Junqiang Jiang^1,2, Zhifang Sun¹, Xiong Jiang¹, Shengjie Jin¹, Yinli Jiang³, Bo Fan^1,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1617-1644, 2023, DOI:10.32604/cmc.2023.041973 - 29 November 2023

Abstract The grey wolf optimizer (GWO) is a swarm-based intelligence optimization algorithm by simulating the steps of searching, encircling, and attacking prey in the process of wolf hunting. Along with its advantages of simple principle and few parameters setting, GWO bears drawbacks such as low solution accuracy and slow convergence speed. A few recent advanced GWOs are proposed to try to overcome these disadvantages. However, they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence. To solve the abovementioned issues, a high-accuracy variable grey wolf optimizer… More >

G. Uganya^*, Radhika Baskar

Computer Systems Science and Engineering, Vol.45, No.1, pp. 641-658, 2023, DOI:10.32604/csse.2023.028341 - 16 August 2022

Abstract Internet of Things (IoT) is an emerging technology that moves the world in the direction of smart things. But, IoT security is the complex problem due to its centralized architecture, and limited capacity. So, blockchain technology has great attention due to its features of decentralized architecture, transparency, immutable records and cryptography hash functions when combining with IoT. Cryptography hash algorithms are very important in blockchain technology for secure transmission. It converts the variable size inputs to a fixed size hash output which is unchangeable. Existing cryptography hash algorithms with digital signature have issues of single… More >

Mohammad Khalid Imam Rahmani^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4895-4913, 2022, DOI:10.32604/cmc.2022.023837 - 14 January 2022

Abstract In the present era, a very huge volume of data is being stored in online and offline databases. Enterprise houses, research, medical as well as healthcare organizations, and academic institutions store data in databases and their subsequent retrievals are performed for further processing. Finding the required data from a given database within the minimum possible time is one of the key factors in achieving the best possible performance of any computer-based application. If the data is already sorted, finding or searching is comparatively faster. In real-life scenarios, the data collected from different sources may not… More >

Haijun Geng^1,2,*, Han Zhang³, Yangyang Zhang⁴

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1733-1744, 2021, DOI:10.32604/cmc.2020.013355 - 26 November 2020

Abstract With an increasing urgent demand for fast recovery routing mechanisms in large-scale networks, minimizing network disruption caused by network failure has become critical. However, a large number of relevant studies have shown that network failures occur on the Internet inevitably and frequently. The current routing protocols deployed on the Internet adopt the reconvergence mechanism to cope with network failures. During the reconvergence process, the packets may be lost because of inconsistent routing information, which reduces the network’s availability greatly and affects the Internet service provider’s (ISP’s) service quality and reputation seriously. Therefore, improving network availability… More >

Sun-Taag Choe, We-Duke Cho^*, Jai-Hoon Kim, and Ki-Hyung Kim

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 679-691, 2020, DOI:10.32604/iasc.2020.010102

Abstract Recent research on activity recognition in wearable devices has identified a key challenge: k-nearest neighbors (k-NN) algorithms have a high operational time complexity. Thus, these algorithms are difficult to utilize in embedded wearable devices. Herein, we propose a method for reducing this complexity. We apply a clustering algorithm for learning data and assign labels to each cluster according to the maximum likelihood. Experimental results show that the proposed method achieves effective operational levels for implementation in embedded devices; however, the accuracy is slightly lower than that of a traditional k-NN algorithm. Additionally, our method provides More >