Zhong Qu^1,*, Guoqing Mu¹, Bin Yuan²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 255-273, 2024, DOI:10.32604/cmes.2024.048175

Abstract Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning, with convolutional neural networks (CNN) playing an important role in this field. However, as the performance of crack detection in cement pavement improves, the depth and width of the network structure are significantly increased, which necessitates more computing power and storage space. This limitation hampers the practical implementation of crack detection models on various platforms, particularly portable devices like small mobile devices. To solve these problems, we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules… More > Graphic Abstract

Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT) model. At the byte-level granularity,… More >

Hae Sung Park¹, Yong Suk Choi^2,*

Computer Systems Science and Engineering, Vol.48, No.2, pp. 451-477, 2024, DOI:10.32604/csse.2023.043245

Abstract Much like humans focus solely on object movement to understand actions, directing a deep learning model’s attention to the core contexts within videos is crucial for improving video comprehension. In the recent study, Video Masked Auto-Encoder (VideoMAE) employs a pre-training approach with a high ratio of tube masking and reconstruction, effectively mitigating spatial bias due to temporal redundancy in full video frames. This steers the model’s focus toward detailed temporal contexts. However, as the VideoMAE still relies on full video frames during the action recognition stage, it may exhibit a progressive shift in attention towards spatial contexts, deteriorating its ability… More >

Xiao Feng^1,2,3,*, Zheng Yuan¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1881-1895, 2024, DOI:10.32604/cmc.2023.047243

Abstract First, we propose a cross-domain authentication architecture based on trust evaluation mechanism, including registration, certificate issuance, and cross-domain authentication processes. A direct trust evaluation mechanism based on the time decay factor is proposed, taking into account the influence of historical interaction records. We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data. We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record. Then we propose an autoencoder-based trust clustering algorithm. We perform feature… More >

S. Abinaya^*, K. Uttej Kumar

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2269-2286, 2024, DOI:10.32604/cmc.2024.047167

Abstract A Recommender System (RS) is a crucial part of several firms, particularly those involved in e-commerce. In conventional RS, a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences. Nowadays, businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’ preferences. On the other hand, the collaborative filtering (CF) algorithm utilizing AutoEncoder (AE) is seen to be effective in identifying user-interested items. However, the cost of these computations increases nonlinearly as the number of items and users increases. To triumph over the… More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for analyzing the conversation progress and… More >

Vaishnawi Priyadarshni¹, Sanjay Kumar Sharma¹, Mohammad Khalid Imam Rahmani^2,*, Baijnath Kaushik³, Rania Almajalid^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2441-2468, 2024, DOI:10.32604/cmc.2024.044963

Abstract Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of various techniques, where the fusion… More >

Zhenxiang He^*, Xunxi Wang, Chunwei Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607

Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long Short-term Memory Network (Bi-LSTM). By… More >

Ou Ye, Xinli Wei, Zhenhua Yu^*, Yan Fu, Ying Yang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1071-1093, 2024, DOI:10.32604/cmc.2023.046418

Abstract In the video captioning methods based on an encoder-decoder, limited visual features are extracted by an encoder, and a natural sentence of the video content is generated using a decoder. However, this kind of method is dependent on a single video input source and few visual labels, and there is a problem with semantic alignment between video contents and generated natural sentences, which are not suitable for accurately comprehending and describing the video contents. To address this issue, this paper proposes a video captioning method by semantic topic-guided generation. First, a 3D convolutional neural network is utilized to extract the… More >

Sumaya S. Sulaiman^1,2,*, Ibraheem Nadher³, Sarab M. Hameed²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051

Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network (CNN), and Long Short-Term Memory… More >