Prem Junswang¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Waleed Adel^4,5, Thongchai Botmart^6,*, Wajaree Weera⁶

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5015-5030, 2022, DOI:10.32604/cmc.2022.027523

Abstract The purpose of this paper is to present a numerical approach based on the artificial neural networks (ANNs) for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented system. The method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation (LMB), named the ANNs-LMB. This technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference solution. Fifteen numbers neurons have been used to solve the fractional-order chaotic financial model. The selection of the… More >

Esraa Hassan^1,3,*, Mahmoud Y. Shams¹, Noha A. Hikal², Samir Elmougy³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5889-5907, 2022, DOI:10.32604/cmc.2022.025629

Abstract Infectious diseases are an imminent danger that faces human beings around the world. Malaria is considered a highly contagious disease. The diagnosis of various diseases, including malaria, was performed manually, but it required a lot of time and had some human errors. Therefore, there is a need to investigate an efficient and fast automatic diagnosis system. Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis. This study proposed a Convolutional Neural Network (CNN) model to detect malaria automatically. A Malaria Convolutional Neural Network (MCNN)… More >

Ankur Dumka¹, Parag Verma², Rajesh Singh³, Anil Kumar Bisht⁴, Divya Anand^5,6,*, Hani Moaiteq Aljahdali⁷, Irene Delgado Noya^6,8, Silvia Aparicio Obregon^6,9

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6029-6044, 2022, DOI:10.32604/cmc.2022.024698

Abstract Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the… More >

S. Hariharan^1,*, R. Venkatesan²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 407-422, 2022, DOI:10.32604/iasc.2022.025309

Abstract Background subtraction is a fundamental and crucial task for computer vision-based automatic video analysis due to various challenging situations that occur in real-world scenarios. This paper presents a novel background subtraction method by estimating the background model using linear regression and local spectral histogram which captures combined spectral and texture features. Different linear filters are applied on the image window centered at each pixel location and the features are captured via these filter responses. Each feature has been approximated by a linear combination of two representative features, each of which corresponds to either a background or a foreground pixel. These… More >

Ghalib H. Alshammri^1,2, Amani K. Samha³, Ezz El-Din Hemdan⁴, Mohammed Amoon^1,4, Walid El-Shafai^5,6,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3529-3548, 2022, DOI:10.32604/cmc.2022.025262

Abstract Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process. In recent times, the most complex task in Software Defined Network (SDN) is security, which is based on a centralized, programmable controller. Therefore, monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment. Consequently, this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms: K-means, Farthest First, Canopy, Density-based algorithm, and Exception-maximization (EM), using the Waikato Environment for Knowledge Analysis (WEKA) software to compare extensively between these five algorithms.… More >

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 >

Seung-Yeon Hwang¹, Jeong-Joon Kim^2,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2649-2663, 2022, DOI:10.32604/cmc.2022.022593

Abstract Artificial intelligence, which has recently emerged with the rapid development of information technology, is drawing attention as a tool for solving various problems demanded by society and industry. In particular, convolutional neural networks (CNNs), a type of deep learning technology, are highlighted in computer vision fields, such as image classification and recognition and object tracking. Training these CNN models requires a large amount of data, and a lack of data can lead to performance degradation problems due to overfitting. As CNN architecture development and optimization studies become active, ensemble techniques have emerged to perform image classification by combining features extracted… More >

Zulqurnain Sabir¹, Manoj Gupta², Muhammad Asif Zahoor Raja³, N. Seshagiri Rao⁴, Muhammad Mubashar Hussain⁵, Faisal Alanazi⁶, Orawit Thinnukool⁷, Pattaraporn Khuwuthyakorn^7,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1627-1644, 2022, DOI:10.32604/cmc.2022.021462

Abstract The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model (NSM) using the supervised neural networks (SNNs) along with the novel features of Levenberg-Marquardt backpropagation technique (LMBT), i.e., SNNs-LMBT. The SNNs-LMBT is implemented with three different types of sample data, authentication, testing and training. The ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75% for training, 15% for validation and 10% for testing, respectively. For the numerical measures of the nonlinear dynamics of the NSM, the Runge-Kutta scheme is implemented to form… More >

Dina M. Ibrahim^1,2,*, Nada M. Elshennawy²

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 331-348, 2022, DOI:10.32604/cmes.2022.016419

Abstract Dates are an important part of human nutrition. Dates are high in essential nutrients and provide a number of health benefits. Date fruits are also known to protect against a number of diseases, including cancer and heart disease. Date fruits have several sizes, colors, tastes, and values. There are a lot of challenges facing the date producers. One of the most significant challenges is the classification and sorting of dates. But there is no public dataset for date fruits, which is a major limitation in order to improve the performance of convolutional neural networks (CNN) models and avoid the overfitting… More >

Shihao Zhou¹, Ziyuan Xu^3,4, Jin Han^1,*, Xingming Sun^1,2, Yi Cao⁵

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5995-6009, 2022, DOI:10.32604/cmc.2022.023316

Abstract In recent years, academic misconduct has been frequently exposed by the media, with serious impacts on the academic community. Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript. For the most part, these techniques can only detect cases of textual plagiarism, which means that potential culprits can easily avoid discovery through clever editing and alterations of text content. In this paper, we propose an academic misconduct detection method based on scholars’ submission behaviors. The model can effectively capture the atypical… More >