P. Tamilarasi

ASA College

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Radhika Rani

Sri Kalabyraveshwara Swamy Ayurvedic Medical College, Hospital and Research Center

C. Kumuthini

G.S. Science, Arts And Commerce College

T. Jayasudha

KPR Institute of Engineering and Technology

K. Kavitha

Velammal Medical College Hospital and Research Institute

R. Uma Rani

T. JayaSudha

R. Uma Rani

ASA College

M. Kavitha

Vellore Institute of Technology University

R. Umarani

Nethradhama Superspeciality Eye Hospital

Yusliza Yusoff

University of Technology Malaysia

Cooperative Institutions

Annamalai University

ASA College

Bharathiar University

Karpagam Academy of Higher Education

Sri Kalabyraveshwara Swamy Ayurvedic Medical College, Hospital and Research Center

Shri Dharmasthala Manjunatheswara College of Ayurveda and Hospital

Mardin Artuklu University

KPR Institute of Engineering and Technology

Velammal Medical College Hospital and Research Institute

Raman Research Institute

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Analysis of Crime Against Women Data Using Machine Learning and Statistical Methods

Advances in web technologies and engineering book series (2025)

P. Tamilarasi R. Umarani T. Jayasudha Yusliza Yusoff C. Kumuthini

Analyzing, cleaning, manipulating, and modelling datasets to conclude the data they include is the process known as data analytics. Qualitative and quantitative data analysis are two more categories into which data analytics may be separated. The scientific and mathematical study of algorithms is known as machine learning (ML). Crime against women has grown to be a major issue. Numerous nations are always working to avoid this offence, which is something that must be done. Crimes against women have increased dramatically in recent years. Every year, a massive dataset is created based on the reporting of crimes. To a certain extent, this data can help us prevent crime by analyzing and forecasting it. The crime rate against women is predicted in this chapter using a variety of statistical techniques, including the correlation coefficient ANOVA, and hypothesis testing with ML models in Python, including linear regression. The results are finally presented based on the performance of the ML algorithms using evolution metrics, such as MAE, MSE, and R2.

10.4018/979-8-3693-7868-7.ch004

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Diagnosis of Crime Rate against Women using k-fold Cross Validation through Machine Learning

P. Tamilarasi R. Uma Rani

Crime against women has become a very big problem of our nation. Many countries are trying to control this offence continuously and its prevention is an essential task. In recent years crimes are significantly increasing against women. Currently the Indian government show interest to address this problem and give more importance to develop our society. Every year a huge amount of data collection is generated on the basis of the crime reporting. This data can be very useful for assessing and predicting crime, and can help us to some degree stop the crime. Data analysis is a process of examining, cleansing, transformation and modelling data with the goal of establish useful information, reporting conclusion and sustaining decision-making. Feature Scaling is one of the most important techniques to standardize the independent features to place the data in a fixed range. It is performed at the time of data pre-processing. K-fold cross-validation is a re-sampling method used for calculating machine learning models on a small sample of data. It is a common strategy since it is easy to understand and usually results in a model deftness calculation that is less biased or less negative than other approaches, such as a simple train or test divide. Machine learning plays a large part in data processing. This paper introduces six different types of Machine learning algorithms such as KNN and decision trees, Naïve Bayes, Linear Regression CART (Classification and Regression Tree) and SVM using similar characteristics on crime data. Those algorithms are tested for accuracy. The main objective of this research is to evaluate the efficacy and application of the machine learning algorithms in data analytics.

Cross-validation

10.1109/iccmc48092.2020.iccmc-000193

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Data Science In Healthcare

Advances in web technologies and engineering book series (2025)

T. JayaSudha R. Uma Rani P. Tamilarasi

Cardiovascular disease (CVD) has been the leading reason of death up to this point. Cardiovascular illness must be diagnosed as soon as possible to save patients' lives. The intention of this research is identifying the most important features to predict CVD by using feature selection method, and find out the most effective classification model to forecast cardiovascular diseases. This study makes use of patient datasets from the UCI repository for prediction. For data scaling, Min-Max Scaler is employed. Data transformation is accomplished using the label encoding technique. The Z-score method is applied to handle the outlier. The most essential features were picked using the chi square approach. For classification, logistic regression, support vector machine, naive bayes, decision tree, and boosted random forest were employed. The achievement was assessed by applying confusion matrix, accuracy, recall, F1 score, and precision, and ROC. Among five classifiers, boosted random forest classifier achieved the highest accuracy (87.3%).

10.4018/979-8-3693-7868-7.ch007

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Predict the Crime Rate Against Women Using Machine Learning Classification Techniques

Chapman and Hall/CRC eBooks (2021)

P. Tamilarasi Radhika Rani

Women are the much-respected person in ancient India, but today Indian women are facing a lot of problems while violence against women is also increasing day by day. From 2011 to 2014, the highest level of crimes against females were recorded in the police department. This is a big problem for our nation. Finally, the government of India has shown an interest in managing this difficulty. Data science is a scientific method extracted from artificial intelligence. This is the combination of machine learning, different types of tools, algorithms, and statistical methods. The combination of data science, as well as machine learning and statastical methods, will process crime data efficiently with good results. In this chapter, we estimate the performance of various machine learning (ML) classification algorithms such as logistic regression, K-nearest neighbor, support vector classifiers, and Gaussian naïve Bayes algorithms implemented on the Crime against Women dataset. Finally, we conclude the results based on the most reliable outcome from converting the imbalanced data to balanced data by using Python.

10.1201/9781003102380-15

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Fault Detection System Using Machine Learning in IoT Based Automobile Industries

C. Kumuthini K. Kavitha N. Thangarasu P. Tamilarasi M. Kavitha

10.1109/iccda64887.2024.10867303

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Crime Prediction and Analysis against women Using LRSRI-Missing Value Imputation and FIPSO - Optimum Feature Selection Methods

International Journal on Recent and Innovation Trends in Computing and Communication (2023)

P. Tamilarasi Radhika Rani

Data investigation is the method of considering crude measurements in arrange to draw conclusions around them. Many statistics evaluation techniques and tendencies had been automated into mechanical techniques and algorithms in such a manner that they provided raw statistics for human consumption. Machine learning could be a portion of artificial intelligence that permits computer frameworks to "analyze" their own statistics and improve them over time without being explicitly programmed. Machine learning algorithms can understand patterns in statistics and analyze them to make their own predictions. Lost esteem ascription is one of the foremost vital procedures in data pre-processing and it is additionally the most prepare of information examination. Ascription of lost information for a variable replaces lost information with a esteem inferred from an assess of the dispersion of that variable. Basic accusation employments as it were one suspicion. Numerous ascriptions employments diverse gauges to reflect the instability in evaluating this dispersion. In this article, The proposed method LRSRI used for impute the missing values on Crime against Women Data-set(CAW).The Linear Regression Imputation and Stochastic regression imputations are used in this method.Feature selection is another important data preprocessing techniques.This is often called attribute selection or feature selection. The most important problem in predictive modeling is the mechanical selection of features in the data. In this work,the proposed method FIPSO implemented for feature selection.This is feature importance and Particle Swarm Optimization based method.The main objective of this work is predict the crime rate against women in India based on 2001 to 2021 crime recorded against women in India.This Data set is collected from Data.gov.in.Finally The predicted result is compared with recent NCRB crime report.The proposed method LRSRI and FIPSO has given 98.34% accuracy of crime prediction.In feature,This outcome will be valuable for the crime office to control the CAW in India.

Imputation (statistics)

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Feature (linguistics)

10.17762/ijritcc.v11i4s.6536

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Citations (1)