Crime Prediction Using Multiple-ANFIS Architecture and Spatiotemporal Data

Islam, Mashnoon; Karim, Redwanul; Roy, Kalyan; Mahmood, Saif; Hossain, Sadat; Rahman, Rashedur M.

doi:10.1109/is.2018.8710564

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…On the flip side, one of the greatest risks to Dhaka city residents is burglary which can be defined as an unauthorized entry into a home with the intent to steal (Islam et al , 2018; Chon, 2017). Among 338 cities, Dhaka ranked 313th in terms of security, according to Papadopoulos (2019).…”

Section: Introductionmentioning

confidence: 99%

“…This is apparent from the Bangladesh police department's released statistics, which confirm that between 2016 and 2019, there were 547, 554, 613 and 49 reported burglary attempts (Crime Statistics, 2019). However, the victims of these burglaries may not always notify the authorities of the occurrences (Islam et al , 2018; Khan, 2015). Because of this, the data provided by the Bangladesh police department may not completely reflect the actual number of instances, which may be far larger than the published data (Chowdhury, 2014; Islam et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

“…However, the victims of these burglaries may not always notify the authorities of the occurrences (Islam et al , 2018; Khan, 2015). Because of this, the data provided by the Bangladesh police department may not completely reflect the actual number of instances, which may be far larger than the published data (Chowdhury, 2014; Islam et al , 2018). For this reason, IoT-supported security measures are highly required to be adopted to reduce burglary attempts and to improve the situation as well.…”

Section: Introductionmentioning

confidence: 99%

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Generation Z’s adoption of IoT: protection motivation theory as the underlying model and gender as a moderator

Mahmud

Yusoff

Husin

2023

JSIT

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Purpose The factors that motivate Generation Z individuals to use the Internet of Things for security purposes have yet to be explored. Therefore, the purpose of this paper is to close a research gap by verifying the protection motivation theory using gender as a moderator. Design/methodology/approach The authors used a purposive sampling approach to collect data from Dhaka city, in which 370 valid responses were selected. Additionally, the quantitative and cross-sectional survey used a seven-point Likert scale. Afterward, the evaluation approach included three phases: a measurement model, a structural model and multi-group analysis. Findings Vulnerability, self-efficacy and response-efficacy were discovered to be critical predictors with a variance of 60.4%. Moreover, there was a significant disparity between males and females in two relationships, response efficacy and intention as well as response cost and intention. Practical implications This research expands our understanding of Generation Z consumers' behavioral intentions to take measures against household threats, allowing preventative programs to be improved. Further, in the case of applying coping strategies, a practical difference between males and females has been found that must be bridged through awareness campaigns. Originality/value This study has made a unique contribution to the information system literature. First, the role of protection motivation theory factors in addressing security concerns in homes has been assessed. Second, the coping evaluation process has a greater impact on users' intentions than the threat appraisal process. However, males and females use slightly different approaches to defending themselves against the threat.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generation Z’s adoption of IoT: protection motivation theory as the underlying model and gender as a moderator

Mahmud

Yusoff

Husin

2023

JSIT

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Machine learning in crime prediction

Jenga

Çatal

Kar

2023

J Ambient Intell Human Comput

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Predicting crimes before they occur can save lives and losses of property. With the help of machine learning, many researchers have studied predicting crimes extensively. In this paper, we evaluate state-of-the-art crime prediction techniques that are available in the last decade, discuss possible challenges, and provide a discussion about the future work that could be conducted in the field of crime prediction. Although many works aim to predict crimes, the datasets they used and methods that are applied are numerous. Using a Systematic Literature Review (SLR) methodology, we aim to collect and synthesize the required knowledge regarding machine learning-based crime prediction and help both law enforcement authorities and scientists to mitigate and prevent future crime occurrences. We focus primarily on 68 selected machine learning papers that predict crime. We formulate eight research questions and observe that the majority of the papers used a supervised machine learning approach, assuming that there is prior labeled data, and however in some cases, there is no labeled data in real-world scenarios. We have also discussed the main challenges found while conducting some of the studies by the researchers. We consider that this research paves the way for further research to help governments and countries fight crime and decrease this for better safety and security.

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Crime Prediction Using Multiple-ANFIS Architecture and Spatiotemporal Data

Cited by 2 publications

References 7 publications

Generation Z’s adoption of IoT: protection motivation theory as the underlying model and gender as a moderator

Generation Z’s adoption of IoT: protection motivation theory as the underlying model and gender as a moderator

Machine learning in crime prediction

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