Crime Against Women: Analysis and Prediction Using Data Mining Techniques

Kaur, Bhajneet; Ahuja, Laxmi; Kumar, Vinay

doi:10.1109/comitcon.2019.8862195

Cited by 12 publications

(10 citation statements)

References 5 publications

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“…These techniques play important roles in offense analysis and forecasting. Kaur et al [6] reviews the data mining techniques used in offense forecasting and analysis. It was concluded from this discussion that most researchers used classification and clustering techniques for offense manner and disclosure.…”

Section: Methods In Literaturementioning

confidence: 99%

“…In crime, preventing offense and force against the human female is one of the important goals. Different data mining techniques were used to analyze the causes of offense [6]. In the healthcare sector, various data mining tools have been applied to a range of diseases for detecting the infection in these diseases such as breast cancer diagnosis, skin diseases, and blood diseases [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Ba’abbad¹,

Althubiti²,

Alharbi³

et al. 2021

JDAIP

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Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students' records to predict degree completion time.

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Section: Methods In Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Ba’abbad¹,

Althubiti²,

Alharbi³

et al. 2021

JDAIP

View full text Add to dashboard Cite

show abstract

“…There is also a close and intimate relationship between gender and sexuality. In the same way that societies associate abilities and behaviours with specific bodies, so they similarly map sexual practices and expression onto specific bodies (Kessi & Boonzaier, 2018;Kaur & Ahuja. 2019).…”

Section: Conceptualising Gbvfmentioning

confidence: 99%

“Measure What Matters”: The Challenges Brought by Non-Categorisation of Gender-Based Violence and Femicide as a Crime at Higher Education Institutions, South Africa

Mofokeng

Tlou

2022

IJSSRR

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Crime in South Africa is categorised in a way that no two crimes can carry the same meaning. Hence, the crime of theft does not relate to the crime of house robbery. Gender-based violence and femicide (GBVF), is a very serious crime that is not categorised as a crime on its own, but is a crime which is identified through other crimes. This paper investigates the impact of the lack of categorisation of GBVF as a crime. The reviewed literature outlined a sense of confusion by the higher education institutions (HEIs) when addressing this crime. There is a sense of misunderstanding on which crimes constitute to be identified as GBVF, since GBVF is not categorised as a crime in the South African Police Service (SAPS) statistics. The reviewed literature outlined that GBVF has developed, and evolved overtime and it now has many terms that associate with it, these terms can range from domestic violence, intimate partner violence (IPV), violence against women (VAW), and attacks on the lesbians gays bisexuals transgender queer questioning and intersex community (LGBTQI). From the findings and recommendations, HEIs can be able to better respond to cases of GBVF on campus.

show abstract

“…These approaches play essential roles in offense analysis and forecasting. Kaur et al [6] analyzes the data mining strategies utilized in offense predicting and analysis. It was concluded from this debate that most researchers used classification and clustering strategies for offense manner and disclosure.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In crime, preventing offense and force against the human female is one of the essential goals. Different data mining techniques were employed to examine the reasons of offense [6]. In the healthcare industry, numerous data mining algorithms have been applied to a range of diseases for detecting the infection in these diseases such as breast cancer detection, skin disorders, and blood diseases [7].…”

Section: Introductionmentioning

confidence: 99%

A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications

Jen

2021

J. Appl. Data Sci.

View full text Add to dashboard Cite

Many business applications rely on their history data to anticipate their company future. The marketing products process is one of the essential procedures for the firm. Customer needs supply a useful piece of information that helps to promote the suitable products at the proper moment. Moreover, services are recognized recently as products. The development of education and health services is reliant on historical data. For the more, lowering online social media networks problems and crimes need a big supply of information. Data analysts need to utilize an efficient categorization system to predict the future of such businesses. However, dealing with a vast quantity of data demands tremendous time to process. Data mining encompasses numerous valuable techniques that are used to anticipate statistical data in a number of business applications. The classification technique is one of the most extensively utilized with a range of algorithms. In this work, numerous categorization methods are revised in terms of accuracy in diverse domains of data mining applications. A complete analysis is done following delegated reading of 20 papers in the literature. This study intends to allow data analysts to identify the best suitable classification algorithm for numerous commercial applications including business in general, online social media networks, agriculture, health, and education. Results reveal FFBPN is the best accurate algorithm in the business arena. The Random Forest algorithm is the most accurate in categorizing online social networks (OSN) activity. Naïve Bayes method is the most accurate to classify agriculture datasets. OneR is the most accurate method to classify occurrences inside the health domain. The C4.5 Decision Tree method is the most accurate to classify students' records to forecast degree completion time.

show abstract

Crime Against Women: Analysis and Prediction Using Data Mining Techniques

Cited by 12 publications

References 5 publications

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

“Measure What Matters”: The Challenges Brought by Non-Categorisation of Gender-Based Violence and Femicide as a Crime at Higher Education Institutions, South Africa

A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications

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