Natural language processing for Nepali text: a review

Shahi, Tej Bahadur; Sitaula, Chiranjibi

doi:10.1007/s10462-021-10093-1

Cited by 24 publications

(16 citation statements)

References 55 publications

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“…DL models have made significant advances in a variety of fields including, but not limited to, deep fakes [ 22 , 23 ], satellite image analysis [ 24 ], image classification [ 25 , 26 ], the optimization of artificial neural networks [ 27 , 28 ], the processing of natural language [ 29 , 30 ], fin-tech [ 31 ], intrusion detection [ 32 ], steganography [ 33 ], and biomedical image analysis [ 14 , 34 ]. CNNs have recently surfaced as one of the most commonly used techniques for plant disease identification [ 35 , 36 ].…”

Section: Related Workmentioning

confidence: 99%

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

et al. 2023

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Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.

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Section: Related Workmentioning

confidence: 99%

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

et al. 2023

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“…This segmentation divides the sentence, which is then trimmed by removing the extraneous white space. Python tokenization is done using the NLTK library method [28], [29].…”

Section: Tokenization Of the Sentencesmentioning

confidence: 99%

An improved extrinsic monolingual plagiarism detection approach of the Bengali text

Ahnaf

Hasan

Sworna

et al. 2023

IJECE

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Plagiarism is an act of literature fraud, which is presenting others’ work or ideas without giving credit to the original work. All published and unpublished written documents are under the cover of this definition. Plagiarism, which increased significantly over the last few years, is a concerning issue for students, academicians, and professionals. Due to this, there are several plagiarism detection tools or software available to detect plagiarism in different languages. Unfortunately, negligible work has been done and no plagiarism detection software available in the Bengali language where Bengali is one of the most spoken languages in the world. In this paper, we have proposed a plagiarism detection tool for the Bengali language that mainly focuses on the educational and newspaper domain. We have collected 82 textbooks from the National Curriculum of Textbooks (NCTB), Bangladesh, scrapped all articles from 12 reputed newspapers and compiled our corpus with more than 10 million sentences. The proposed method on Bengali text corpus shows an accuracy rate of 97.31%

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“…As for neural networks, which are modeled on the structure of the human brain, they are a famous approach to machine learning and one of the most popular artificial intelligence techniques. Natural language processing (NLP) is one of the main aspects of artificial intelligence techniques [47] [48]. It involves teaching computers to comprehend and interpret human written and spoken language and to translate any language into a language humans understand.…”

Section: Artifical Intelligence Techniquesmentioning

confidence: 99%

The Evolving Role of Artificial Intelligence in the Future of Distance Learning: Exploring the Next Frontier

Mijwil

Ali

Sadıkoğlu

2023

Mesopotamian Journal of Computer Science

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In recent years, education has become especially related to the applications provided by artificial intelligence technology through a digital environment that includes a set of tools that assist in processing and storing information. Artificial intelligence techniques contribute to the development of students' skills by providing them with advanced scientific content and building their mental capabilities faster. Moreover, these techniques support analysing student data and suggest suitable educational materials and activities for them. Artificial intelligence is a noteworthy tool for the growth of distance education, especially after the development of expert systems that have become a human advisor in many domains, as this leads to the development of education systems that adjust the level of difficulty of materials based on the student’s performance in the electronic classroom, which ensures that the student continues in education and is not frustrated. This article will review the influential role of artificial intelligence applications in growing distance learning, improving the quality of education, and making it an adaptable and practical environment for students.

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Natural language processing for Nepali text: a review

Cited by 24 publications

References 55 publications

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

An improved extrinsic monolingual plagiarism detection approach of the Bengali text

The Evolving Role of Artificial Intelligence in the Future of Distance Learning: Exploring the Next Frontier

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