Hybrid approach to crime prediction using deep learning

Azeez, Jazeem; Aravindhar, D. John

doi:10.1109/icacci.2015.7275858

Cited by 34 publications

(14 citation statements)

References 4 publications

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“…A combination of neural network and genetic algorithm has been shown effectiveness from various works. Another work uses the genetic algorithm in conjunction with SVM [43]. The work seeks to predict the event of bankruptcy using those algorithms.…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

Systematic review on event prediction models

Jamaludin

Ramasamy

2020

IJEECS

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Predicting an event can comes as a huge advantage for a lot of people, especially if it involves profits that a company can make. In this paper, matters that were discussed were the algorithms used for the event prediction systems, the type of algorithms and the comparison between different types of algorithm. Two main categories that were compared was the single algorithm for the prediction model and hybrid algorithms which is a combination of two or more algorithms in the model. Hybrid algorithm shows signs of better efficiency when data used were complex and large but a smaller dataset is more suitable with a single algorithm as the base as it is easier to apply with satisfactory results.

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Section: Hybrid Algorithmsmentioning

confidence: 99%

Systematic review on event prediction models

Jamaludin

Ramasamy

2020

IJEECS

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“…As stated by [14], on the basis of data source, predicting crimes can be categorized in to two strategies. One approach is to use usual statistic techniques and models such as STL [15], ARIMA, kernel density distribution, and more in identifying hotspots of crime.…”

Section: Related Literaturementioning

confidence: 99%

Applying Data Mining Techniques in Predicting Index and non-Index Crimes

Delima¹

2019

IJMLC

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An increasing incidence of crime has led to the development and use of computer-aided diagnosis system, tools and methods in analyzing, classifying and predicting crimes. This paper clusters municipalities in Surigao del Norte using K-Means algorithm. This is instrumental in finding identical traits, patterns and values in categorizing municipalities with much, more, and most number of recorded index and non-index crimes from 2013-2017. Prediction of its occurrence for the year 2018-2022 was also provided using ARIMA (1,0,7) model. Results showed that Surigao City has the most number of recorded index and non-index crimes. Followed by the municipality of Placer, Claver, and Dapa of cluster 2 and Del Carmen of cluster 3. Further, physical injury, homicide, violation of special laws, car napping, reckless imprudence resulting to physical injury, and other non-index crimes has 26%, 25%, 25%, 24%, 24%, and 23% forecasted increase for the year 2018-2022 with the highest occurrence in years 2018, 2018, 2020, 2018, 2020, and 2020 respectively. Future researchers may utilize other data mining techniques supported by a better accuracy result.

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“…As stated by [14], predicting crimes can be categorized into two strategies. One approach is to use usual statistic techniques and models such as STL [15], ARIMA, kernel density distribution, and more in identifying hotspots of crime.…”

Section: Literature Reviewmentioning

confidence: 99%

Crime Prediction using Autoregressive Integrated Moving Average (ARIMA) Algorithm

Patulin¹

2019

IJATCSE

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Data mining (DM), as coined with the term Knowledge Discovery in databases, used diverse approaches of DM analysis such as clustering, prediction or forecasting and many more. These are instrumental in extracting important information from the database. Along with DM analytics, big data and predictive analysis are relatively new concepts in criminology. This paper analyzes the index and non-index crimes in the province of Surigao del Norte, Philippines using the crime dataset from years 2013-2017. The prediction of its occurrence for the year 2018-2022 was also provided using ARIMA(1,0,7) model. Results showed that physical injury, homicide, violation of special laws, car napping, reckless imprudence resulting to physical injury, and other non-index crimes has 26%,

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Hybrid approach to crime prediction using deep learning

Cited by 34 publications

References 4 publications

Systematic review on event prediction models

Systematic review on event prediction models

Applying Data Mining Techniques in Predicting Index and non-Index Crimes

Crime Prediction using Autoregressive Integrated Moving Average (ARIMA) Algorithm

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