Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique

Shobana, M.; Balasraswathi, V. R.; Radhika, R.; Oleiwi, Ahmed Kareem; Chaudhury, Sushovan; Ladkat, Ajay S.; Naved, Mohd; Rahmani, Abdul Wahab

doi:10.1155/2022/9900668

Cited by 20 publications

(2 citation statements)

References 28 publications

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“…Pande et al [17][18][19] worked on the spline curve etc. The mathematical model for the classification using deep neural network approach is very important to get higher accuracy [20][21][22].…”

Section: Figure 2 Generic Model For Language Processingmentioning

confidence: 99%

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

Shelke¹,

Vanjale²

2022

ISI

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NER assumes a key part in Information Extraction from reports (for example email), conversational information, and so forth. Many tongue handling applications, for example, data recovery, question responding to, and machine interpretation, depend on NER. It tends to be challenging to determine the ambiguities of lexical components utilized in a text arrangement. There is too much work has been already done in English language but there is a need to improve accuracy for the NER in Hindi language. In this research researcher are minimize chances of misclassification by using different classifier namely location, name, weather etc. BiLSTM Development of a NER framework for Indian languages is a similarly troublesome task. In this paper, Researcher have done the different research to contrast the aftereffects of NER and typical implanting and quick text implanting layers to examinations the exhibition of word installing with various bunch sizes to prepare the profound learning models. In this paper, Researcher have done the different examinations to contrast the consequences of NER and typical implanting and quick text installing layers to investigations the presentation of word inserting with various group sizes to prepare the profound learning models. The value of the precision of proposed system architecture is 76.13% which is way more than other system architectures. Also, the value of recall and F1-score of proposed system architecture is 71.49 and 74.26 respectively. So, by comparing proposed system architecture with existing SpaCy, CoreNLP and NLTK it is easy to conclude that proposed system architecture is reliable in all the sense.

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Section: Figure 2 Generic Model For Language Processingmentioning

confidence: 99%

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

Shelke¹,

Vanjale²

2022

ISI

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show abstract

“…Ladkat et al [23] propose systems time complexity. To achieve greater accuracy when utilising deep neural networks for classification, a mathematical model is crucial [24][25][26].…”

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confidence: 99%

Deep Named Entity Recognition in Hindi Using Neural Networks

Shelke¹,

Vanjale²

2022

RIA

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In Natural Language Processing, named entity recognition (NER) is a task of (NLP) that tries to automatically identify and annotate Named Entities in text, such as people, places, and organisations. We employ a deep learning-based architecture in this work to solve the problem of recognising named entities in a Hindi text phrase. In the literature, approaches based on bidirectional long short-term memory (BiLSTM) have been utilized for the NER task. We performed recursive BiLSTM in this study, which includes a de-noising autoencoder with conditioning logic. Experiments were undertaken to examine the behaviour of individual word embeddings and batch sizes, which is vital for training deep models. The findings of the suggested system architecture, as well as a comparison of performance characteristics with existing systems, are presented in this study.

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AlexDarkNet: Hybrid CNN architecture for real-time Traffic monitoring with unprecedented reliability

Joshi,

Rao

2024

Neural Comput & Applic

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Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique

Cited by 20 publications

References 28 publications

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

Deep Named Entity Recognition in Hindi Using Neural Networks

AlexDarkNet: Hybrid CNN architecture for real-time Traffic monitoring with unprecedented reliability

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