Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Isha Sood^*, Varsha Sharma

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2791-2818, 2024, DOI:10.32604/cmc.2024.055463 - 18 November 2024

Abstract Ransomware has emerged as a critical cybersecurity threat, characterized by its ability to encrypt user data or lock devices, demanding ransom for their release. Traditional ransomware detection methods face limitations due to their assumption of similar data distributions between training and testing phases, rendering them less effective against evolving ransomware families. This paper introduces TLERAD (Transfer Learning for Enhanced Ransomware Attack Detection), a novel approach that leverages unsupervised transfer learning and co-clustering techniques to bridge the gap between source and target domains, enabling robust detection of both known and unknown ransomware variants. The proposed method More >

HUI ZHOU^#, ZHENGYU YU^#, JING XU, ZHONGWANG WANG, YALI TAO, JINJIN WANG, PEIPEI YANG, JINRONG YANG^*, TING NIU^*

Oncology Research, Vol.32, No.12, pp. 1903-1919, 2024, DOI:10.32604/or.2024.048138 - 13 November 2024

Abstract Background: Dihydrolipoamide S-acetyltransferase (DLAT) is a subunit of the pyruvate dehydrogenase complex (PDC), a rate-limiting enzyme complex, that can participate in either glycolysis or the tricarboxylic acid cycle (TCA). However, the pathogenesis is not fully understood. We aimed to perform a more systematic and comprehensive analysis of DLAT in the occurrence and progression of tumors, and to investigate its function in patients’ prognosis and immunotherapy. Methods: The differential expression, diagnosis, prognosis, genetic and epigenetic alterations, tumor microenvironment, stemness, immune infiltration cells, function enrichment, single-cell analysis, and drug response across cancers were conducted based on multiple computational… More > Graphic Abstract

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 >

Wei Lu^1,3, Yujie Chen^2,*, Bo Yu², Dongliang Sun², Wei Zhang², Yanru Yang^1,3, Xiaodong Wang^1,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012265

Abstract Flow boiling offers superior heat transfer performance compared to single-phase flow, therefore holding significant potential for application in thermal management. In mini-channel applications, due to their narrow dimensions, the size characteristics of the channel have a particularly notable impact on bubble dynamics and flow boiling heat transfer performance. This study employs the VOSET method to explore the impact of different aspect ratios (1:3, 1:2, 1:1, 2:1, 3:1) on the heat transfer performance of mini-channels. By maintaining a consistent equivalent diameter across the channels, the study aims to unveil the mechanism by which aspect ratios affect… More >

Bumsoo Kim¹, Wonseop Shin², Yonghoon Jung¹, Youngsup Park³, Sanghyun Seo^1,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2143-2164, 2024, DOI:10.32604/cmes.2024.056079 - 31 October 2024

Abstract Arbitrary style transfer aims to perceptually reflect the style of a reference image in artistic creations with visual aesthetics. Traditional style transfer models, particularly those using adaptive instance normalization (AdaIN) layer, rely on global statistics, which often fail to capture the spatially local color distribution, leading to outputs that lack variation despite geometric transformations. To address this, we introduce Patchified AdaIN, a color-inspired style transfer method that applies AdaIN to localized patches, utilizing local statistics to capture the spatial color distribution of the reference image. This approach enables enhanced color awareness in style transfer, adapting… More >

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 >

Meena N. Alnuaimi¹, Nourah S. Alqahtani¹, Mohammed Gollapalli², Atta Rahman^1,*, Alaa Alahmadi¹, Aghiad Bakry¹, Mustafa Youldash³, Dania Alkhulaifi¹, Rashad Ahmed⁴, Hesham Al-Musallam¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2609-2623, 2024, DOI:10.32604/cmes.2024.055303 - 31 October 2024

Abstract Human beings are often affected by a wide range of skin diseases, which can be attributed to genetic factors and environmental influences, such as exposure to sunshine with ultraviolet (UV) rays. If left untreated, these diseases can have severe consequences and spread, especially among children. Early detection is crucial to prevent their spread and improve a patient’s chances of recovery. Dermatology, the branch of medicine dealing with skin diseases, faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance, type of skin, and… 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 >

Kunrong Shen^*, Wanchun Jin, Jin Wang

Frontiers in Heat and Mass Transfer, Vol.22, No.5, pp. 1361-1379, 2024, DOI:10.32604/fhmt.2024.057372 - 30 October 2024

Abstract The modified Siemens method is the dominant process for the production of polysilicon, yet it is characterised by high energy consumption. Two models of laboratory-grade Siemens reduction furnace and 12 pairs of rods industrial-grade Siemens chemical vapor deposition (CVD) reduction furnace were established, and the effects of factors such as the diameter of silicon rods, the surface temperature of silicon rods, the air inlet velocity and temperature on the heat transfer process inside the reduction furnace were investigated by numerical simulation. The results show that the convective and radiant heat losses in the furnace increased… More >