Empirical Analysis for Crime Prediction and Forecasting using Machine Learning and Deep Learning Techniques Article Swipe
Suma T
,
C Megha
,
Mittal Savan Kumar
,
Mahesh Jadhav
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48175/ijarsct-5900
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48175/ijarsct-5900
Crime Forecasting refers to the basic process of predicting crimes before they occur. Crimes are a common social problem affecting the quality of life and the economic growth of a society. A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. For our daily purposes we have to go many places every day and many times in our daily lives we face numerous security issues such as hijacking, kidnapping, harassment, etc. Daily there are huge numbers of crimes occurring frequently. These require keeping track of all the crimes and maintaining a database for same which may be used for future reference. The current problem faced are maintaining of proper dataset of crime and analyzing this data to help in predicting and solving crimes in future. The main objective of this project is to analyze dataset which consist of numerous crimes and predicting the type of crime which might occur in future depending upon various conditions. We will be using the technique of machine learning and data science for crime prediction of Chicago and Los Angeles crime data set. The K-Nearest Neighbor (KNN) classification and various other algorithms will be tested for crime prediction and one with better accuracy will be used for training. The main purpose of this project is to give a brief idea of how machine learning can be used by the law enforcement agencies to detect, predict and solve crimes at a much faster rate and thus reduce the crime rate. It is not restricted to Chicago and Los Angeles, this can be used in other states or countries depending upon the availability of the dataset.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.48175/ijarsct-5900
- OA Status
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- Cited By
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All OpenAlex metadata
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https://openalex.org/W4290059346Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48175/ijarsct-5900Digital Object Identifier
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Empirical Analysis for Crime Prediction and Forecasting using Machine Learning and Deep Learning TechniquesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
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2022-08-06Full publication date if available
- Authors
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Suma T, C Megha, Mittal Savan Kumar, Mahesh JadhavList of authors in order
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https://doi.org/10.48175/ijarsct-5900Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.48175/ijarsct-5900Direct OA link when available
- Concepts
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Machine learning, Computer science, Artificial intelligence, Law enforcement, Harm, Harassment, Process (computing), Punishment (psychology), Criminology, Political science, Psychology, Law, Social psychology, Operating systemTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2024: 5, 2022: 1Per-year citation counts (last 5 years)
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| abstract_inverted_index.many | 77, 82 |
| abstract_inverted_index.much | 263 |
| abstract_inverted_index.rate | 265 |
| abstract_inverted_index.same | 121 |
| abstract_inverted_index.set. | 205 |
| abstract_inverted_index.such | 93 |
| abstract_inverted_index.that | 37 |
| abstract_inverted_index.they | 11 |
| abstract_inverted_index.this | 142, 157, 235, 281 |
| abstract_inverted_index.thus | 267 |
| abstract_inverted_index.type | 171 |
| abstract_inverted_index.upon | 180, 291 |
| abstract_inverted_index.used | 125, 228, 249, 284 |
| abstract_inverted_index.well | 45 |
| abstract_inverted_index.will | 184, 215, 226 |
| abstract_inverted_index.with | 223 |
| abstract_inverted_index.(KNN) | 209 |
| abstract_inverted_index.Crime | 0 |
| abstract_inverted_index.Daily | 99 |
| abstract_inverted_index.These | 108 |
| abstract_inverted_index.basic | 5 |
| abstract_inverted_index.brief | 241 |
| abstract_inverted_index.cause | 39 |
| abstract_inverted_index.crime | 32, 139, 173, 196, 203, 219, 270 |
| abstract_inverted_index.daily | 71, 86 |
| abstract_inverted_index.every | 79 |
| abstract_inverted_index.faced | 132 |
| abstract_inverted_index.harm, | 43 |
| abstract_inverted_index.lives | 87 |
| abstract_inverted_index.loss, | 50 |
| abstract_inverted_index.might | 175 |
| abstract_inverted_index.occur | 176 |
| abstract_inverted_index.other | 60, 213, 286 |
| abstract_inverted_index.rate. | 271 |
| abstract_inverted_index.solve | 259 |
| abstract_inverted_index.state | 58 |
| abstract_inverted_index.there | 100 |
| abstract_inverted_index.times | 83 |
| abstract_inverted_index.track | 111 |
| abstract_inverted_index.using | 186 |
| abstract_inverted_index.which | 122, 163, 174 |
| abstract_inverted_index.Crimes | 13 |
| abstract_inverted_index.before | 10 |
| abstract_inverted_index.better | 224 |
| abstract_inverted_index.common | 16 |
| abstract_inverted_index.crime. | 68 |
| abstract_inverted_index.crimes | 9, 105, 115, 150, 167, 260 |
| abstract_inverted_index.damage | 48 |
| abstract_inverted_index.faster | 264 |
| abstract_inverted_index.future | 127, 178 |
| abstract_inverted_index.growth | 27 |
| abstract_inverted_index.issues | 92 |
| abstract_inverted_index.occur. | 12 |
| abstract_inverted_index.places | 78 |
| abstract_inverted_index.proper | 136 |
| abstract_inverted_index.reduce | 268 |
| abstract_inverted_index.refers | 2 |
| abstract_inverted_index.social | 17 |
| abstract_inverted_index.states | 287 |
| abstract_inverted_index.tested | 217 |
| abstract_inverted_index.Angeles | 202 |
| abstract_inverted_index.Chicago | 199, 277 |
| abstract_inverted_index.analyze | 161 |
| abstract_inverted_index.consist | 164 |
| abstract_inverted_index.current | 130 |
| abstract_inverted_index.dataset | 137, 162 |
| abstract_inverted_index.detect, | 256 |
| abstract_inverted_index.future. | 152 |
| abstract_inverted_index.keeping | 110 |
| abstract_inverted_index.machine | 190, 245 |
| abstract_inverted_index.numbers | 103 |
| abstract_inverted_index.predict | 257 |
| abstract_inverted_index.problem | 18, 131 |
| abstract_inverted_index.process | 6 |
| abstract_inverted_index.project | 158, 236 |
| abstract_inverted_index.purpose | 233 |
| abstract_inverted_index.quality | 21 |
| abstract_inverted_index.require | 109 |
| abstract_inverted_index.science | 194 |
| abstract_inverted_index.solving | 149 |
| abstract_inverted_index.various | 181, 212 |
| abstract_inverted_index.Angeles, | 280 |
| abstract_inverted_index.Neighbor | 208 |
| abstract_inverted_index.accuracy | 225 |
| abstract_inverted_index.agencies | 254 |
| abstract_inverted_index.database | 119 |
| abstract_inverted_index.dataset. | 296 |
| abstract_inverted_index.economic | 26 |
| abstract_inverted_index.learning | 191, 246 |
| abstract_inverted_index.numerous | 90, 166 |
| abstract_inverted_index.physical | 40 |
| abstract_inverted_index.property | 47 |
| abstract_inverted_index.purposes | 72 |
| abstract_inverted_index.security | 91 |
| abstract_inverted_index.severity | 65 |
| abstract_inverted_index.society. | 30 |
| abstract_inverted_index.K-Nearest | 207 |
| abstract_inverted_index.according | 62 |
| abstract_inverted_index.affecting | 19 |
| abstract_inverted_index.analyzing | 141 |
| abstract_inverted_index.authority | 61 |
| abstract_inverted_index.countries | 289 |
| abstract_inverted_index.depending | 179, 290 |
| abstract_inverted_index.objective | 155 |
| abstract_inverted_index.occurring | 106 |
| abstract_inverted_index.technique | 188 |
| abstract_inverted_index.training. | 230 |
| abstract_inverted_index.algorithms | 214 |
| abstract_inverted_index.deliberate | 35 |
| abstract_inverted_index.hijacking, | 95 |
| abstract_inverted_index.predicting | 8, 147, 169 |
| abstract_inverted_index.prediction | 197, 220 |
| abstract_inverted_index.punishment | 55 |
| abstract_inverted_index.reference. | 128 |
| abstract_inverted_index.restricted | 275 |
| abstract_inverted_index.Forecasting | 1 |
| abstract_inverted_index.conditions. | 182 |
| abstract_inverted_index.enforcement | 253 |
| abstract_inverted_index.frequently. | 107 |
| abstract_inverted_index.harassment, | 97 |
| abstract_inverted_index.kidnapping, | 96 |
| abstract_inverted_index.maintaining | 117, 134 |
| abstract_inverted_index.availability | 293 |
| abstract_inverted_index.psychological | 42 |
| abstract_inverted_index.classification | 210 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.66084624 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |