Jeng-Shyang Pan^1,2, Wenda Li¹, Shu-Chuan Chu^1,*, Xiao Sui¹, Junzo Watada³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3033-3062, 2024, DOI:10.32604/cmc.2024.056310 - 18 November 2024

Abstract The growth optimizer (GO) is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environment. However, the original GO algorithm is constrained by two significant limitations: slow convergence and high memory requirements. This restricts its application to large-scale and complex problems. To address these problems, this paper proposes an innovative enhanced growth optimizer (eGO). In contrast to conventional population-based optimization algorithms, the eGO algorithm utilizes a probabilistic model, designated as the virtual population, which is capable of accurately replicating the… More >

Sameema Tariq¹, Ata-Ur- Rehman^2,3, Maria Abubakar², Waseem Iqbal⁴, Hatoon S. Alsagri⁵, Yousef A. Alduraywish⁵, Haya Abdullah A. Alhakbani^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2493-2515, 2024, DOI:10.32604/cmc.2024.055787 - 18 November 2024

Abstract In video surveillance, anomaly detection requires training machine learning models on spatio-temporal video sequences. However, sometimes the video-only data is not sufficient to accurately detect all the abnormal activities. Therefore, we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video data. This paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual data. The proposed model is trained to produce low reconstruction error… More >

Pengfei Duan^1,*, Luling Li¹, Jianhui Liu¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012431

Abstract The supply of hydrogen-blended natural gas to civil and industrial users can assist downstream firm to achieve carbon emission reduction, and ensure energy security as an alternative gas source. This application mode has been widely concerned by urban gas enterprises. This paper focuses on the leakage problem of hydrogen-blended pipelines in utility tunnel due to corrosion and other reasons. Using dimensional analysis method, a model experiment is designed to verify that the three-dimensional compressible fluid model coupled with transport equations can effectively simulate the concentration change of hydrogen-blended natural gas after leakage in the utility… More >

Xin Lin^1,2,3, Kunpeng Zhu^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012499

Abstract This paper reveals the interplay mechanism between melt pool, spattering and vapors, aiming to further improve the forming quality through in-situ monitoring with a CMOS camera. A Residual Network based on Convolutional Block Attention Module and Focal loss function is proposed to extract multi-scale features of single tracks and learn about their behavior changes. A t-SNE clustering analysis is utilized to analysis a large amount of time sequence data on the melt pool by collecting the schlieren photographs. It is found that patterns of unstable melt pool changing corelate to the defects in single tracks, More >

Ziyu Xu¹, Kuo Tian^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011245

Abstract Considering experimental testing data is costly, and sensor data is often sparse, while simulation analysis provides overall strength information with lower accuracy, a digital twin framework is proposed for full-field structural strength assessment and prediction. The framework is mainly divided into two stages. In the offline stage, the simulation model of the structure is established, and the sensor layouts are completed. Then, the DNN pre-training model is constructed based on the reduced simulation data. In the online stage, the experimentally measured data are predicted to obtain the time-series sensors data, and the traditional transfer learning… More >

Lizheng Zhang^1,2, Jimin Chen^1,2,*, Yong Zeng^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011127

Abstract In laser powder bed fusion (LPBF), the unmelted powder recovered from the powder bed is degraded due to particle-laser interaction during continuous processing. The sensitivity of LPBF performance and molding quality to powder properties, waste powder is usually discarded after several molding cycles, which increases the cost of raw materials. At the same time, the low laser absorption rate and high thermal conductivity of copper and copper alloys inhibit the complete melting of copper powder prepared by LPBF. Therefore, it is challenging to fabricate copper alloy components with full high density and high conductivity through… More >

Hamad Naeem¹, Amjad Alsirhani^2,*, Faeiz M. Alserhani³, Farhan Ullah⁴, Ondrej Krejcar¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2185-2223, 2024, DOI:10.32604/cmes.2024.056308 - 31 October 2024

Abstract When it comes to smart healthcare business systems, network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults. To protect IoMT devices and networks in healthcare and medical settings, our proposed model serves as a powerful tool for monitoring IoMT networks. This study presents a robust methodology for intrusion detection in Internet of Medical Things (IoMT) environments, integrating data augmentation, feature selection, and ensemble learning to effectively handle IoMT data complexity. Following rigorous preprocessing, including feature extraction, correlation removal, and Recursive Feature Elimination (RFE), selected features are standardized… More >

Mohammed A. Mahdi¹, Shahanawaj Ahamad², Sawsan A. Saad³, Alaa Dafhalla³, Alawi Alqushaibi⁴, Rizwan Qureshi^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2351-2373, 2024, DOI:10.32604/cmes.2024.055723 - 31 October 2024

Abstract The segmentation of head and neck (H&N) tumors in dual Positron Emission Tomography/Computed Tomography (PET/CT) imaging is a critical task in medical imaging, providing essential information for diagnosis, treatment planning, and outcome prediction. Motivated by the need for more accurate and robust segmentation methods, this study addresses key research gaps in the application of deep learning techniques to multimodal medical images. Specifically, it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution. The primary research questions guiding this study… More >

Shahlaa Mashhadani^1,*, Wisal Hashim Abdulsalam¹, Oday Ali Hassen², Saad M. Darwish³

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 805-828, 2024, DOI:10.32604/iasc.2024.054611 - 31 October 2024

Abstract Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also… More >

Marat Satayev^1,2,*, Abdugani Azimov², Arnold Brener², Nina Alekseyeva¹, Zulfia Shakiryanova²

Frontiers in Heat and Mass Transfer, Vol.22, No.5, pp. 1533-1558, 2024, DOI:10.32604/fhmt.2024.052901 - 30 October 2024

Abstract The goal of this work is, first of all, to construct a mathematical model of the mass transfer process in porous adsorption layers, taking into account the fact that in most cases the adsorption process is carried out in non-stationary technological modes, which requires a clear description of its various stages. The scientific contribution of the novel model is based on a probability approach allowing for deriving a differential equation that takes into account the diffusion migration of adsorbed particles. Solving this equation allows us to calculate the reduced degree of the adsorption surface coverage… More >