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  • Open Access

    ARTICLE

    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

    ARTICLE

    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 >

  • Open Access

    ARTICLE

    Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

    Subhajit Chatterjee1, Yung-Cheol Byun2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5507-5525, 2023, DOI:10.32604/cmc.2023.032843

    Abstract The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with… More >

  • Open Access

    ARTICLE

    Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement

    Xiaojun Zhu1,2,3, Heming Huang1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2155-2172, 2023, DOI:10.32604/cmes.2023.021453

    Abstract Recently, speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals. However, the training of Generative Adversarial Networks has such problems as convergence difficulty, model collapse, etc. In this work, an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed, and some improvements have been made in order to get faster convergence speed and better generated speech quality. Specifically, in the generator coding part, each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales; a gated More >

  • Open Access

    ARTICLE

    Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

    Hong’an Li1, Min Zhang1,*, Dufeng Chen2, Jing Zhang1, Meng Yang3, Zhanli Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 779-794, 2023, DOI:10.32604/cmes.2022.022369

    Abstract Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN More > Graphic Abstract

    Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

  • Open Access

    ARTICLE

    Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

    Abdullah A. Asiri1, Ahmad Shaf2,*, Tariq Ali2, Muhammad Aamir2, Ali Usman2, Muhammad Irfan3, Hassan A. Alshamrani1, Khlood M. Mehdar4, Osama M. Alshehri5, Samar M. Alqhtani6

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 127-143, 2023, DOI:10.32604/iasc.2023.032391

    Abstract The brain tumor is an abnormal and hysterical growth of brain tissues, and the leading cause of death affected patients worldwide. Even in this technology-based arena, brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones. The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data. To overcome the highlighted issue, a Generative Adversarial Network (GAN) deep learning technique… More >

  • Open Access

    ARTICLE

    Generative Adversarial Networks for Secure Data Transmission in Wireless Network

    E. Jayabalan*, R. Pugazendi

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3757-3784, 2023, DOI:10.32604/iasc.2023.031200

    Abstract In this paper, a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios. It is designed further to make their transmission decision that automatically adapts to the transmission dynamics to mitigate the launched jamming attacks. The generative adversarial learning neural network (GALNN) or generative dynamic neural network (GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompass minimax game theory. The elimination of the jamming attack is carried out with the assistance of the… More >

  • Open Access

    ARTICLE

    Artificially Generated Facial Images for Gender Classification Using Deep Learning

    Valliappan Raman1, Khaled ELKarazle2,*, Patrick Then2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1341-1355, 2023, DOI:10.32604/csse.2023.026674

    Abstract Given the current expansion of the computer vision field, several applications that rely on extracting biometric information like facial gender for access control, security or marketing purposes are becoming more common. A typical gender classifier requires many training samples to learn as many distinguishable features as possible. However, collecting facial images from individuals is usually a sensitive task, and it might violate either an individual's privacy or a specific data privacy law. In order to bridge the gap between privacy and the need for many facial images for deep learning training, an artificially generated dataset… More >

  • Open Access

    ARTICLE

    Hyper-Parameter Optimization of Semi-Supervised GANs Based-Sine Cosine Algorithm for Multimedia Datasets

    Anas Al-Ragehi1, Said Jadid Abdulkadir1,2,*, Amgad Muneer1,2, Safwan Sadeq3, Qasem Al-Tashi4,5

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2169-2186, 2022, DOI:10.32604/cmc.2022.027885

    Abstract Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep representations without requiring a large amount of training data. Semi-Supervised GAN Classifiers are a recent innovation in GANs, where GANs are used to classify generated images into real and fake and multiple classes, similar to a general multi-class classifier. However, GANs have a sophisticated design that can be challenging to train. This is because obtaining the proper set of parameters for all models-generator, discriminator, and classifier is complex. As a result, training a single GAN model for different datasets may not produce… More >

  • Open Access

    ARTICLE

    Optimized Generative Adversarial Networks for Adversarial Sample Generation

    Daniyal M. Alghazzawi1, Syed Hamid Hasan1,*, Surbhi Bhatia2

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3877-3897, 2022, DOI:10.32604/cmc.2022.024613

    Abstract Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times. Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic. We are using Deep Convolutional Generative Adversarial Networks (DCGAN) to trick the malware classifier to believe it is a normal entity. In this work, a new dataset is created to fool the Artificial Intelligence (AI) based malware detectors, and it consists of different types of attacks such as Denial of Service (DoS), scan 11, scan 44, botnet,… More >

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