Yan Zhang^1,2, Jin Han^1,2,*, Chengsheng Yuan^1,2, Shuo Yang³, Chuanlong Li^1,2, Xingming Sun^1,2

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 563-574, 2021, DOI:10.32604/cmc.2020.012502

Abstract Over the past 10 years, lightning disaster has caused a large number of casualties and considerable economic loss worldwide. Lightning poses a huge threat to various industries. In an attempt to reduce the risk of lightning-caused disaster, many scholars have carried out in-depth research on lightning. However, these studies focus primarily on the lightning itself and other meteorological elements are ignored. In addition, the methods for assessing the risk of lightning disaster fail to give detailed attention to regional features (lightning disaster risk). This paper proposes a grid-based risk assessment method based on data from multiple sources. First, this paper… More >

Gia Nhu Nguyen^1,2, Nin Ho Le Viet^1,2, Gyanendra Prasad Joshi³, Bhanu Shrestha^4,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 551-562, 2021, DOI:10.32604/cmc.2020.012441

Abstract Fog computing in the Internet of Health Things (IoHT) is promising owing to the increasing need for energy- and latency-optimized health sector provisioning. Additionally, clinical data (particularly, medical image data) are a delicate, highly protected resource that should be utilized in an effective and responsible manner to fulfil consumer needs. Herein, we propose an energy-effi- cient fog-based IoHT with a tunicate swarm-optimization-(TSO)-based lightweight Simon cipher to enhance the energy efficiency at the fog layer and the security of data stored at the cloud server. The proposed Simon cipher uses the TSO algorithm to select the optimal keys that will minimize… More >

Muhammad Arif¹, Dost Muhammad Khan¹, Saima Khan Khosa², Muhammad Aamir¹, Adnan Aslam³, Zubair Ahmad⁴, Wei Gao^5,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 537-550, 2021, DOI:10.32604/cmc.2020.012420

Abstract The actuaries always look for heavy-tailed distributions to model data relevant to business and actuarial risk issues. In this article, we introduce a new class of heavy-tailed distributions useful for modeling data in financial sciences. A specific sub-model form of our suggested family, named as a new extended heavy-tailed Weibull distribution is examined in detail. Some basic characterizations, including quantile function and raw moments have been derived. The estimates of the unknown parameters of the new model are obtained via the maximum likelihood estimation method. To judge the performance of the maximum likelihood estimators, a simulation analysis is performed in… More >

Yunping Zhao, Jianzhuang Lu^*, Xiaowen Chen

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 517-535, 2021, DOI:10.32604/cmc.2020.012380

Abstract Convolutional Neural Networks (CNNs) are widely used in many fields. Due to their high throughput and high level of computing characteristics, however, an increasing number of researchers are focusing on how to improve the computational efficiency, hardware utilization, or flexibility of CNN hardware accelerators. Accordingly, this paper proposes a dynamically reconfigurable accelerator architecture that implements a Sparse-Winograd F(2 2.3 3)-based high-parallelism hardware architecture. This approach not only eliminates the pre-calculation complexity associated with the Winograd algorithm, thereby reducing the difficulty of hardware implementation, but also greatly improves the flexibility of the hardware; as a result, the accelerator can realize the… More >

Lejun Zhang^1,2,*, Minghui Peng¹, Weizheng Wang³ , Yansen Su⁴, Shuna Cui^5,6, Seokhoon Kim⁷

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 499-515, 2021, DOI:10.32604/cmc.2020.012205

Abstract In the digital era, electronic medical record (EMR) has been a major way for hospitals to store patients’ medical data. The traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data sharing. The storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low throughput. To address these issues, we propose a secure and efficient medical data storage and sharing scheme based on double blockchain. In our scheme, we encrypt the original EMR and store it in the cloud. The storage blockchain stores the index of the… More >

Sajjad Haider^*, Muhammad Tanvir Afzal

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 481-498, 2021, DOI:10.32604/cmc.2020.012057

Abstract Social networking platforms provide a vital source for disseminating information across the globe, particularly in case of disaster. These platforms are great mean to find out the real account of the disaster. Twitter is an example of such platform, which has been extensively utilized by scientific community due to its unidirectional model. It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users. Research community has proposed diverse sets of techniques to identify eyewitness account. A recent state-of-the-art approach has proposed a comprehensive set of features to identify eyewitness… More >

Muhammad Adnan Khan^1,*, Abdur Rehman², Khalid Masood Khan¹, Mohammed A. Al Ghamdi³, Sultan H. Almotiri³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 467-480, 2021, DOI:10.32604/cmc.2020.013121

Abstract Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular… More >

Zongshuai Liu¹, Xuyu Xiang^1,2,*, Jiaohua Qin¹, Yun Tan¹, Qin Zhang¹, Neal N. Xiong³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 457-466, 2021, DOI:10.32604/cmc.2020.012165

Abstract In recent years, with the development of machine learning and deep learning, it is possible to identify and even control crop diseases by using electronic devices instead of manual observation. In this paper, an image recognition method of citrus diseases based on deep learning is proposed. We built a citrus image dataset including six common citrus diseases. The deep learning network is used to train and learn these images, which can effectively identify and classify crop diseases. In the experiment, we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,… More >

Changli Lu¹, Ming Zhao^1,2, Imran Khan³, Peerapong Uthansakul^4,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 437-455, 2021, DOI:10.32604/cmc.2020.012557

Abstract Cap-and-trade regulation provides incentives for manufacturers to reduce carbon emissions, but manufacturers’ insufficient capital can disrupt the implementation of low-carbon emission reduction technologies. To alleviate capital constraints, manufacturers can adopt external financing for low-carbon emission reduction investments. This paper studies the independent financing and financing cooperation behavior in a supply chain in which the manufacturer and retailer first implement low-carbon emission reduction technologies and then organize production and sales in accordance with wholesale price contracts. Through comparing the optimal profits and low-carbon emission reduction levels under the independent financing and financing cooperation mode, we come to the following conclusions: (1)… More >

Ahlam Aljabali¹, Abdul Rahman Mohd Kasim^1,*, Nur Syamilah Arifin², Sharena Mohamad Isa³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 421-435, 2021, DOI:10.32604/cmc.2020.012322

Abstract The viscosity of a substance or material is intensely influenced by the temperature, especially in the field of lubricant engineering where the changeable temperature is well executed. In this paper, the problem of temperature-dependent viscosity on mixed convection flow of Eyring Powell fluid was studied together with Newtonian heating thermal boundary condition. The flow was assumed to move over a vertical stretching sheet. The model of the problem, which is in partial differential equations, was first transformed to ordinary differential equations using appropriate transformations. This approach was considered to reduce the complexity of the equations. Then, the transformed equations were… More >