Daoming Wei¹, Fangyan Ge², Bopeng Zhang¹, Zhiqiang Zhao³, Dequan Li^3,*, Lizong Xi⁴, Jinrong Hu^5,*, Xin Wang⁶

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5729-5746, 2025, DOI:10.32604/cmc.2025.061402 - 19 May 2025

Abstract Accurate cloud classification plays a crucial role in aviation safety, climate monitoring, and localized weather forecasting. Current research has been focusing on machine learning techniques, particularly deep learning based model, for the types identification. However, traditional approaches such as convolutional neural networks (CNNs) encounter difficulties in capturing global contextual information. In addition, they are computationally expensive, which restricts their usability in resource-limited environments. To tackle these issues, we present the Cloud Vision Transformer (CloudViT), a lightweight model that integrates CNNs with Transformers. The integration enables an effective balance between local and global feature extraction. To… More >

Xiangbin Wang¹, Qingjun Yuan^1,*, Weina Niu², Qianwei Meng¹, Yongjuan Wang¹, Chunxiang Gu¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2267-2285, 2025, DOI:10.32604/cmc.2025.061767 - 16 April 2025

Abstract With the rise of encrypted traffic, traditional network analysis methods have become less effective, leading to a shift towards deep learning-based approaches. Among these, multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic, improving classification accuracy. However, existing research predominantly relies on late fusion techniques, which hinder the full utilization of deep features within the data. To address this limitation, we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature extraction. Specifically, our approach performs real-time fusion of modalities More >

Hong Huang, Yunfei Wang^*, Guotao Yuan, Xin Li

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 361-387, 2024, DOI:10.32604/cmc.2024.051633 - 18 July 2024

Abstract Deep Neural Networks (DNNs) are integral to various aspects of modern life, enhancing work efficiency. Nonetheless, their susceptibility to diverse attack methods, including backdoor attacks, raises security concerns. We aim to investigate backdoor attack methods for image categorization tasks, to promote the development of DNN towards higher security. Research on backdoor attacks currently faces significant challenges due to the distinct and abnormal data patterns of malicious samples, and the meticulous data screening by developers, hindering practical attack implementation. To overcome these challenges, this study proposes a Gaussian Noise-Targeted Universal Adversarial Perturbation (GN-TUAP) algorithm. This approach… More >

Tao Zhou^1,3, Yujie Guo^1,3,*, Caiyue Peng^1,3, Yuxia Niu^1,3, Yunfeng Pan^1,3, Huiling Lu²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4863-4882, 2024, DOI:10.32604/cmc.2024.050767 - 20 June 2024

Abstract Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the… More >

Pakorn Santakij¹, Samai Srisuay^2,*, Pongporn Punpeng¹

Computer Systems Science and Engineering, Vol.48, No.3, pp. 665-689, 2024, DOI:10.32604/csse.2024.045066 - 20 May 2024

Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.… More >

Huixiang Xu^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2309-2335, 2024, DOI:10.32604/cmc.2024.046253 - 27 February 2024

Abstract The Internet of Things (IoT) has revolutionized how we interact with and gather data from our surrounding environment. IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights. The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented data generation and connectivity. These IoT devices, equipped with many sensors and actuators, continuously produce vast volumes of data. However, the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges. However, transmitting… More >

Huanhua Liu, Wei Wang^*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084 - 22 September 2023

Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor… More >

Ehab Bahaudien Ashary¹, Sahar Jambi², Rehab B. Ashari², Mahmoud Ragab^3,4,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2953-2969, 2023, DOI:10.32604/csse.2023.041523 - 09 November 2023

Abstract Object Detection is the task of localization and classification of objects in a video or image. In recent times, because of its widespread applications, it has obtained more importance. In the modern world, waste pollution is one significant environmental problem. The prominence of recycling is known very well for both ecological and economic reasons, and the industry needs higher efficiency. Waste object detection utilizing deep learning (DL) involves training a machine-learning method to classify and detect various types of waste in videos or images. This technology is utilized for several purposes recycling and sorting waste,… More >

Nida Aslam^1,*, Irfan Ullah Khan², Salma Abdulrahman Bader², Aisha Alansari³, Lama Abdullah Alaqeel², Razan Mohammed Khormy², Zahra Abdultawab AlKubaish², Tariq Hussain^4,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3167-3188, 2023, DOI:10.32604/cmc.2023.039721 - 08 October 2023

Abstract One of the most widely used smartphone operating systems, Android, is vulnerable to cutting-edge malware that employs sophisticated logic. Such malware attacks could lead to the execution of unauthorized acts on the victims’ devices, stealing personal information and causing hardware damage. In previous studies, machine learning (ML) has shown its efficacy in detecting malware events and classifying their types. However, attackers are continuously developing more sophisticated methods to bypass detection. Therefore, up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices. Therefore, this study employed ML algorithms to… More >

T. Satyanarayana Murthy¹, Kollati Vijaya Kumar², Fayadh Alenezi³, E. Laxmi Lydia⁴, Gi-Cheon Park⁵, Hyoung-Kyu Song⁶, Gyanendra Prasad Joshi⁷, Hyeonjoon Moon^7,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1633-1650, 2023, DOI:10.32604/csse.2023.034769 - 28 July 2023

Abstract Fruit classification utilizing a deep convolutional neural network (CNN) is the most promising application in personal computer vision (CV). Profound learning-related characterization made it possible to recognize fruits from pictures. But, due to the similarity and complexity, fruit recognition becomes an issue for the stacked fruits on a weighing scale. Recently, Machine Learning (ML) methods have been used in fruit farming and agriculture and brought great convenience to human life. An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts. CNN’s (convolutional neural networks) have attained… More >