Abdallah Ghourabi^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2743-2760, 2024, DOI:10.32604/cmc.2024.053163 - 15 August 2024

Abstract The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware. These malicious applications have become a serious concern to the security of Android systems. To address this problem, researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples. However, most existing studies have focused on the classification task and overlooked the feature selection process, which is crucial to reduce the training time and maintain or improve the classification results. The… More >

Gonghui Deng, Dunzhi Wu, Weizhen Chen^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1985-2003, 2024, DOI:10.32604/cmc.2024.052174 - 15 August 2024

Abstract The task of food image recognition, a nuanced subset of fine-grained image recognition, grapples with substantial intra-class variation and minimal inter-class differences. These challenges are compounded by the irregular and multi-scale nature of food images. Addressing these complexities, our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion, grounded in the ConvNeXt architecture. Our model employs hybrid attention (HA) mechanisms to pinpoint critical discriminative regions within images, substantially mitigating the influence of background noise. Furthermore, it introduces a multi-stage local fusion (MSLF) module, fostering long-distance dependencies between feature maps at… More >

Jing Zhang^*, Guangli Wu, Shanshan Song

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1947-1965, 2024, DOI:10.32604/cmc.2024.051781 - 15 August 2024

Abstract Video summarization aims to select key frames or key shots to create summaries for fast retrieval, compression, and efficient browsing of videos. Graph neural networks efficiently capture information about graph nodes and their neighbors, but ignore the dynamic dependencies between nodes. To address this challenge, we propose an innovative Adaptive Graph Convolutional Adjacency Matrix Network (TAMGCN), leveraging the attention mechanism to dynamically adjust dependencies between graph nodes. Specifically, we first segment shots and extract features of each frame, then compute the representative features of each shot. Subsequently, we utilize the attention mechanism to dynamically adjust More >

Jingtao Sun^1,2, Fengling Zhang^1,2,*, Huanqi Liu^1,2, Wenyan Hou^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3173-3192, 2024, DOI:10.32604/cmc.2024.051705 - 15 August 2024

Abstract As image manipulation technology advances rapidly, the malicious use of image tampering has alarmingly escalated, posing a significant threat to social stability. In the realm of image tampering localization, accurately localizing limited samples, multiple types, and various sizes of regions remains a multitude of challenges. These issues impede the model’s universality and generalization capability and detrimentally affect its performance. To tackle these issues, we propose FL-MobileViT-an improved MobileViT model devised for image tampering localization. Our proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain, and captures richer traces of tampering… More >

Tuğba Çelikten¹, Aytuğ Onan^2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3351-3377, 2024, DOI:10.32604/cmc.2024.051574 - 15 August 2024

Abstract The purpose of this study is to develop a reliable method for distinguishing between AI-generated, paraphrased, and human-written texts, which is crucial for maintaining the integrity of research and ensuring accurate information flow in critical fields such as healthcare. To achieve this, we propose HybridGAD, a novel hybrid model that combines Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU) architectures with an attention mechanism. Our methodology involves training this hybrid model on a dataset of radiology abstracts, encompassing texts generated by AI, paraphrased by AI, and written by humans. The… More >

Fangfang Shan^1,2,*, Mengyao Liu^1,2, Menghan Zhang^1,2, Zhenyu Wang^1,2

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1521-1542, 2024, DOI:10.32604/cmc.2024.053937 - 18 July 2024

Abstract Social media has become increasingly significant in modern society, but it has also turned into a breeding ground for the propagation of misleading information, potentially causing a detrimental impact on public opinion and daily life. Compared to pure text content, multmodal content significantly increases the visibility and share ability of posts. This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection. To effectively address the critical challenge of accurately detecting fake news on social media, this paper proposes a fake… More >

Bo Cao^1,*, Qinghua Xing², Longyue Li², Huaixi Xing¹, Zhanfu Song¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1251-1275, 2024, DOI:10.32604/cmc.2024.052842 - 18 July 2024

Abstract As a core part of battlefield situational awareness, air target intention recognition plays an important role in modern air operations. Aiming at the problems of insufficient feature extraction and misclassification in intention recognition, this paper designs an air target intention recognition method (KGTLIR) based on Knowledge Graph and Deep Learning. Firstly, the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism. Meanwhile, the accuracy, recall, and F1-score after iteration are introduced More >

Batyrkhan Omarov^1,2,*, Meirzhan Baikuvekov¹, Daniyar Sultan¹, Nurzhan Mukazhanov³, Madina Suleimenova², Maigul Zhekambayeva³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 341-359, 2024, DOI:10.32604/cmc.2024.052437 - 18 July 2024

Abstract This research introduces an innovative ensemble approach, combining Deep Residual Networks (ResNets) and Bidirectional Gated Recurrent Units (BiGRU), augmented with an Attention Mechanism, for the classification of heart arrhythmias. The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency. The model leverages the deep hierarchical feature extraction capabilities of ResNets, which are adept at identifying intricate patterns within electrocardiogram (ECG) data, while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals. The integration of an Attention Mechanism refines the model’s focus on critical segments… More >

Bing Li^1,2,*, Liangyu Wang¹, Xia Liu^1,2, Hongbin Fan¹, Bo Wang³, Shoudi Tong¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1543-1561, 2024, DOI:10.32604/cmc.2024.052009 - 18 July 2024

Abstract Nuclear magnetic resonance imaging of breasts often presents complex backgrounds. Breast tumors exhibit varying sizes, uneven intensity, and indistinct boundaries. These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation. Thus, we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms. Initially, the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs. Subsequently, we devise a fusion network incorporating multi-scale features and boundary attention mechanisms for breast tumor segmentation. We incorporate multi-scale parallel dilated convolution modules into… More >

Li Li^*, Qing Zhang, Youran Kong

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 801-818, 2024, DOI:10.32604/cmc.2024.051916 - 18 July 2024

Abstract Due to the diversity and unpredictability of changes in malicious code, studying the traceability of variant families remains challenging. In this paper, we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants. This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images. The method includes a lightweight classifier and a simulator. The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile, embedded, and other devices. The simulator utilizes… More >