recognition, an improved EfficientNetV2 pneumonia recognition model based on multiscale attention is proposed. First, the number of main module stacks of the model is reduced to avoid overfitting, while the dilated convolution is introduced in the first convolutional layer to expand the receptive field of the model; second, a redesigned improved mobile inverted bottleneck convolution (IMBConv) module is proposed, in which GSConv is introduced to enhance the model’s attention to inter-channel information,… More >

Abdullah M. Alnajim^1,*, Faisal Mohammed Alotaibi^2,#, Sheroz Khan^3,#

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063139

Abstract Distributed denial of service (DDoS) attacks are common network attacks that primarily target Internet of Things (IoT) devices. They are critical for emerging wireless services, especially for applications with limited latency. DDoS attacks pose significant risks to entrepreneurial businesses, preventing legitimate customers from accessing their websites. These attacks require intelligent analytics before processing service requests. Distributed denial of service (DDoS) attacks exploit vulnerabilities in IoT devices by launching multi-point distributed attacks. These attacks generate massive traffic that overwhelms the victim’s network, disrupting normal operations. The consequences of distributed denial of service (DDoS) attacks are typically… More >

Aqsa Rahim^*, Fuqing Yuan, Javad Barabady

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061575

Abstract In recent years, advancements in autonomous vehicle technology have accelerated, promising safer and more efficient transportation systems. However, achieving fully autonomous driving in challenging weather conditions, particularly in snowy environments, remains a challenge. Snow-covered roads introduce unpredictable surface conditions, occlusions, and reduced visibility, that require robust and adaptive path detection algorithms. This paper presents an enhanced road detection framework for snowy environments, leveraging Simple Framework for Contrastive Learning of Visual Representations (SimCLR) for Self-Supervised pretraining, hyperparameter optimization, and uncertainty-aware object detection to improve the performance of You Only Look Once version 8 (YOLOv8). The model… More >

Xiaohui Yang, Shuo Huang^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061507

Abstract With the rapid development of medical data sharing, issues of privacy and ownership have become prominent, which have limited the scale of data sharing. To address the above challenges, we propose a blockchain-based data-sharing framework to ensure data security and encourage data owners to actively participate in sharing. We introduce a reliable attribute-based searchable encryption scheme that enables fine-grained access control of encrypted data and ensures secure and efficient data sharing. The revenue distribution model is constructed based on Shapley value to motivate participants. Additionally, by integrating the smart contract technology of blockchain, the search More >

Bisni Fahad Mon¹, Mohammad Hayajneh^1,2,*, Najah Abu Ali¹, Farman Ullah¹, Hikmat Ullah³, Shayma Alkobaisi⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061411

Abstract In this paper, we explore the ever-changing field of Digital Twins (DT) in the Industrial Internet of Things (IIoT) context, emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0. The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT, focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration. It discusses the emergence of DT, contextualizing its evolution within the framework of IIoT. The study categorizes the different types of DT, including prototypes and instances, and provides an… More >