Syeda Shamaila Zareen^1,*, Guangmin Sun^1,*, Mahwish Kundi², Syed Furqan Qadri³, Salman Qadri⁴

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418

Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model achieves a high average recognition… More >

Zenglun Guan^1,2, Murad S. Alfarzaeai^1,3,*, Eryi Hu^1,3,*, Taqiaden Alshmeri⁴, Wang Peng³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 279-306, 2024, DOI:10.32604/cmc.2024.047159

Abstract In the coal mining industry, the gangue separation phase imposes a key challenge due to the high visual similarity between coal and gangue. Recently, separation methods have become more intelligent and efficient, using new technologies and applying different features for recognition. One such method exploits the difference in substance density, leading to excellent coal/gangue recognition. Therefore, this study uses density differences to distinguish coal from gangue by performing volume prediction on the samples. Our training samples maintain a record of 3-side images as input, volume, and weight as the ground truth for the classification. The prediction process relies on a… More >

Linlin Yuan^1,2, Tiantian Zhang^1,3, Yuling Chen^1,*, Yuxiang Yang¹, Huang Li¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1561-1579, 2024, DOI:10.32604/cmc.2023.046907

Abstract The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’ privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based on this, we propose a… More >

Naglaa F. Soliman¹, Fatma E. Fadl-Allah², Walid El-Shafai^3,4,*, Mahmoud I. Aly², Maali Alabdulhafith¹, Fathi E. Abd El-Samie¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 201-241, 2024, DOI:10.32604/cmc.2024.046757

Abstract The efficient transmission of images, which plays a large role in wireless communication systems, poses a significant challenge in the growth of multimedia technology. High-quality images require well-tuned communication standards. The Single Carrier Frequency Division Multiple Access (SC-FDMA) is adopted for broadband wireless communications, because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio (PAPR). Data transmission through open-channel networks requires much concentration on security, reliability, and integrity. The data need a space away from unauthorized access, modification, or deletion. These requirements are to be fulfilled by digital image watermarking and encryption. This paper is mainly… More >

BIOCELL, Vol.48, Suppl.1, pp. 1-289, 2024

Abstract This article has no abstract. More >

KARAN WADHWA¹, PAYAL CHAUHAN¹, SHOBHIT KUMAR², RAKESH PAHWA^3,*, RAVINDER VERMA⁴, RAJAT GOYAL⁵, GOVIND SINGH¹, ARCHANA SHARMA⁶, NEHA RAO³, DEEPAK KAUSHIK^1,*

Oncology Research, Vol.32, No.5, pp. 877-897, 2024, DOI:10.32604/or.2024.047278

Abstract Background: Glioblastoma multiforme (GBM) is recognized as the most lethal and most highly invasive tumor. The high likelihood of treatment failure arises from the presence of the blood-brain barrier (BBB) and stem cells around GBM, which avert the entry of chemotherapeutic drugs into the tumor mass. Objective: Recently, several researchers have designed novel nanocarrier systems like liposomes, dendrimers, metallic nanoparticles, nanodiamonds, and nanorobot approaches, allowing drugs to infiltrate the BBB more efficiently, opening up innovative avenues to prevail over therapy problems and radiation therapy. Methods: Relevant literature for this manuscript has been collected from a comprehensive and systematic search of… More > Graphic Abstract

Farhad Mortezapour Shiri^1,*, Ehsan Ahmadi², Mohammadreza Rezaee¹, Thinagaran Perumal¹

Journal on Artificial Intelligence, Vol.6, pp. 85-103, 2024, DOI:10.32604/jai.2024.048911

Abstract Automatic detection of student engagement levels from videos, which is a spatio-temporal classification problem is crucial for enhancing the quality of online education. This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos. The evaluation of these models utilizes the DAiSEE dataset, a public repository capturing student affective states in e-learning scenarios. The initial model integrates EfficientNetV2-L with Gated Recurrent Unit (GRU) and attains an accuracy of 61.45%. Subsequently, the second model combines EfficientNetV2-L with bidirectional GRU (Bi-GRU), yielding an accuracy of 61.56%. The… More >

Dawei Ruan¹, Mingwei Hu^1,2,3,4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 821-844, 2024, DOI:10.32604/cmes.2024.048392

Abstract The specialized equipment utilized in long-line tunnel engineering is evolving towards large-scale, multifunctional, and complex orientations. The vibration caused by the high-frequency units during regular operation is supported by the foundation of the units, and the magnitude of vibration and the operating frequency fluctuate in different engineering contexts, leading to variations in the dynamic response of the foundation. The high-frequency units yield significantly diverse outcomes under different startup conditions and times, resulting in failure to meet operational requirements, influencing the normal function of the tunnel, and causing harm to the foundation structure, personnel, and property in severe cases. This article… More >

Zun-Di Huang¹, Zhen-Bin Zhou^1,2,3, Ning Chang¹, Zheng-Wei Chen^2,3,*, Su-Mei Wang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 975-996, 2024, DOI:10.32604/cmes.2024.047664

Abstract The safety and stability of high-speed maglev trains traveling on viaducts in crosswinds critically depend on their aerodynamic characteristics. Therefore, this paper uses an improved delayed detached eddy simulation (IDDES) method to investigate the aerodynamic features of high-speed maglev trains with different marshaling lengths under crosswinds. The effects of marshaling lengths (varying from 3-car to 8-car groups) on the train’s aerodynamic performance, surface pressure, and the flow field surrounding the train were investigated using the three-dimensional unsteady compressible Navier-Stokes (N-S) equations. The results showed that the marshaling lengths had minimal influence on the aerodynamic performance of the head and middle… More > Graphic Abstract

Hung-Hsiang Wang¹, Chih-Ping Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 685-709, 2024, DOI:10.32604/cmes.2024.047630

Abstract This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of the mouse devices with such traditional methods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep learning to predict the extent… More >