Abdulaziz Almehmadi^*

Computer Systems Science and Engineering, Vol.36, No.3, pp. 451-460, 2021, DOI:10.32604/csse.2021.015309

Abstract Unlike the 2007–2008 market crash, which was caused by a banking failure and led to an economic recession, the 1918 influenza pandemic triggered a worldwide financial depression. Pandemics usually affect the global economy, and the COVID-19 pandemic is no exception. Many stock markets have fallen over 40%, and companies are shutting down, ending contracts, and issuing voluntary and involuntary leaves for thousands of employees. These economic effects have led to an increase in unemployment rates, crime, and instability. Studying pandemics’ economic effects, especially on the stock market, has not been urgent or feasible until recently. However, with advances in artificial… More >

Mohammad Aldossary^*

Computer Systems Science and Engineering, Vol.36, No.3, pp. 461-476, 2021, DOI:10.32604/csse.2021.014975

Abstract In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive… More >

Adel Saad Assiri^*

Computer Systems Science and Engineering, Vol.36, No.3, pp. 435-450, 2021, DOI:10.32604/csse.2021.014894

Abstract Artificial neural networks (ANNs) are one of the hottest topics in computer science and artificial intelligence due to their potential and advantages in analyzing real-world problems in various disciplines, including but not limited to physics, biology, chemistry, and engineering. However, ANNs lack several key characteristics of biological neural networks, such as sparsity, scale-freeness, and small-worldness. The concept of sparse and scale-free neural networks has been introduced to fill this gap. Network sparsity is implemented by removing weak weights between neurons during the learning process and replacing them with random weights. When the network is initialized, the neural network is fully… More >

Muhammad Javed Azhar Khan¹, Muhammad Ibrahim^1,*, Ali Ahmad²

Computer Systems Science and Engineering, Vol.36, No.3, pp. 485-492, 2021, DOI:10.32604/csse.2021.014810

Abstract Among the huge diversity of ideas that show up while studying graph theory, one that has obtained a lot of popularity is the concept of labelings of graphs. Graph labelings give valuable mathematical models for a wide scope of applications in high technologies (cryptography, astronomy, data security, various coding theory problems, communication networks, etc.). A labeling or a valuation of a graph is any mapping that sends a certain set of graph elements to a certain set of numbers subject to certain conditions. Graph labeling is a mapping of elements of the graph, i.e., vertex and/or edges to a set… More >

Yanyun Jiang¹, Yuanjie Zheng^1,2,*, Xiaodan Sui¹, Wanzhen Jiao³, Yunlong He⁴, Weikuan Jia¹

Computer Systems Science and Engineering, Vol.36, No.3, pp. 537-549, 2021, DOI:10.32604/csse.2021.014578

Abstract Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation… More >

Jingyu Zhang^1,2, Siqi Zhong¹, Jin Wang^1,3, Xiaofeng Yu^4,*, Osama Alfarraj⁵

Computer Systems Science and Engineering, Vol.36, No.3, pp. 521-535, 2021, DOI:10.32604/csse.2021.014530

Abstract As the typical peer-to-peer distributed networks, blockchain systems require each node to copy a complete transaction database, so as to ensure new transactions can by verified independently. In a blockchain system (e.g., bitcoin system), the node does not rely on any central organization, and every node keeps an entire copy of the transaction database. However, this feature determines that the size of blockchain transaction database is growing rapidly. Therefore, with the continuous system operations, the node memory also needs to be expanded to support the system running. Especially in the big data era, the increasing network traffic will lead to… More >

Xin Chen¹, Ying Zhang², Lang Lin¹, Junxiang Wang^2,*, Jiangqun Ni³

Computer Systems Science and Engineering, Vol.36, No.3, pp. 551-564, 2021, DOI:10.32604/csse.2021.014495

Abstract Nowadays the computer vision technique has widely found applications in industrial manufacturing process to improve their efficiency. However, it is hardly applied in the field of daily ceramic detection due to the following two key reasons: (1) Low detection accuracy as a result of ceramic glare, and (2) Lack of efficient detection algorithms. To tackle these problems, a homomorphic filtering based anti-glare ceramic decals defect detection technique is proposed in this paper. Considering that smooth ceramic surface usually causes glare effects and leads to low detection results, in our approach, the ceramic samples are taken in low light environment and… More >

Mudassir Shams^1,*, Naila Rafiq², Nazir Ahmad Mir^1,2, Babar Ahmad³, Saqib Abbasi¹, Mutee-Ur-Rehman Kayani¹

Computer Systems Science and Engineering, Vol.36, No.3, pp. 493-507, 2021, DOI:10.32604/csse.2021.014476

Abstract In this research article, we interrogate two new modifications in inverse Weierstrass iterative method for estimating all roots of non-linear equation simultaneously. These modifications enables us to accelerate the convergence order of inverse Weierstrass method from 2 to 3. Convergence analysis proves that the orders of convergence of the two newly constructed inverse methods are 3. Using computer algebra system Mathematica, we find the lower bound of the convergence order and verify it theoretically. Dynamical planes of the inverse simultaneous methods and classical iterative methods are generated using MATLAB (R2011b), to present the global convergence properties of inverse simultaneous iterative… More >

Praveena Rajasekaran^*, Preetha Jaganathan, Anjali Annadurai

Computer Systems Science and Engineering, Vol.36, No.3, pp. 477-484, 2021, DOI:10.32604/csse.2021.014247

Abstract The stabilizing mechanisms of cervical spine spondylosis are involved in the degenerating segmentation vertebra, which often causes pain. Health of the individual is affected, both physically and mentally. Due to depression, nervousness, and psychological damages occur thereby losing their human activity functions. The nucleus pulposus of spinal disc herniation is prolapsed through a deficiency of annulus fibrosus. A jelly-like core part of the disc contains proteins that cause the tissues to become swollen when it touches the nucleus pulposus. The proposed Gradient Linear Classification (GLC) algorithm is used for the efficient automatic classification of disc degeneration herniation of Inter vertebral/… More >

Guang Sun¹, Jingjing Lin^1,*, Chen Yang¹, Xiangyang Yin¹, Ziyu Li¹, Peng Guo^1,2, Junqi Sun³, Xiaoping Fan¹, Bin Pan¹

Computer Systems Science and Engineering, Vol.36, No.3, pp. 509-520, 2021, DOI:10.32604/csse.2021.014189

Abstract Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean… More >