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  • 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 - 15 June 2022

    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

    Conditional Generative Adversarial Network Approach for Autism Prediction

    K. Chola Raja1,*, S. Kannimuthu2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 741-755, 2023, DOI:10.32604/csse.2023.025331 - 01 June 2022

    Abstract Autism Spectrum Disorder (ASD) requires a precise diagnosis in order to be managed and rehabilitated. Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD. The majority of available techniques in the literature use functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text, images, and videos, but their performance and robustness are restricted by the size of the accompanying… More >

  • Open Access

    REVIEW

    A Survey of GAN Based Image Synthesis

    Jiahe Ni*

    Journal of Information Hiding and Privacy Protection, Vol.4, No.2, pp. 79-88, 2022, DOI:10.32604/jihpp.2022.039751 - 17 April 2023

    Abstract Image generation is a hot topic in the academic recently, and has been applied to AI drawing, which can bring Vivid AI paintings without labor costs. In image generation, we represent the image as a random vector, assuming that the images of the natural scene obey an unknown distribution, we hope to estimate its distribution through some observation samples. Especially, with the development of GAN (Generative Adversarial Network), The generator and discriminator improve the model capability through adversarial, the quality of the generated image is also increasing. The image quality generated by the existing GAN More >

  • Open Access

    ARTICLE

    Production Dynamic Prediction Method of Waterflooding Reservoir Based on Deep Convolution Generative Adversarial Network (DC-GAN)

    Liyuan Xin1,2,3, Xiang Rao1,2,3,*, Xiaoyin Peng1,2,3, Yunfeng Xu1,2,3, Jiating Chen1,2,3

    Energy Engineering, Vol.119, No.5, pp. 1905-1922, 2022, DOI:10.32604/ee.2022.019556 - 21 July 2022

    Abstract The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has been the basis of production optimization of water-flooding reservoirs. Considering that the construction of geological models with traditional numerical simulation software is complicated, the computational efficiency of the simulation calculation is often low, and the numerical simulation tools need to be repeated iteratively in the process of model optimization, machine learning methods have been used for fast reservoir simulation. However, traditional artificial neural network (ANN) has large degrees of freedom, slow convergence speed, and complex network model. This paper aims to… More >

  • Open Access

    ARTICLE

    A Survey of Anti-forensic for Face Image Forgery

    Haitao Zhang*

    Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 41-51, 2022, DOI:10.32604/jihpp.2022.031707 - 17 June 2022

    Abstract Deep learning related technologies, especially generative adversarial network, are widely used in the fields of face image tampering and forgery. Forensics researchers have proposed a variety of passive forensic and related anti-forensic methods for image tampering and forgery, especially face images, but there is still a lack of overview of anti-forensic methods at this stage. Therefore, this paper will systematically discuss the anti-forensic methods for face image tampering and forgery. Firstly, this paper expounds the relevant background, including the relevant tampering and forgery methods and forensic schemes of face images. The former mainly includes four More >

  • Open Access

    ARTICLE

    Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network

    Yun Tan1,2, Weizhao Wu2, Ling Tan3, Haikuo Peng2, Jiaohua Qin2,*

    Journal of New Media, Vol.4, No.3, pp. 155-164, 2022, DOI:10.32604/jnm.2022.031113 - 13 June 2022

    Abstract At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network More >

  • Open Access

    ARTICLE

    Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model

    Jian Liu1,*, Xiaodong Xia1, Chunyang Han2, Jiao Hui3, Jim Feng4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 265-278, 2022, DOI:10.32604/cmc.2022.029227 - 18 May 2022

    Abstract As a common and high-risk type of disease, heart disease seriously threatens people’s health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decoder model. The model uses Long Short-Term 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 - 18 May 2022

    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

    Deep-sea Nodule Mineral Image Segmentation Algorithm Based on Pix2PixHD

    Wei Song1,2,3, Haolin Wang1, Xinping Zhang1, Jianxin Xia4,*, Tongmu Liu5, Yuxi Shi6

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1449-1462, 2022, DOI:10.32604/cmc.2022.027213 - 18 May 2022

    Abstract Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and evaluation. The application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of mining. The existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly longer. In order to improve the segmentation performance of underwater mineral images, this paper uses the Pix2PixHD (Pixel to Pixel High Definition) algorithm based on Conditional Generative Adversarial Network (CGAN) to segment deep-sea… 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 - 29 March 2022

    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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