Machine Learning based Advanced Crime Prediction and Analysis DOI

Sameya Khatun,

Kavyasree Banoth,

Akshara Dilli

et al.

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Journal Year: 2023, Volume and Issue: unknown, P. 90 - 96

Published: March 23, 2023

One of the society's most important challenges is crime. It visible part our civilization. As a result, one crucial jobs crime prevention. Machine learning approach can better help in prediction and analysis The subject machine India has been addressed through number prediction-based theories. Finding dynamic character crimes becomes difficult challenge. goal to lower rates discourage criminal activity. In order discover proper predictions by using learning-based techniques, this study provides many algorithms, such as Naive Bayes, Support Vector Machine, Linear Regression, Decision Tree, Bagging Stacking Random Forest Regression algorithms. Comparing Byes algorithm other models SVM, bagging, tree, stacking, Forest, it used create configurations that are specific certain domain. On test data, suggested technique had classification accuracy 99.9%. discovered model stronger predictive impact than earlier one. When compared baseline studies just looked at data sets based on violence, found have greater power. outcomes demonstrated criminological theories compatible with any actual evidence method was be helpful for making potential predictions.

Language: Английский

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions DOI Creative Commons
Varun Mandalapu,

Lavanya Elluri,

Piyush Vyas

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 60153 - 60170

Published: Jan. 1, 2023

Predicting crime using machine learning and deep techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns trends occurrences. This review paper examines over 150 articles to explore the various algorithms applied predict crime. The study provides access datasets used for prediction by analyzes prominent approaches crime, offering insights into different factors related criminal activities. Additionally, highlights potential gaps future directions that can enhance accuracy of prediction. Finally, comprehensive overview research discussed this serves as a valuable reference field. By gaining deeper understanding techniques, law enforcement agencies develop strategies prevent respond activities more effectively.

Language: Английский

Citations

55
Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai DOI Creative Commons
Waishan Qiu, Wenjing Li, Xun Liu

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2021, Volume and Issue: 10(8), P. 493 - 493

Published: July 21, 2021

Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets objectively extract the indices of various streetscape features such as trees proxy urban scene qualities have emerged. However, human perception (e.g., imageability) a subtle relationship visual elements that cannot be fully captured using indices. Conversely, subjective measures survey and interview data explain behaviors more. effectiveness integrating with SVI has been less discussed. To address this, we integrated crowdsourcing, CV, machine learning (ML) subjectively measure four important perceptions suggested by classical design theory. We first collected ratings from experts on sample SVIs regarding these qualities, which became training labels. CV segmentation was applied samples extracting explanatory variables. then trained ML models achieved high accuracy in predicting scores. found strong correlation between predicted complexity score density amenities services points interest (POI), validates measures. In addition, test generalizability proposed framework well inform renewal strategies, compared measured Pudong other five cores are renowned worldwide. Rather than perceptual scores directly generic image convolution neural network, our approach follows what theory confirmed affecting multi-dimensional perceptions. Therefore, results provide more interpretable actionable implications for policymakers city planners.

Language: Английский

Citations

60
Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence DOI Creative Commons
Faris Kamil Hasan Mihna, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 2024, P. 4 - 16

Published: March 3, 2024

With the escalation of cybercriminal activities, demand for forensic investigations into these crimeshas grown significantly. However, concept systematic pre-preparation potential forensicexaminations during software design phase, known as readiness, has only recently gainedattention. Against backdrop surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing history, varioussocio-economic indicators, geographical locations attain comprehensive understanding howcrimes manifest within city. Leveraging sophisticated AI algorithms, focuses on scrutinizingsubtle periodic patterns uncovering relationships among collected datasets. Through thiscomprehensive analysis, endeavors pinpoint hotspots, detect fluctuations infrequency, identify underlying causes criminal activities. Furthermore, evaluates theefficacy model generating productive insights providing most accurate predictionsof future trends. These predictive are poised revolutionize strategies lawenforcement agencies, enabling them adopt proactive targeted approaches. Emphasizing ethicalconsiderations, ensures continued feasibility use while safeguarding individuals'constitutional rights, including privacy. The anticipated outcomes tofurnish actionable intelligence law enforcement, policymakers, planners, aiding theidentification effective prevention strategies. By harnessing AI, researchcontributes promotion data-driven models andprediction, offering promising avenue enhancing public security Angeles othermetropolitan areas.

Language: Английский

Citations

9
Prediction of crime rate in urban neighborhoods based on machine learning DOI
Jingyi He, Hao Zheng

Engineering Applications of Artificial Intelligence, Journal Year: 2021, Volume and Issue: 106, P. 104460 - 104460

Published: Sept. 24, 2021

Language: Английский

Citations

48
Cognizable crime rate prediction and analysis under Indian penal code using deep learning with novel optimization approach DOI
Rabia Musheer Aziz, Aftab Hussain,

Prajwal Sharma

et al.

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(8), P. 22663 - 22700

Published: Aug. 7, 2023

Language: Английский

Citations

13
Agricultural land use modeling and climate change adaptation: A reinforcement learning approach DOI Creative Commons
Christian Stetter, Robert Huber, Robert Finger

et al.

Applied Economic Perspectives and Policy, Journal Year: 2024, Volume and Issue: 46(4), P. 1379 - 1405

Published: May 27, 2024

Abstract This paper provides a novel approach to integrate farmers' behavior in spatially explicit agricultural land use modeling investigate climate change adaptation strategies. More specifically, we develop and apply computationally efficient machine learning based on reinforcement simulate the adoption of agroforestry practices. Using data from an economic experiment with crop farmers Southeast Germany, our results show that climate, market, policy conditions shifts spatial distribution uptake systems. Our can be used advance currently models for ex ante analysis by upscaling existing knowledge about behavioral characteristics combine it environmental farm structural data. The presents potential solution researchers who aim upscale information, potentially enriching complementing approaches.

Language: Английский

Citations

5
A Framework for LLM-Assisted Smart Policing System DOI Creative Commons
Paria Sarzaeim, Qusay H. Mahmoud, Akramul Azim

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 74915 - 74929

Published: Jan. 1, 2024

In the face of rapidly increasing crime rates, evolving complexity data processing, and public safety challenges, need for more advanced policing solutions has increased leading to emergence smart systems predictive techniques. This urgency shift toward incorporates artificial intelligence (AI), with a specific focus on machine learning (ML) as an essential tool analysis, pattern recognition, proactive forecasting. Among these, flexibility power AI techniques including large language models (LLMs), subset generative AI, have interest in applying them real-world applications, such financial, medical, legal, agricultural applications. However, abilities possibilities adopting LLMs applications prediction remain unexplored. paper focuses bridging this gap by developing framework based transformative potential BART, GPT-3, GPT-4, three state-of-the-art LLMs, domain policing, specifically, prediction. As prototype, diverse methods zero-shot prompting, few-shot fine-tuning are used comprehensively assess performance these datasets from two major cities: San Francisco Los Angeles. The main objective is illuminate adaptability their capacity revolutionize analysis practices. Additionally, comparative aforementioned GPT series model BART ML provided which shows that suitable than traditional classification most experimental scenarios.

Language: Английский

Citations

4
A survey of Emotional Artificial Intelligence and crimes: detection, prediction, challenges and future direction DOI

Tala Talaei Khoei,

Aditi Singh

Journal of Computational Social Science, Journal Year: 2024, Volume and Issue: 7(3), P. 2359 - 2402

Published: July 17, 2024

Language: Английский

Citations

4
Geovisualization and Prediction of Crime Against Women in West Bengal Using Statistical Modeling DOI
Priyanka Biswas, Nilanjana Das Chatterjee

SpringerBriefs in GIS, Journal Year: 2025, Volume and Issue: unknown, P. 33 - 64

Published: Jan. 1, 2025

Language: Английский

Citations

0
Exploratory data analysis, time series analysis, crime type prediction, and trend forecasting in crime data using machine learning, deep learning, and statistical methods DOI Creative Commons
Esen Gül İLGÜN, Murat Dener

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Language: Английский

Citations

0