Using Term Frequency - Inverse Document Frequency to find the Relevance of Words in Gujarati Language

Dodiya, Tripti

doi:10.22214/ijraset.2021.33625

Cited by 3 publications

References 11 publications

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A Deep Learning Approach for Transgender and Gender Diverse Patient Identification in Electronic Health Records

Hua,

Wang,

Nguyen

et al. 2023

Preprint

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Background: Although accurate identification of gender identity in the electronic health record (EHR) is crucial for providing equitable health care, particularly for transgender and gender diverse (TGD) populations, it remains a challenging task due to incomplete gender information in structured EHR fields. Objective: To develop a deep learning classifier to accurately identify patient gender identity using patient-level EHR data, including free-text notes. Methods: This study included adult patients in a large healthcare system in Boston, MA, between 4/1/2017 to 4/1/2022. To identify relevant information from massive clinical notes and to denoise, we compiled a list of gender-related keywords through expert curation, literature review, and expansion via a fine-tuned BioWordVec model. This keyword list was used to pre-screen potential TGD individuals and create two datasets for model training, testing, and validation. Dataset I was a balanced dataset that contained clinician-confirmed TGD patients and cases without keywords. Dataset II contained cases with keywords. The performance of the deep learning model was compared to traditional machine learning and rule-based algorithms. Results: The final keyword list consists of 109 keywords, of which 58 (53.2%) were expanded by the BioWordVec model. Dataset I contained 3,150 patients (50% TGD) while Dataset II contained 200 patients (90% TGD). On Dataset I the deep learning model achieved a F1 score of 0.917, sensitivity of 0.854, and a precision of 0.980; and on Dataset II a F1 score of 0.969, sensitivity of 0.967, and precision of 0.972. The deep learning model significantly outperformed rule-based algorithms. Conclusion: This is the first study to show that deep learning algorithms can accurately identify gender identity using EHR data. Future work should leverage and evaluate additional diverse data sources to generate more generalizable algorithms.

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A Deep Learning Approach for Transgender and Gender Diverse Patient Identification in Electronic Health Records

Hua,

Wang,

Nguyen

et al. 2023

Preprint

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Detecting Cybercrime: An Evaluation of Machine Learning and Deep Learning Using Natural Language Processing Techniques on the Social Network

Amer

Siddiqui

Athamena

2022

Preprint

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The widespread use of online social networks has culminated in across-the-board social communication among users, resulting in a considerable amount of user-generated contact data. Cybercrime has become a significant issue in recent years with the rise of online communication and social network. Cybercrime has lately been identified as a severe national psychological concern among platform users, and building a reliable detection model is crucial. Cyberbullying is the phrase used to describe such online harassment, insults, and attacks. It has become challenging to identify such unauthorized content due to the massive number of user-generated content. Because deep neural networks have various advantages over conventional machine learning approaches, researchers are turning to them more frequently to identify cyberbullying. Deep learning and machine learning have several uses in text classification. This article suggested the novel neural network model through parameters of an algorithmic and optimization comparative analysis of nine category approaches, four neural networks, and five machine learning, in two scenarios with real-world datasets of cyberbullying. Moreover, this work also analyzes the impact of word embedding and feature extraction techniques based on text mining and NLP on algorithms' performances. We performed extensive experiments on the two scenarios with a split dataset to demonstrate the merit of this research, comparing nine classification approaches through five feature extraction techniques. Our proposed cybercriminal detection model using neural networks, deep learning, and machine learning outperforms the existing state-of-the-art method of cybercriminal detection in terms of accuracy achieving higher performance.

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Natural Language Processing Algorithms and Performance Comparison

Arısoy

2024

Yalvaç Akademi Dergisi

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Natural language processing (NLP) is the general name for the methods and algorithms developed for computers to understand, interpret and produce human language. NLP plays a critical role in many fields, from social media analyses to customer service, from language translation to healthcare. This paper provides a comprehensive overview of the basic concepts of NLP, popular algorithms and models, performance comparisons, and various application areas. Key concepts of NLP include language models, tokenisation, lemmatisation, stemming, POS tagging, NER and syntactic parsing. These concepts are critical for processing, analysing and making sense of texts. Language models include popular methods such as N-gram, Word2Vec, GloVe and BERT. NLP algorithms are classified as rule-based methods, machine learning methods and deep learning methods. Rule-based methods are based on grammatical rules, while machine learning methods work on the principle of learning from data. Deep learning methods, on the other hand, achieve high accuracy results by using large datasets and powerful computational resources. In the performance comparison section, it is stated that the algorithms are evaluated with metrics such as accuracy, precision, recall and F1 score. Advanced models such as BERT and GPT-3 show superior performance in many NLP tasks. In conclusion, the field of NLP is rapidly evolving, with significant advancements anticipated in several key areas. These include the creation of more effective and efficient models, efforts to reduce biases, enhanced privacy protection, the growth of multilingual and cross-cultural models, and the development of explainable artificial intelligence techniques. This paper provides a comprehensive overview to understand the current status and future directions of NLP technologies.

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Using Term Frequency - Inverse Document Frequency to find the Relevance of Words in Gujarati Language

Cited by 3 publications

References 11 publications

A Deep Learning Approach for Transgender and Gender Diverse Patient Identification in Electronic Health Records

A Deep Learning Approach for Transgender and Gender Diverse Patient Identification in Electronic Health Records

Detecting Cybercrime: An Evaluation of Machine Learning and Deep Learning Using Natural Language Processing Techniques on the Social Network

Natural Language Processing Algorithms and Performance Comparison

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