Daniyal M. Alghazzawi¹, Syed Hamid Hasan^1,*, 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, spam, User Datagram Portal (UDP)… More >

Jong-In Choi¹, Soo-Kyun Kim², Shin-Jin Kang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 747-762, 2022, DOI:10.32604/cmes.2022.018201

Abstract Two-dimensional (2D) character animation is one of the most important visual elements on which users’ interest is focused in the game field. However, 2D character animation works in the game field are mostly performed manually in two dimensions, thus generating high production costs. This study proposes a generative adversarial network based production tool that can easily and quickly generate the sprite images of 2D characters. First, we proposed a methodology to create a synthetic dataset for training using images from the real world in the game resource production field where machine learning datasets are insufficient. In addition, we have enabled… More >

Biying Deng¹ , Desheng Zheng^{1, *}, Zhifeng Liu¹ , Yanling Lai¹, Zhihong Zhang²

Journal of Quantum Computing, Vol.3, No.3, pp. 119-126, 2021, DOI:10.32604/jqc.2021.017250

Abstract There are two difficult in the existing image restoration methods. One is that the method is difficult to repair the image with a large damaged, the other is the result of image completion is not good and the speed is slow. With the development and application of deep learning, the image repair algorithm based on generative adversarial networks can repair images by simulating the distribution of data. In the process of image completion, the first step is trained the generator to simulate data distribution and generate samples. Then a large number of falsified images are quickly generated using the generative… More >

Alaa Eisa¹, Nora EL-Rashidy², Mohammad Dahman Alshehri^3,*, Hazem M. El-bakry¹, Samir Abdelrazek¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2901-2921, 2022, DOI:10.32604/cmc.2022.021342

Abstract At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant… More >

Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun^*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143

Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a technique used for increasing the… More >

Shinjin Kang¹, Jongin Choi^2,*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1089-1105, 2022, DOI:10.32604/iasc.2022.019979

Abstract The game user interface (UI) provides a large volume of information necessary to analyze the game screen. The availability of such information can be functional in vision-based machine learning algorithms. With this, there will be an enhancement in the application power of vision deep learning neural networks. Therefore, this paper proposes a game UI segmentation technique based on unsupervised learning. We developed synthetic labeling created on the game engine, image-to-image translation and segmented UI components in the game. The network learned in this manner can segment the target UI area in the target game regardless of the location of the… More >

Jiaming Mao^1,*, Mingming Zhang¹, Mu Chen², Lu Chen², Fei Xia¹, Lei Fan¹, ZiXuan Wang³, Wenbing Zhao⁴

Computer Systems Science and Engineering, Vol.39, No.3, pp. 373-390, 2021, DOI:10.32604/csse.2021.018086

Abstract The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal… More >

Xiaoli Hao^1,*, Xiaojuan Meng¹, Yueqin Zhang¹, Jindong Xue², Jinyue Xia³

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 601-613, 2021, DOI:10.32604/iasc.2021.017963

Abstract The belt conveyor is essential in coal mine underground transportation. The belt properties directly affect the safety of the conveyor. It is essential to monitor that the belt works well. Traditional non-contact detection methods are usually time-consuming, and they only identify a single instance of damage. In this paper, a new belt-tear detection method is developed, characterized by two time-scale update rules for a multi-class deep convolution generative adversarial network. To use this method, only a small amount of image data needs to be labeled, and batch normalization in the generator must be removed to avoid artifacts in the generated… More >

Tao Zhang^1,2, Zhanjie Zhang^1,2,*, Wenjing Jia³, Xiangjian He³, Jie Yang⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2733-2747, 2021, DOI:10.32604/cmc.2021.019305

Abstract The generative adversarial network (GAN) is first proposed in 2014, and this kind of network model is machine learning systems that can learn to measure a given distribution of data, one of the most important applications is style transfer. Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image. CYCLE-GAN is a classic GAN model, which has a wide range of scenarios in style transfer. Considering its unsupervised learning characteristics, the mapping is easy to be learned between an input image and an output… More >

Aswathy K. Cherian^*, E. Poovammal

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1537-1552, 2021, DOI:10.32604/cmc.2021.018213

Abstract Obtaining clear images of underwater scenes with descriptive details is an arduous task. Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors. Consequently, a need for a system that produces clear images for underwater image study has been necessitated. To overcome problems in resolution and to make better use of the Super-Resolution (SR) method, this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network (AlphaGAN) model, named Alpha Super Resolution Generative Adversarial Network (AlphaSRGAN). The model put forth in this paper helps in enhancing the… More >