Author Identification via a Distributed Neural-Evolutionary Hybrid (DiNEH)

Aykent, Sarp; Dozier, Gerry

doi:10.1109/southeastcon44009.2020.9249707

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…The accuracy obtained on the said dataset is provided in Table 13 where it can be observed that our system outperformed the existing systems. [45] 68.76 Our method 93.52…”

Section: Performance Of Popular Classifiersmentioning

confidence: 99%

Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents

et al. 2022

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Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets.

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“…The accuracy obtained on the said dataset is provided in Table 13 where it can be observed that our system outperformed the existing systems. [45] 68.76 Our method 93.52…”

Section: Performance Of Popular Classifiersmentioning

confidence: 99%