He Gong^1,3,4, Mingwang Deng¹, Shijun Li^1,2,6,*, Tianli Hu^1,3,4, Yu Sun^1,3,4, Ye Mu^1,3,4, Zilian Wang¹, Chang Zhang¹, Thobela Louis Tyasi⁵

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4953-4969, 2022, DOI:10.32604/cmc.2022.027457

Abstract With the increasing intensive and large-scale development of the sika deer breeding industry, it is crucial to assess the health status of the sika deer by monitoring their behaviours. A machine vision–based method for the behaviour recognition of sika deer is proposed in this paper. Google Inception Net (GoogLeNet) is used to optimise the model in this paper. First, the number of layers and size of the model were reduced. Then, the 5 × 5 convolution was changed to two 3 × 3 convolutions, which reduced the parameters and increased the nonlinearity of the model. A 5 × 5 convolution… More >

Yongjiang Zhao, Jae Hung Yoo, Chang Gyoon Lim^*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5671-5686, 2022, DOI:10.32604/cmc.2022.027443

Abstract With the rapid economic growth and improved living standards, electricity has become an indispensable energy source in our lives. Therefore, the stability of the grid power supply and the conservation of electricity is critical. The following are some of the problems facing now: 1) During the peak power consumption period, it will pose a threat to the power grid. Enhancing and improving the power distribution infrastructure requires high maintenance costs. 2) The user's electricity schedule is unreasonable due to personal behavior, which will cause a waste of electricity. Controlling load as a vital part of incentive demand response (DR) can… More >

Guanghao Jin¹, Chunmei Pei¹, Na Zhao¹, Hengguang Li², Qingzeng Song³, Jing Yu^1,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5185-5196, 2022, DOI:10.32604/cmc.2022.027307

Abstract When utilizing the deep learning models in some real applications, the distribution of the labels in the environment can be used to increase the accuracy. Generally, to compute this distribution, there should be the validation set that is labeled by the ground truths. On the other side, the dependency of ground truths limits the utilization of the distribution in various environments. In this paper, we carried out a novel system for the deep learning-based classification to solve this problem. Firstly, our system only uses one validation set with ground truths to compute some hyper parameters, which is named as one-shot… More >

Xukun Zhang¹, Wei Song^1,2,3,*, Ganghua Lin^2,4, Yuxi Shi⁵

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6181-6193, 2022, DOI:10.32604/cmc.2022.027215

Abstract In ground-based observations of the Sun, solar images are often affected by appearance of thin clouds, which contaminate the images and affect the scientific results from data analysis. In this paper, the improved Pixel to Pixel Network (Pix2Pix) network is used to convert polluted images to clear images to remove the cloud shadow in the solar images. By adding attention module to the model, the hidden layer of Pix2Pix model can infer the attention map of the input feature vector according to the input feature vector. And then, the attention map is multiplied by the input feature map to give… More >

Timur Merembayev^1,2,*, Yedilkhan Amirgaliyev^1,3, Murat Kunelbayev¹, Didar Yedilkhan^1,4

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4627-4640, 2022, DOI:10.32604/cmc.2022.027180

Abstract In this paper, we studied theoretically and numerically heated losses of a flat solar collector to model the solar water heating system for the Kazakhstan climate condition. For different climatic zones with a growing cost for energy or lack of central heating systems, promising is to find ways to improve the energy efficiency of the solar system. The mathematical model (based on ordinary differential equation) simulated the solar system work process under different conditions. To bridge the modeling and real values results, we studied the important physical parameters such as loss coefficient, Nu, Ra, and Pr values. They impacted the… More >

Xiliang Zhang¹, Jin Liu^1,*, Yue Li¹, Zhongdai Wu^2,3, Y. Ken Wang⁴

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6407-6424, 2022, DOI:10.32604/cmc.2022.027097

Abstract Performance of Video Question and Answer (VQA) systems relies on capturing key information of both visual images and natural language in the context to generate relevant questions’ answers. However, traditional linear combinations of multimodal features focus only on shallow feature interactions, fall far short of the need of deep feature fusion. Attention mechanisms were used to perform deep fusion, but most of them can only process weight assignment of single-modal information, leading to attention imbalance for different modalities. To address above problems, we propose a novel VQA model based on Triple Multimodal feature Cyclic Fusion (TMCF) and Self-Adaptive Multimodal Balancing… More >

Akbar Khan¹, Kushsairy Abdul Kadir^1,*, Jawad Ali Shah², Waleed Albattah³, Muhammad Saeed⁴, Haidawati Nasir⁵, Megat Norulazmi Megat Mohamed Noor⁵, Muhammad Haris Kaka Khel¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5825-5844, 2022, DOI:10.32604/cmc.2022.027077

Abstract With the rapid progress of deep convolutional neural networks, several applications of crowd counting have been proposed and explored in the literature. In congested scene monitoring, a variety of crowd density estimating approaches has been developed. The understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task, as a large number of individuals stand nearby and, it is hard for detection techniques to recognize them, as the crowd can vary from low density to high density. To deal with such highly congested scenes, we have proposed the Congested Scene Crowd Counting… More >

Mu Ye^1,2,3,4, Li Ji¹, Luo Tianye¹, Li Sihan⁵, Zhang Tong¹, Feng Ruilong¹, Hu Tianli^1,2,3,4, Gong He^1,2,3,4, Guo Ying^1,2,3,4, Sun Yu^1,2,3,4, Thobela Louis Tyasi⁶, Li Shijun^7,8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6195-6205, 2022, DOI:10.32604/cmc.2022.026880

Abstract Remote sensing image analysis is a basic and practical research hotspot in remote sensing science. Remote sensing images contain abundant ground object information and it can be used in urban planning, agricultural monitoring, ecological services, geological exploration and other aspects. In this paper, we propose a lightweight model combining vgg-16 and u-net network. By combining two convolutional neural networks, we classify scenes of remote sensing images. While ensuring the accuracy of the model, try to reduce the memory of the model. According to the experimental results of this paper, we have improved the accuracy of the model to 98%. The… More >

Eun-Tae Jang¹, Sung Hoon Baek², Ki-Woong Park^1,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5091-5102, 2022, DOI:10.32604/cmc.2022.026663

Abstract When a Windows-based system is used for an exceedingly long time, its performance degrades, and the error occurrence rate tends to increase. This is generally called system aging. To investigate the reasons for system aging, various studies have been conducted within the range of the operating system kernel to the user application. However, finding an accurate reason for system performance degradation remains challenging research topic. In this study, system monitoring was conducted by dividing a system into ‘before software installation,’ ‘after software installation,’ and ‘after software removal.’ We confirmed that when a software installed in a system is removed, various… More >

Samah Alshathri^1,*, Fatma M. Talaat², Aida A. Nasr³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5863-5885, 2022, DOI:10.32604/cmc.2022.026547

Abstract Virtual cloud network (VCN) usage is popular today among large and small organizations due to its safety and money-saving. Moreover, it makes all resources in the company work as one unit. VCN also facilitates sharing of files and applications without effort. However, cloud providers face many issues in managing the VCN on cloud computing including these issues: Power consumption, network failures, and data availability. These issues often occur due to overloaded and unbalanced load tasks. In this paper, we propose a new automatic system to manage VCN for executing the workflow. The new system called Multi-User Hybrid Scheduling (MUSH) can… More >