Study on preparation and structure of chrysanthemum-shaped micron calcium carbonate based on inverse microemulsion

Micro & nano letters

article

Tiaobin Zhao¹  Chao Xu¹  Yitong Ma¹  Yiwen Zeng²  Nong Wang¹ 

[1] School of Chemical and Biological Engineering, Lanzhou Jiaotong University;College of Materials and Chemical Engineering, Hezhou University

关键词: calcium compounds;    X-ray diffraction;    nanofabrication;    scanning electron microscopy;    surface morphology;    microemulsions;    Fourier transform infrared spectra;    organic compounds;    nanoparticles;    n-hexane inverse microemulsion system;    chrysanthemum-shaped micron particles;    CTAB;    n-butanol;    scanning electron microscopy;    X-ray powder diffraction;    Fourier transform infrared spectroscopy;    surface morphology;    CaCO3;    CaCl2;   

DOI  :  10.1049/mnl.2020.0349

学科分类：计算机科学（综合）

来源: Wiley

【 摘 要 】

The internet made an individual’s life very easy and more productive, but there are some associated threats linked to the internet and devices. Malware is considered the most severe threat for decades to the digital world and malware variants identification and classification is the most vital and critical research problem. It is an invasive malicious code that accesses devices, information, and services without the permission, knowledge of the user. Researchers, analysts and antivirus companies are incessantly inventing and implementing new strategies to fight back malware and its variants. In the last decade, one of the strategies is extensively used in the field of malware detection and classification is the deep learning methods using malware visualization. Results revealed that using visualization; malware can be identified, classified more promptly, efficiently, and accurately. Deep learning algorithms vary according to applications, architecture, and uses, so it is required to review and inspect the work based on deep learning to use malware visualization to know the recent approaches and innovations that have been established, to identify problems, current issues, challenges, and of course at the same time to motivate potential research directions. In this effort, an extensive survey of works that utilized deep learning methods using malware image representation, for malware classification is reviewed with a detailed discussion on key methods such as data sets description, malware image representation strategies, and deep learning architectures of parameters, contributions, and limitations. A comparison of the reviewed work is presented based on various key factors.

【 授权许可】

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