Jagmeet Kaur¹, Shakeel Ahmed², Yogesh Kumar³, A. Alaboudi⁴, N. Z. Jhanjhi⁵, Muhammad Fazal Ijaz^6,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2617-2633, 2021, DOI:10.32604/cmc.2021.017470

Abstract There has been an explosion of cloud services as organizations take advantage of their continuity, predictability, as well as quality of service and it raises the concern about latency, energy-efficiency, and security. This increase in demand requires new configurations of networks, products, and service operators. For this purpose, the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet optimization. This work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing requests. Performance is evaluated in terms of reducing bandwidth, task execution… More >

K. Muthumayil¹, S. Manikandan², S. Srinivasan³, José Escorcia-Gutierrez^4,*, Margarita Gamarra⁵, Romany F. Mansour⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2031-2044, 2021, DOI:10.32604/cmc.2021.017116

Abstract White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to… More >

Dengyong Zhang^1,2, Jiawei Hu^1,2, Feng Li^1,2,*, Xiangling Ding³, Arun Kumar Sangaiah⁴, Victor S. Sheng⁵

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1503-1517, 2021, DOI:10.32604/cmc.2021.017089

Abstract In the past several years, remarkable achievements have been made in the field of object detection. Although performance is generally improving, the accuracy of small object detection remains low compared with that of large object detection. In addition, localization misalignment issues are common for small objects, as seen in GoogLeNets and residual networks (ResNets). To address this problem, we propose an improved region-based fully convolutional network (R-FCN). The presented technique improves detection accuracy and eliminates localization misalignment by replacing position-sensitive region of interest (PS-RoI) pooling with position-sensitive precise region of interest (PS-Pr-RoI) pooling, which avoids coordinate quantization and directly calculates… More >

Lin Zhou^1,*, Yue Xu¹, Tianyi Wang¹, Kun Feng¹, Jingang Shi²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2705-2716, 2021, DOI:10.32604/cmc.2021.017080

Abstract Traditional separation methods have limited ability to handle the speech separation problem in high reverberant and low signal-to-noise ratio (SNR) environments, and thus achieve unsatisfactory results. In this study, a convolutional neural network with temporal convolution and residual network (TC-ResNet) is proposed to realize speech separation in a complex acoustic environment. A simplified steered-response power phase transform, denoted as GSRP-PHAT, is employed to reduce the computational cost. The extracted features are reshaped to a special tensor as the system inputs and implements temporal convolution, which not only enlarges the receptive field of the convolution layer but also significantly reduces the… More >

Naglaa F. Soliman^1,2, Abeer D. Algarni^1,*, Walid El-Shafai³, Fathi E. Abd El-Samie^1,3, Ghada M. El Banby⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1571-1595, 2021, DOI:10.32604/cmc.2021.016980

Abstract Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things (IoT) networks. The objective of using cancelable biometrics is to save the original ones from hacking attempts. A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT applications. The original biometric is blurred with two co-prime operators. Hence, it can be recovered as the Greatest Common Divisor (GCD) between its two blurred versions. Minimal changes if induced in the biometric… More >

Xingjian Xue^1,*, Zixu Wang¹, Linjuan Ge¹, Lirong Deng¹, Rui Song¹, Neal Naixue Xiong²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2779-2791, 2021, DOI:10.32604/cmc.2021.016885

Abstract Vehicle–bicycle conflict incurs a higher risk of traffic accidents, particularly as it frequently takes place at intersections. Mastering the traffic characteristics of vehicle–bicycle conflict and optimizing the design of intersections can effectively reduce such conflict. In this paper, the conflict between right-turning motor vehicles and straight-riding bicycles was taken as the research object, and T-Analyst video recognition technology was used to obtain data on riding (driving) behavior and vehicle–bicycle conflict at seven intersections in Changsha, China. Herein, eight typical traffic characteristics of vehicle–bicycle conflict are summarized, the causes of vehicle–bicycle conflict are analyzed using 18 factors in three dimensions, the… More >

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

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2671-2685, 2021, DOI:10.32604/cmc.2021.016856

Abstract In underground mining, the belt is a critical component, as its state directly affects the safe and stable operation of the conveyor. Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations. This tends to cause a large amount of calculation and low detection precision. To solve these problems, in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network (CDCGAN) was designed. In the traditional DCGAN, the image generated by the generator has a… More >

Jaegwang Lee, Jongsuk R. Lee, Sunil Ahn^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1611-1629, 2021, DOI:10.32604/cmc.2021.016803

Abstract With the increase in research on AI (Artificial Intelligence), the importance of DL (Deep Learning) in various fields, such as materials, biotechnology, genomes, and new drugs, is increasing significantly, thereby increasing the number of deep-learning framework users. However, to design a deep neural network, a considerable understanding of the framework is required. To solve this problem, a GUI (Graphical User Interface)-based DNN (Deep Neural Network) design tool is being actively researched and developed. The GUI-based DNN design tool can design DNNs quickly and easily. However, the existing GUI-based DNN design tool has certain limitations such as poor usability, framework dependency,… More >

Juan Huang¹, Bifang Zhou¹, Huajun Huang^2,*, Jianjiang Liu¹, Neal N. Xiong³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2111-2121, 2021, DOI:10.32604/cmc.2021.016770

Abstract To overcome the deficiency of traditional mathematical statistics methods, an adaptive Lasso grey model algorithm for regional FDI (foreign direct investment) prediction is proposed in this paper, and its validity is analyzed. Firstly, the characteristics of the FDI data in six provinces of Central China are generalized, and the mixture model's constituent variables of the Lasso grey problem as well as the grey model are defined. Next, based on the influencing factors of regional FDI statistics (mean values of regional FDI and median values of regional FDI), an adaptive Lasso grey model algorithm for regional FDI was established. Then, an… More >

Cheng Gong^1,2, Xinzhu Yang¹, Wei Huangfu^3,4,*, Qinghua Lu⁵

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2445-2457, 2021, DOI:10.32604/cmc.2021.016713

Abstract Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles (IoV). Mixture models are appropriate to describe complex spatial-temporal data. By calculating the expectation of hidden variables in vehicle communication, Expectation Maximization (EM) algorithm solves the maximum likelihood estimation of parameters, and then obtains the mixture model of vehicle communication opportunities. However, the EM algorithm requires multiple iterations and each iteration needs to process all the data. Thus its computational complexity is high. A parameter estimation algorithm with low computational complexity based on Bin Count (BC) and Differential Evolution (DE) (PEBCDE) is proposed. It overcomes the… More >