Sen Yang¹, Jie Zhong¹, Boyu Gan¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2943-2967, 2025, DOI:10.32604/cmes.2025.067282 - 30 September 2025

Abstract Foam concrete is widely used in engineering due to its lightweight and high porosity. Its compressive strength, a key performance indicator, is influenced by multiple factors, showing nonlinear variation. As compressive strength tests for foam concrete take a long time, a fast and accurate prediction method is needed. In recent years, machine learning has become a powerful tool for predicting the compressive strength of cement-based materials. However, existing studies often use a limited number of input parameters, and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.… More >

Cheng Cheng^*, Xuan-Ping Gong, Xiao-Yu Cheng, Lu Xiao, Xing-Ying Ma

Frontiers in Heat and Mass Transfer, Vol.23, No.3, pp. 1037-1051, 2025, DOI:10.32604/fhmt.2025.065719 - 30 June 2025

Abstract To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume, which adversely impacts gas utilization efficiency in mines, a gas extraction pure volume prediction model was developed using Support Vector Regression (SVR) and Random Forest (RF), with hyperparameters fine-tuned via the Genetic Algorithm (GA). Building upon this, an adaptive control model for gas extraction negative pressure was formulated to maximize the extracted gas volume within the pipeline network, followed by field validation experiments. Experimental results indicate that the GA-SVR model surpasses comparable models in terms of mean… More >

Van-Ha Hoang¹, Jong Weon Lee¹, Chun-Su Park^2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4097-4116, 2025, DOI:10.32604/cmc.2025.063468 - 19 May 2025

Abstract Fire can cause significant damage to the environment, economy, and human lives. If fire can be detected early, the damage can be minimized. Advances in technology, particularly in computer vision powered by deep learning, have enabled automated fire detection in images and videos. Several deep learning models have been developed for object detection, including applications in fire and smoke detection. This study focuses on optimizing the training hyperparameters of YOLOv8 and YOLOv10 models using Bayesian Tuning (BT). Experimental results on the large-scale D-Fire dataset demonstrate that this approach enhances detection performance. Specifically, the proposed approach… More >

Hamza Murad Khan¹, Anwar Khan^1,*, Santos Gracia Villar^2,3,4, Luis Alonso Dzul Lopez^2,5,6, Abdulaziz Almaleh⁷, Abdullah M. Al-Qahtani⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3369-3388, 2025, DOI:10.32604/cmc.2025.060474 - 16 April 2025

Abstract Traffic forecasting with high precision aids Intelligent Transport Systems (ITS) in formulating and optimizing traffic management strategies. The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity. To address this problem, this paper uses the Tree-structured Parzen Estimator (TPE) to tune the hyperparameters of the Long Short-term Memory (LSTM) deep learning framework. The Tree-structured Parzen Estimator (TPE) uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples. This ensures fast convergence in… More >

Suganya Athisayamani¹, A. Robert Singh², Gyanendra Prasad Joshi³, Woong Cho^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 155-183, 2025, DOI:10.32604/cmes.2024.056129 - 17 December 2024

Abstract In radiology, magnetic resonance imaging (MRI) is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures. MRI is particularly effective for detecting soft tissue anomalies. Traditionally, radiologists manually interpret these images, which can be labor-intensive and time-consuming due to the vast amount of data. To address this challenge, machine learning, and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans. This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods. There are three… More >

Avi Deb Raha¹, Fatema Jannat Dihan², Mrityunjoy Gain¹, Saydul Akbar Murad³, Apurba Adhikary², Md. Bipul Hossain², Md. Mehedi Hassan¹, Taher Al-Shehari⁴, Nasser A. Alsadhan⁵, Mohammed Kadrie⁴, Anupam Kumar Bairagi^1,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4033-4048, 2024, DOI:10.32604/cmc.2024.057415 - 19 December 2024

Abstract Breast cancer stands as one of the world’s most perilous and formidable diseases, having recently surpassed lung cancer as the most prevalent cancer type. This disease arises when cells in the breast undergo unregulated proliferation, resulting in the formation of a tumor that has the capacity to invade surrounding tissues. It is not confined to a specific gender; both men and women can be diagnosed with breast cancer, although it is more frequently observed in women. Early detection is pivotal in mitigating its mortality rate. The key to curbing its mortality lies in early detection.… More >

Ho-Yin Wong¹, Guan-Yan Yang^1,*, Kuo-Hui Yeh², Farn Wang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-4, 2024, DOI:10.32604/icces.2024.013279

Abstract 1 Introduction
This paper explores various strategies to enhance neural network performance, including adjustments to network architecture, selection of activation functions and optimizers, and regularization techniques. Hyperparameter optimization is a widely recognized approach for improving model performance [2], with methods such as grid search, genetic algorithms, and particle swarm optimization (PSO) [3] previously utilized to identify optimal solutions for neural networks. However, these techniques can be complex and challenging for beginners. Consequently, this research advocates for the use of SSO, a straightforward and effective method initially applied to the LeNet model in 2023 [4]. SSO optimizes… More >

Jiachang Liu^1,*, Zhengwei Huang², Junfeng Xiang¹, Lu Liu¹, Manlin Hu¹

Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

Tajim Md. Niamat Ullah Akhund^1,2,*, Waleed M. Al-Nuwaiser³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222 - 12 September 2024

Abstract This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While More >

Abdulrahman Alzahrani^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2331-2349, 2024, DOI:10.32604/cmc.2024.052804 - 15 August 2024

Abstract The recent development of the Internet of Things (IoTs) resulted in the growth of IoT-based DDoS attacks. The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices. Anomaly detection models evaluate transmission patterns, network traffic, and device behaviour to detect deviations from usual activities. Machine learning (ML) techniques detect patterns signalling botnet activity, namely sudden traffic increase, unusual command and control patterns, or irregular device behaviour. In addition, intrusion detection systems (IDSs) and signature-based techniques are applied to recognize known malware signatures related to botnets.… More >