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  • Open Access


    Generative Multi-Modal Mutual Enhancement Video Semantic Communications

    Yuanle Chen1, Haobo Wang1, Chunyu Liu1, Linyi Wang2, Jiaxin Liu1, Wei Wu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2985-3009, 2024, DOI:10.32604/cmes.2023.046837

    Abstract Recently, there have been significant advancements in the study of semantic communication in single-modal scenarios. However, the ability to process information in multi-modal environments remains limited. Inspired by the research and applications of natural language processing across different modalities, our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos. Specifically, we propose a deep learning-based Multi-Modal Mutual Enhancement Video Semantic Communication system, called M3E-VSC. Built upon a Vector Quantized Generative Adversarial Network (VQGAN), our system aims to leverage mutual enhancement among different modalities by using text as the main More >

  • Open Access


    A Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

    Kun Fang, Julong Pan*, Lingyi Li, Ruihan Xiang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 493-514, 2024, DOI:10.32604/cmc.2023.045008

    Abstract With the widespread use of Internet of Things (IoT) technology in daily life and the considerable safety risks of falls for elderly individuals, research on IoT-based fall detection systems has gained much attention. This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection (Skip-DSCGAN) for fall detection. The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data. A semisupervised learning approach is adopted to train the model using only activities of daily living (ADL) data, which can avoid data imbalance… More >

  • Open Access


    Image to Image Translation Based on Differential Image Pix2Pix Model

    Xi Zhao1, Haizheng Yu1,*, Hong Bian2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 181-198, 2023, DOI:10.32604/cmc.2023.041479

    Abstract In recent years, Pix2Pix, a model within the domain of GANs, has found widespread application in the field of image-to-image translation. However, traditional Pix2Pix models suffer from significant drawbacks in image generation, such as the loss of important information features during the encoding and decoding processes, as well as a lack of constraints during the training process. To address these issues and improve the quality of Pix2Pix-generated images, this paper introduces two key enhancements. Firstly, to reduce information loss during encoding and decoding, we utilize the U-Net++ network as the generator for the Pix2Pix model,… More >

  • Open Access


    A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

    Yalong Xie1, Aiping Li1,*, Biyin Hu2, Liqun Gao1, Hongkui Tu1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2707-2726, 2023, DOI:10.32604/cmc.2023.037039

    Abstract Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models’ performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature… More >

  • Open Access


    A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

    Jianfeng Lu1,*, Xinyi Liu1, Mengtao Shi1, Chen Cui1,2, Mahmoud Emam1,3

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2865-2882, 2023, DOI:10.32604/iasc.2023.039742

    Abstract Ceramic tiles are one of the most indispensable materials for interior decoration. The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures. In this paper, we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network (GAN). The generated tile images can be tailored to meet the specific needs of the user for the tile textures. The proposed method consists of four steps. Firstly, a dataset of ceramic tile images with diverse distributions is created and… More >

  • Open Access


    Text-to-Sketch Synthesis via Adversarial Network

    Jason Elroy Martis1, Sannidhan Manjaya Shetty2,*, Manas Ranjan Pradhan3, Usha Desai4, Biswaranjan Acharya5,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 915-938, 2023, DOI:10.32604/cmc.2023.038847

    Abstract In the past, sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes. However, relying on eyewitness observations can lead to discrepancies in the depictions of the sketch, depending on the experience and skills of the sketch artist. With the emergence of modern technologies such as Generative Adversarial Networks (GANs), generating images using verbal and textual cues is now possible, resulting in more accurate sketch depictions. In this study, we propose an adversarial network that generates human facial sketches using such cues provided More >

  • Open Access


    OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

    M. Muzaffar Hameed1,2, Rodina Ahmad1,*, Laiha Mat Kiah1, Ghulam Murtaza3, Noman Mazhar1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1267-1289, 2023, DOI:10.32604/cmc.2023.035063

    Abstract Offline signature verification (OfSV) is essential in preventing the falsification of documents. Deep learning (DL) based OfSVs require a high number of signature images to attain acceptable performance. However, a limited number of signature samples are available to train these models in a real-world scenario. Several researchers have proposed models to augment new signature images by applying various transformations. Others, on the other hand, have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples. Hence, augmenting a sufficient number of signatures with variations is still a challenging task. This… More >

  • Open Access


    Stock Market Prediction Using Generative Adversarial Networks (GANs): Hybrid Intelligent Model

    Fares Abdulhafidh Dael1,*, Ömer Çağrı Yavuz2, Uğur Yavuz1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 19-35, 2023, DOI:10.32604/csse.2023.037903

    Abstract The key indication of a nation’s economic development and strength is the stock market. Inflation and economic expansion affect the volatility of the stock market. Given the multitude of factors, predicting stock prices is intrinsically challenging. Predicting the movement of stock price indexes is a difficult component of predicting financial time series. Accurately predicting the price movement of stocks can result in financial advantages for investors. Due to the complexity of stock market data, it is extremely challenging to create accurate forecasting models. Using machine learning and other algorithms to anticipate stock prices is an More >

  • Open Access


    Tight Sandstone Image Augmentation for Image Identification Using Deep Learning

    Dongsheng Li, Chunsheng Li*, Kejia Zhang, Tao Liu, Fang Liu, Jingsong Yin, Mingyue Liao

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1209-1231, 2023, DOI:10.32604/csse.2023.034395

    Abstract Intelligent identification of sandstone slice images using deep learning technology is the development trend of mineral identification, and accurate mineral particle segmentation is the most critical step for intelligent identification. A typical identification model requires many training samples to learn as many distinguishable features as possible. However, limited by the difficulty of data acquisition, the high cost of labeling, and privacy protection, this has led to a sparse sample number and cannot meet the training requirements of deep learning image identification models. In order to increase the number of samples and improve the training effect… More >

  • Open Access


    Data Augmentation and Random Multi-Model Deep Learning for Data Classification

    Fatma Harby1, Adel Thaljaoui1, Durre Nayab2, Suliman Aladhadh3,*, Salim EL Khediri3,4, Rehan Ullah Khan3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5191-5207, 2023, DOI:10.32604/cmc.2022.029420

    Abstract In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of More >

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