V. Murugesh¹, B. Saravana Balaji^2,*, Habib Sano Aliy³, J. Bhuvana⁴, P. Saranya⁵, Andino Maseleno⁶, K. Shankar⁷, A. Sasikala⁸

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3685-3692, 2021, DOI:10.32604/cmc.2021.017836 - 24 August 2021

Abstract Bilinear singular systems can be used in the investigation of different types of engineering systems. In the past decade, considerable attention has been paid to analyzing and synthesizing singular bilinear systems. Their importance lies in their real world application such as economic, ecological, and socioeconomic processes. They are also applied in several biological processes, such as population dynamics of biological species, water balance, temperature regulation in the human body, carbon dioxide control in lungs, blood pressure, immune system, cardiac regulation, etc. Bilinear singular systems naturally represent different physical processes such as the fundamental law of… More >

Fatmah Abdulrahman Baothman^*, Osama Ahmed Abulnaja, Fatima Jafar Muhdher

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3337-3363, 2021, DOI:10.32604/cmc.2021.017637 - 24 August 2021

Abstract Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements; time, capacity, speed, and culture. Cross-cultural variations are increasing the complexity level because each mass and event have different characteristics and challenges. However, no prior study has employed the six Hofstede Cultural Dimensions (HCD) for predicting crowd behaviors. This study aims to develop the Cultural Crowd-Artificial Neural Network (CC-ANN) learning model that considers crowd’s HCD to predict their physical (distance and speed) and social (collectivity and cohesion) characteristics. The model was developed towards a cognitive intelligent decision support tool where the… More >

Ziadoon K. Maseer¹, Robiah Yusof¹, Salama A. Mostafa^2,*, Nazrulazhar Bahaman¹, Omar Musa³, Bander Ali Saleh Al-rimy⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3945-3966, 2021, DOI:10.32604/cmc.2021.016074 - 24 August 2021

Abstract With an increasing number of services connected to the internet, including cloud computing and Internet of Things (IoT) systems, the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points. Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. However, due to the high dynamics and imbalanced nature of the data, the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks. Therefore,… More >

Mohammad Tabrez Quasim¹, Saad Alhuwaimel^2,*, Asadullah Shaikh³, Yousef Asiri³, Khairan Rajab³, Rihem Farkh^4,5, Khaled Al Jaloud⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4169-4181, 2021, DOI:10.32604/cmc.2021.015984 - 24 August 2021

Abstract Heart disease is the leading cause of death worldwide. Predicting heart disease is challenging because it requires substantial experience and knowledge. Several research studies have found that the diagnostic accuracy of heart disease is low. The coronary heart disorder determines the state that influences the heart valves, causing heart disease. Two indications of coronary heart disorder are strep throat with a red persistent skin rash, and a sore throat covered by tonsils or strep throat. This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness. At first, we More >

Muhammad Ramzan¹, Mudassar Raza¹, Muhammad Sharif¹, Muhammad Attique Khan², Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3239-3257, 2021, DOI:10.32604/cmc.2021.015920 - 24 August 2021

Abstract Automatic gastrointestinal (GI) tract disease recognition is an important application of biomedical image processing. Conventionally, microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract. The procedure is subjective and results in significant inter-/intra-observer variations in disease detection. Moreover, a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination. Consequently, there is a huge demand for a reliable computer-aided diagnostic system (CADx) for diagnosing GI tract diseases. In this work, a CADx was proposed for the… More >

Shabana Habib¹, Noreen Fayyaz Khan^2,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3321-3335, 2021, DOI:10.32604/cmc.2021.015504 - 24 August 2021

Abstract Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used… More >

Qasim M. Zainel¹, Murad B. Khorsheed², Saad Darwish^3,*, Amr A. Ahmed⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3813-3828, 2021, DOI:10.32604/cmc.2021.014759 - 24 August 2021

Abstract Convolutional Neural Networks (CNNs) models succeed in vast domains. CNNs are available in a variety of topologies and sizes. The challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the architecture. Our proposed framework to automated design is aimed at resolving this problem. The proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best fit. In comparison to the co-authored work,… More >

Daxing Zhang^1,*, Erguan Cai²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3931-3943, 2021, DOI:10.32604/cmc.2021.012302 - 24 August 2021

Abstract Previous research in the area of using deep learning algorithms to forecast stock prices was focused on news headlines, company reports, and a mix of daily stock fundamentals, but few studies achieved excellent results. This study uses a convolutional neural network (CNN) to predict stock prices by considering a great amount of data, consisting of financial news headlines. We call our model N-CNN to distinguish it from a CNN. The main concept is to narrow the diversity of specific stock prices as they are impacted by news headlines, then horizontally expand the news headline data… More >

Jiahui Pan^1,2,*, Liangxin Liu¹, Mianfen Lin¹, Shengzhou Luo¹, Chengju Zhou¹, Huijian Liao³, Fei Wang^1,2

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 673-688, 2021, DOI:10.32604/iasc.2021.020240 - 11 August 2021

Abstract Abnormal behavior detection is an essential step in a wide range of application domains, such as smart video surveillance. In this study, we proposed an improved two-stream inflated 3D ConvNet network approach based on probability regression for abnormal behavior detection. The proposed approach consists of four parts: (1) preprocessing pretreatment for the input video; (2) dynamic feature extraction from video streams using a two-stream inflated 3D (I3D) ConvNet network; (3) visual feature transfer into a two-dimensional matrix; and (4) feature classification using a generalized regression neural network (GRNN), which ultimately achieves a probability regression. Compared… More >

Yujie Zhou¹, Jin Liu^1,*, Yurong Xie¹, Y. Ken Wang²

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 715-733, 2021, DOI:10.32604/iasc.2021.020184 - 11 August 2021

Abstract Text recognition is a crucial and challenging task, which aims at translating a cropped text instance image into a target string sequence. Recently, Convolutional neural networks (CNN) have been widely used in text recognition tasks as it can effectively capture semantic and structural information in text. However, most existing methods are usually based on contextual clues. If only recognize a single character, the accuracy of these approaches can be reduced. For example, it is difficult to distinguish 0 and O in the traditional CNN network because they are very similar in composition and structure. To… More >