Sara Salem Alzaid¹, Badr Saad T. Alkahtani^1,*, Kumar Chandan², Ravikumar Shashikala Varun Kumar³

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2555-2574, 2024, DOI:10.32604/cmes.2024.055312 - 31 October 2024

Abstract Heat transport has been significantly enhanced by the widespread usage of extended surfaces in various engineering domains. Gas turbine blade cooling, refrigeration, and electronic equipment cooling are a few prevalent applications. Thus, the thermal analysis of extended surfaces has been the subject of a significant assessment by researchers. Motivated by this, the present study describes the unsteady thermal dispersal phenomena in a wavy fin with the presence of convection heat transmission. This analysis also emphasizes a novel mathematical model in accordance with transient thermal change in a wavy profiled fin resulting from convection using the… More >

Hussain AlSalman¹, Taha Alfakih², Mabrook Al-Rakhami², Mohammad Mehedi Hassan^2,*, Amerah Alabrah²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011 - 31 October 2024

Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

Alaa U. Khawaja¹, Ahmad Shaf^2,*, Faisal Al Thobiani³, Tariq Ali⁴, Muhammad Irfan⁵, Aqib Rehman Pirzada², Unza Shahkeel²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2399-2420, 2024, DOI:10.32604/cmes.2024.054257 - 31 October 2024

Abstract Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This study proposes FTCNNLSTM (Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory), an algorithm combining Convolutional Neural Networks, Long Short-Term Memory Networks, and Attentive Interpretable Tabular Learning. The model preprocesses the CWRU (Case Western Reserve University) bearing dataset using segmentation, normalization, feature scaling, and label encoding. Its architecture comprises multiple 1D Convolutional layers, batch normalization, max-pooling, and LSTM blocks with dropout, followed by batch normalization, dense layers, and More >

Dhia K. Suker, Ahmed R. Abdo^*, Khalid Abdulkhaliq M. Alharbi

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 929-947, 2024, DOI:10.32604/iasc.2024.056341 - 31 October 2024

Abstract This paper presents a deep learning Convolutional Neural Network (CNN) for predicting grain orientations from electron backscatter diffraction (EBSD) patterns. The proposed model consists of multiple neural network layers and has been trained on a dataset of EBSD patterns obtained from stainless steel 316 (SS316). Grain orientation changes when considering the effects of temperature and strain rate on material deformation. The deep learning CNN predicts material orientation using the EBSD method to address this challenge. The accuracy of this approach is evaluated by comparing the predicted crystal orientation with the actual orientation under different conditions, More >

Huan Ma¹, Linlin Ma², Zengwei Wang^3,*, Zhendong Li³, Yuanzhen Zhu¹, Yutian Liu³

Energy Engineering, Vol.121, No.11, pp. 3133-3160, 2024, DOI:10.32604/ee.2024.055051 - 21 October 2024

Abstract With the increasing penetration of renewable energy in power system, renewable energy power ramp events (REPREs), dominated by wind power and photovoltaic power, pose significant threats to the secure and stable operation of power systems. This paper presents an early warning method for REPREs based on long short-term memory (LSTM) network and fuzzy logic. First, the warning levels of REPREs are defined by assessing the control costs of various power control measures. Then, the next 4-h power support capability of external grid is estimated by a tie line power prediction model, which is constructed based More > Graphic Abstract

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 >

Vesal Khean¹, Chomyong Kim², Sunjoo Ryu², Awais Khan¹, Min Kyung Hong³, Eun Young Kim⁴, Joungmin Kim⁵, Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 773-787, 2024, DOI:10.32604/cmc.2024.056767 - 15 October 2024

Abstract Human Interaction Recognition (HIR) was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements. HIR requires more sophisticated analysis than Human Action Recognition (HAR) since HAR focuses solely on individual activities like walking or running, while HIR involves the interactions between people. This research aims to develop a robust system for recognizing five common human interactions, such as hugging, kicking, pushing, pointing, and no interaction, from video sequences using multiple cameras. In this study, a hybrid Deep… More >

Bolin Guo^1,2, Shi Qiu^1,*, Pengchang Zhang¹, Xingjia Tang³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1809-1833, 2024, DOI:10.32604/cmc.2024.056706 - 15 October 2024

Abstract Mural paintings hold significant historical information and possess substantial artistic and cultural value. However, murals are inevitably damaged by natural environmental factors such as wind and sunlight, as well as by human activities. For this reason, the study of damaged areas is crucial for mural restoration. These damaged regions differ significantly from undamaged areas and can be considered abnormal targets. Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false detections. Hyperspectral imaging can reflect the material properties more effectively than visual characterization methods. Thus, this study employs hyperspectral imaging… More >

Baowei Wang^1,2,*, Wen You²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1085-1100, 2024, DOI:10.32604/cmc.2024.055538 - 15 October 2024

Abstract As computer graphics technology continues to advance, Collision Detection (CD) has emerged as a critical element in fields such as virtual reality, computer graphics, and interactive simulations. CD is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments, particularly within complex scenarios like virtual assembly, where both high precision and real-time responsiveness are imperative. Despite ongoing developments, current CD techniques often fall short in meeting these stringent requirements, resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly systems. To address these limitations, this study introduces a… More >

Yuanjing Luo, Xichen Tan, Zhiping Cai^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 133-160, 2024, DOI:10.32604/cmc.2024.055150 - 15 October 2024

Abstract In the era of internet proliferation, safeguarding digital media copyright and integrity, especially for images, is imperative. Digital watermarking stands out as a pivotal solution for image security. With the advent of deep learning, watermarking has seen significant advancements. Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces, resilient to various attacks. These methods, characterized by a streamlined encoder-decoder architecture, have shown enhanced performance through the incorporation of novel training modules. This article offers an in-depth analysis of deep watermarking’s core technologies, current status, and prospective More >