Mahmoud Badry^1,*, Mohammed Hassanin^1,2, Asghar Chandio^2,3, Nour Moustafa²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1847-1858, 2021, DOI:10.32604/cmc.2021.015489

Abstract Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things (IoT) systems, much research has been done in using machine learning methods to recognize IoT sensors data. This is particularly the case for optical character recognition of handwritten scripts. Recognizing text in images has several useful applications, including content-based image retrieval, searching and document archiving. The Arabic language is one of the mostly used tongues in the world. However, Arabic text recognition in imagery is still very much in the nascent stage, especially handwritten text. This is mainly due to… More >

Sidharth Samanta¹, Mrutyunjaya Panda¹, Somula Ramasubbareddy², S. Sankar³, Daniel Burgos^4,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1937-1948, 2021, DOI:10.32604/cmc.2021.014406

Abstract Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) More >

Ying Cui¹, Shi Qiu^2,*, Tong Li³

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 417-428, 2021, DOI:10.32604/iasc.2021.016802

Abstract It is difficult to recognize a target object from foggy images. Aiming at solving this problem, a new algorithm is thereby proposed. Fog concentration estimation is the prerequisite for image defogging. Due to the uncertainty of fog distribution, a fog concentration estimation model is accordingly proposed. Establish the brightness and gradient model in the spatial domain, and establish the FFT model in the frequency domain. Also, a multiple branch network framework is established to realize the qualitative estimation of the fog concentration in images based on comprehensive analysis from spatial and frequency domain levels. In More >

Johnson Kinyua¹, Lawrence Awuah^2,*

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 527-545, 2021, DOI:10.32604/iasc.2021.016240

Abstract Today’s cyber defense capabilities in many organizations consist of a diversity of tools, products, and solutions, which are very challenging for Security Operations Centre (SOC) teams to manage in current advanced and dynamic cyber threat environments. Security researchers and industry practitioners have proposed security orchestration, automation, and response (SOAR) solutions designed to integrate and automate the disparate security tasks, processes, and applications in response to security incidents to empower SOC teams. The next big step for cyber threat detection, mitigation, and prevention efforts is to leverage AI/ML in SOAR solutions. AI/ML will act as a More >

Amjed Basil Abdulkareem¹, Nor Samsiah Sani^1,*, Shahnorbanun Sahran¹, Zaid Abdi Alkareem Alyessari¹, Afzan Adam¹, Abdul Hadi Abd Rahman¹, Abdulkarem Basil Abdulkarem²

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 305-320, 2021, DOI:10.32604/iasc.2021.015413

Abstract The coronavirus disease 2019 (COVID-19) has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet)… More >

Mei Sun¹, Qingtao Li², Peiguang Lin^2,*

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 369-378, 2021, DOI:10.32604/iasc.2021.014962

Abstract Stocks are the key components of most investment portfolios. The accurate forecasting of stock prices can help investors and investment brokerage firms make profits or reduce losses. However, stock forecasting is complex because of the intrinsic features of stock data, such as nonlinearity, long-term dependency, and volatility. Moreover, stock prices are affected by multiple factors. Various studies in this field have proposed ways to improve prediction accuracy. However, not all of the proposed features are valid, and there is often noise in the features—such as political, economic, and legal factors—which can lead to poor prediction… More >

Sameer I. Ali Al-Janabi¹, Belal Al-Khateeb^2,*, Maha Mahmood², Begonya Garcia-Zapirain³

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 293-303, 2021, DOI:10.32604/iasc.2021.014419

Abstract The recent novel coronavirus (COVID-19, as the World Health Organization has called it) has proven to be a source of risk for global public health. The virus, which causes an acute respiratory disease in persons, spreads rapidly and is now threatening more than 150 countries around the world. One of the essential procedures that patients with COVID-19 need is an accurate and rapid screening process. In this research, utilizing the features of deep learning methods, we present a method for detecting COVID-19 and a screening model that uses pulmonary computed tomography images to differentiate COVID-19 More >

Mohammed Ibrahim Ahmed Al-mashhadani¹, Theyazn H. H. Aldhyani^2,*, Mosleh Hmoud Al-Adhaileh³, Alwi M. Bamhdi⁴, Mohammed Y. Alzahrani⁵, Fawaz Waselallah Alsaade⁶, Hasan Alkahtani^1,6

Computer Systems Science and Engineering, Vol.38, No.1, pp. 25-37, 2021, DOI:10.32604/csse.2021.014992

Abstract Touch gesture recognition is an important aspect in human–robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human–animal affective robot touch (HAART) using a deep learning algorithm. The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015. The dataset was tested with numerous subjects performing different HAART gestures; each touch was performed on a robotic animal covered by a pressure sensor skin. A convolutional neural network algorithm is… More >

Jilong Bian^1,*, Jinfeng Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 175-189, 2021, DOI:10.32604/cmes.2021.014635

Abstract The stereo matching method based on a space-aware network is proposed, which divides the network into three sections: Basic layer, scale layer, and decision layer. This division is beneficial to integrate residue network and dense network into the space-aware network model. The vertical splitting method for computing matching cost by using the space-aware network is proposed for solving the limitation of GPU RAM. Moreover, a hybrid loss is brought forward to boost the performance of the proposed deep network. In the proposed stereo matching method, the space-aware network is used to calculate the matching cost More >

Jiongzhi Yang, Srivatsa Harish, Candy Li, Hengduo Zhao, Brittney Antous, Pinar Acar^*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1285-1302, 2021, DOI:10.32604/cmc.2021.016829

Abstract This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D More >