Yunjing Liu^1,, Erhu Zhang^1,2,,*, Jingjing Wang³, Guangfeng Lin², Jinghong Duan⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1633-1653, 2024, DOI:10.32604/cmc.2024.052611

Abstract Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue. Different from natural images, character images pay more attention to stroke information. However, existing models mainly consider pixel-level information while ignoring structural information of the character, such as its edge and glyph, resulting in reconstructed images with mottled local structure and character damage. To solve these problems, we propose a novel generative adversarial network (GAN) framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework, i.e., EDU-GAN. Unlike existing frameworks, the generator introduces the… More >

Haotang Tan¹, Song Sun^2,*, Tian Cheng³, Xiyuan Shu²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 661-678, 2024, DOI:10.32604/cmc.2024.052208

Abstract Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmental monitoring. Addressing the limitations of conventional convolutional neural networks, we propose an innovative transformer-based method. This method leverages transformers, which are adept at processing data sequences, to enhance cloud detection accuracy. Additionally, we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction, thereby aiding in the retention of critical details often lost during cloud detection. Our extensive experimental validation shows that our approach significantly outperforms established models, excelling in high-resolution feature extraction and More >

Roseline Oluwaseun Ogundokun^1,2, Joseph Bamidele Awotunde³, Hakeem Babalola Akande⁴, Cheng-Chi Lee^5,6,*, Agbotiname Lucky Imoize^7,8

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 139-161, 2024, DOI:10.32604/cmc.2024.052153

Abstract Mobile technology is developing significantly. Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners. Typically, computer vision models focus on image detection and classification issues. MobileNetV2 is a computer vision model that performs well on mobile devices, but it requires cloud services to process biometric image information and provide predictions to users. This leads to increased latency. Processing biometrics image datasets on mobile devices will make the prediction faster, but mobiles are resource-restricted devices in terms of storage, power, and computational speed. Hence, a model that is small in size,… More >

Chunwei Tian^1,2,3,4, Haoyang Gao^2,3, Pengwei Wang², Bob Zhang^1,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 105-118, 2024, DOI:10.32604/cmc.2024.052097

Abstract Generative adversarial networks (GANs) with gaming abilities have been widely applied in image generation. However, gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying scenes. Enhancing the relation of hierarchical information in a generation network and enlarging differences of different network architectures can facilitate more structural information to improve the generation effect for image generation. In this paper, we propose an enhanced GAN via improving a generator for image generation (EIGGAN). EIGGAN applies a spatial attention to a generator to extract salient information to enhance the truthfulness… More >

Seolhee Kim¹, Sang-Duck Lee^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070

Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… 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

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 >

Jinqiao Dai, Pengsen Cheng, Jiayong Liu^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 547-565, 2024, DOI:10.32604/cmc.2024.051745

Abstract Generating diverse and factual text is challenging and is receiving increasing attention. By sampling from the latent space, variational autoencoder-based models have recently enhanced the diversity of generated text. However, existing research predominantly depends on summarization models to offer paragraph-level semantic information for enhancing factual correctness. The challenge lies in effectively generating factual text using sentence-level variational autoencoder-based models. In this paper, a novel model called fact-aware conditional variational autoencoder is proposed to balance the factual correctness and diversity of generated text. Specifically, our model encodes the input sentences and uses them as facts to… 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

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 >

Anas AbuKaraki¹, Tawfi Alrawashdeh¹, Sumaya Abusaleh¹, Malek Zakarya Alksasbeh^1,*, Bilal Alqudah¹, Khalid Alemerien², Hamzah Alshamaseen³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1055-1073, 2024, DOI:10.32604/cmc.2024.051420

Abstract This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data… More >

Shiming Lin^1,2,3, Ling Ye², Yijie Zhuang¹, Lingyun Lu^2,*, Shaoqiu Zheng^2,*, Chenxi Huang¹, Ng Yin Kwee⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 299-317, 2024, DOI:10.32604/cmc.2024.051379

Abstract In recent years, with the continuous development of deep learning and knowledge graph reasoning methods, more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning. By searching paths on the knowledge graph and making fact and link predictions based on these paths, deep learning-based Reinforcement Learning (RL) agents can demonstrate good performance and interpretability. Therefore, deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic. However, even in a small and fixed knowledge graph reasoning action… More >