Crime Against Women Analysis &amp; Prediction in India Using Supervised Regression

Patel, Bansi; Zala, Manmitsinh C.

doi:10.1109/iceeict53079.2022.9768533

Cited by 9 publications

(1 citation statement)

References 11 publications

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“…In [19] authors explored how the use of machine learning methods in the investigative process may be used to study the pattern of crimes and criminal characteristics in India, such as rape, sexual assault, kidnapping, etc. The datasets collected from each state in the nation have been studied and worked on using the potent Python package pandas.…”

Section: Literature Surveymentioning

confidence: 99%

The Role of Social Media In Promoting The Safety Of Women in Indian Cities

Reddy,

Nikhitha,

Manideep

et al. 2023

TURCOMAT

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In every city, harassment and violence becomes one of the major problems for women. Further, women’s personal life is suffered by the bullying and abusive content presented in online social networking (OSN). Therefore, it is necessary to identify the women safety in OSN environment. However, the conventional methods failed to predict the maximum safety analysis. So, this work is focused on women safety prediction using decision tree (WSP-DT) classifier. Initially, twitter dataset is considered to implement the entire system, which is then pre-processed to remove the missing and unknown symbols. Then, natural language toolbox kit (NLTK) applied to perform the tokenization, conversion to lowercase, stop words identification, stemming and lemmatization of tweets. Then, text blob protocol is developed to identify sentiments of pre-processed tweets, which identifies the positive, negative and neutral polarities of tweets. Further, term frequency-inverse document frequency (TFIDF) is applied to extract the data features based on word and character frequency. Finally, decision tree classifier applied to identify the fake or genuine tweet based on multi-level training. The simulations conducted on twitter dataset show that the proposed WSP-DT classifier resulted in superior performance than the other methods.

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Section: Literature Surveymentioning

confidence: 99%