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Journal articles on the topic 'Crime Analytics'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Acharya, Prof Suman. "Crime Analytics using Machine Learning." International Journal of Inventive Engineering and Sciences 10, no. 3 (2023): 1–5. http://dx.doi.org/10.35940/ijies.f7444.0310323.

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Crime is one of the most significant and pervasive problems in our society, and preventing it is a crucial duty. A large number of crimes are perpetrated each day. Maintaining and analyzing crime data to forecast and solve crimes is the current issue. This project analyzes a large dataset of crimes and predicts future crimes based on conditions. This project uses data science and machine learning for India's crime data prediction. Thus, Decision Tree, Logistic Regression, Multi-Regression, k-NN, Lasso & Ridge, and Random Forest are all involved in the supervised classification problem. Pre

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Prof., Suman Acharya. "Crime Analytics using Machine Learning." International Journal of Inventive Engineering and Sciences (IJIES) 10, no. 3 (2023): 1–5. https://doi.org/10.35940/ijies.F7444.0310323.

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<strong>Abstract: </strong>Crime is one of the most significant and pervasive problems in our society, and preventing it is a crucial duty. A large number of crimes are perpetrated each day. Maintaining and analyzing crime data to forecast and solve crimes is the current issue. This project analyzes a large dataset of crimes and predicts future crimes based on conditions. This project uses data science and machine learning for India's crime data prediction. Thus, Decision Tree, Logistic Regression, Multi-Regression, k-NN, Lasso & Ridge, and Random Forest are all involved in the supervi

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K, Vijay, and Amal Prakash. "A Systematic Review on Tanpin Kandri Based Crime Prediction." Remittances Review 7, no. 2 (2022): 01–11. http://dx.doi.org/10.47059/rr.v7i2.2407.

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Predictive policing refers to the potential preventive measure to fight crimes in the modern age. Crimes are social issues that have a negative effect on society. The number of cases filed under the Indian Penal Code (IPC) in 2020 increased by over 430 percent compared to the same year-ago period. Data analytics is the process of inspecting, transforming, and modelling unstructured data into useful information. Predictive policing and crime analytics with Artificial intelligence increase attention among a multifarious scientific community. Crime pattern analysis is the computational approach t

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Dr.N.Kala and Premanand Narasimhan. "Data Analytics of Illegal Wildlife Trading in Cyberspace." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2424–37. https://doi.org/10.32628/cseit2410612442.

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India is one of the most bio-diverse Nation. Organised illegal wildlife trafficking and poaching poses threat as it creates an imbalance in the ecosystem as such. In case this situation continues some species will soon become extinct. The major legislative framework in India for addressing wildlife crime is the Wild Life (Protection) Act of 1972. The methods, strategies, and procedures employed by various Governments to look into wildlife crimes are inconsistent, which usually leads to problems with operations and the law. Diverse countries use different tactics, methodologies, and procedures

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Fitzpatrick, Dylan J., Wilpen L. Gorr, and Daniel B. Neill. "Keeping Score: Predictive Analytics in Policing." Annual Review of Criminology 2, no. 1 (2019): 473–91. http://dx.doi.org/10.1146/annurev-criminol-011518-024534.

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Predictive analytics in policing is a data-driven approach to ( a) characterizing crime patterns across time and space and ( b) leveraging this knowledge for the prevention of crime and disorder. This article outlines the current state of the field, providing a review of forecasting tools that have been successfully applied by police to the task of crime prediction. We then discuss options for structured design and evaluation of a predictive policing program so that the benefits of proactive intervention efforts are maximized given fixed resource constraints. We highlight examples of predictiv

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Prathap, Boppuru Rudra, and K. Ramesha. "Geospatial Crime Analysis to Determine Crime Density Using Kernel Density Estimation for the Indian Context." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 74–86. http://dx.doi.org/10.1166/jctn.2020.8632.

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Crime is the most common social problem faced in a developing country. Crime affects the reputation of a nation and the quality of life of its citizens. Crime also affects the economy of the country, increasing the financial burden of the government due to the need for expenditure in the police force and judicial system. Various initiatives are taken by law enforcement to reduce the crime rate. One such initiative, real-time accurate crime predictions can help reduce the occurrence of crime. In this paper, a crime analytics platform is developed, which processes newsfeed data analysis for diff

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Falope, Olayemi, and Surendra Thakur. "Sexual Crime Prediction in an African Context." International Conference on Intelligent and Innovative Computing Applications 2022 (December 31, 2022): 1–12. http://dx.doi.org/10.59200/iconic.2022.001.

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The growth of sexual crime in Africa and the incapacity to control it have had a major physical and psychological impact on victims. Crime in general can reduce foreign direct investment in a country. This study was driven by the need to reduce sexual crime across the country. Data mining techniques were applied to a sexual crime dataset extracted from the South African crime statistics database on Kaggle to visualise sexual crime trends and create a model that predicts the occurrence of sexual crimes, thereby helping government and law enforcement agencies gain insights into the most common s

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D G, Kavyashree, and Amruth M M. "Crime Trends Analysis." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–8. https://doi.org/10.55041/ijsrem.spejss010.

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Abstract—Crime has become a growing concern in urban and rural regions alike, posing significant challenges to law enforcement agencies and policymakers. The analysis and forecasting of crime rates through data-driven methods is the main topic of this paper. This study intends to uncover crime patterns, hotspot regions, and temporal trends by utilizing statistical approaches, machine learning techniques, and historical crime data. The system integrates real-time data (where available) and uses predictive models to forecast the likelihood of various types of crimes in specific areas. The primar

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D G, Kavyashree, and Ajay Kumar M. "Criminal Incidence Rate." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–7. https://doi.org/10.55041/ijsrem.spejss013.

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Abstract—Crime has become a growing concern in urban and rural regions alike, posing significant challenges to law enforcement agencies and policymakers. The analysis and forecasting of crime rates through data-driven methods is the main topic of this paper. This study intends to uncover crime patterns, hotspot regions, and temporal trends by utilizing statistical approaches, machine learning techniques, and historical crime data. The system integrates real-time data (where available) and uses predictive models to forecast the likelihood of various types of crimes in specific areas. The primar

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Soppari, Dr Kavitha. "City Crime Hotspot Identification and Visualization Development of a Web Application for Improving Urban Safety and Decision Making." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03400.

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Abstract: Urban crime hotspots present significant challenges to public safety and effective city planning. This study proposes a web-based crime hotspot identification and visualization system that utilizes geospatial mapping, machine learning, and data analytics. The application empowers law enforcement agencies and policymakers by classifying high-risk areas, tracking evolving crime trends, and predicting future crime occurrences. The system’s interactive dashboard delivers real-time, data-driven insights to support strategic decision-making and improve urban safety outcomes. Keywords: Crim

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Tyagi, Deepika, and Dr Sanjiv Sharma. "AN APPROACH TO CRIME DATA ANALYSIS: A SYSTEMATIC REVIEW." International Journal of Engineering Technologies and Management Research 5, no. 2 (2020): 67–74. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.615.

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In the current era, number of crimes occurs in the society and this criminal rate increase day by day. There is tremendous growth of criminal data. Crime has negatively influenced the societies. Crime control is essential for the welfare, stability and development of society. Law enforcement agencies are seeking for the system to target crime structure efficiently. The intelligent crime data analysis provides the best understanding of the dynamics of unlawful activities, discovering patterns of criminal behavior that will be useful to understand where, when and why crimes can occur. There is a

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Deepika, Tyagi, and Sanjiv Sharma Dr. "AN APPROACH TO CRIME DATA ANALYSIS: A SYSTEMATIC REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGIES AND MANAGEMENT RESEARCH 5, no. 2 :SE (2018): 67–74. https://doi.org/10.5281/zenodo.1197513.

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In the current era, number of crimes occur in the society and this criminal rate increase day by day. There is tremendous growth of criminal data. Crime has negatively influenced the societies. Crime control is essential for the welfare, stability and development of society. Law enforcement agencies are seeking for the system to target crime structure efficiently. The intelligent crime data analysis provides the best understanding of the dynamics of unlawful activities, discovering patterns of criminal behavior that will be useful to understand where, when and why crimes can occur. There is a

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Saltos, Ginger, and Mihaela Cocea. "An Exploration of Crime Prediction Using Data Mining on Open Data." International Journal of Information Technology & Decision Making 16, no. 05 (2017): 1155–81. http://dx.doi.org/10.1142/s0219622017500250.

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The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas — an administrative system of areas used by the UK police) and the f

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Lazarenko, A., and Yu Fedorova. "METHODS OF CRIMINOLOGICAL PROFILING AND THEIR ROLE IN PREVENTING SERIAL MURDERS." Scientific journal Criminal and Executive System: Yesterday. Today. Tomorrow 2025, no. 1 (2025): 74–88. https://doi.org/10.32755/sjcriminal.2025.01.074.

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The article provides a comprehensive analysis of criminological profiling methods for serial killers, their effectiveness, and role in preventing serial crimes. The main profiling methods are identified, including criminal-analytical, psychological, geographical, linguistic, and digital. A detailed analysis of psychological profiling used to determine the motivation and behavioral characteristics of serial killers is conducted. Geographical profiling allows for determining the offender’s zone of activity, which increases the effectiveness of search and apprehension of suspects. Linguistic prof

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Mohemad, Rosmayati, Nazratul Naziah Mohd Muhait, Noor Maizura Mohamad Noor, and Zulaiha Ali Othman. "Unstructured Malay Text Analytics Model in Crime." IOP Conference Series: Materials Science and Engineering 769 (June 9, 2020): 012015. http://dx.doi.org/10.1088/1757-899x/769/1/012015.

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Boppuru, Prathap Rudra, and Ramesha K. "Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context." International Journal of Digital Crime and Forensics 12, no. 4 (2020): 1–19. http://dx.doi.org/10.4018/ijdcf.2020100101.

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In developing countries like India, crime plays a detrimental role in economic growth and prosperity. With the increase in delinquencies, law enforcement needs to deploy limited resources optimally to protect citizens. Data mining and predictive analytics provide the best options for the same. This paper examines the news feed data collected from various sources regarding crime in India and Bangalore city. The crimes are then classified on the geographic density and the crime patterns such as time of day to identify and visualize the distribution of national and regional crime such as theft, m

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G. Pavan, S. Uday Kiran, S. Harshitha, S. Leela Sai Kumar, and VVDS Phanindra Naidu. "CRIME FORECASTING WITH MACHINE LEARNING AND DEEP LEARNING: A SYSTEMATIC ANALYSIS." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 194–200. https://doi.org/10.36893/jnao.2025.v16.i01.025.

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This study analyzes and predicts crime patterns using a dataset of crimes from Chicago starting from 2001. Through exploratory data analysis (EDA), we uncovered unique and non-unique case numbers and crime distributions. The data was preprocessed for machine learning by handling duplications, extracting date components, and encoding categorical data. Visualization techniques highlighted key trends and high-crime areas. We developed a predictive model using an artificial neural network (ANN), equipped with dense and dropout layers to prevent overfitting, and optimized using the Adam optimizer f

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Mir Affan Ali Osmani, Mohammed Rasheed, Mohammed Shujauddin Siddiqui, and Dr. Vaishnavi Lakadaram. "Behavioral Analysis and Crime Severity Prediction using Machine Learning." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 204–9. https://doi.org/10.62647/ijitce2025v13i2spp204-209.

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Crime analysis and prediction involve a systematic methodology for identifying patterns and anticipating potential criminal activity. This study presents a system capable of predicting regions with a high likelihood of crime occurrence and visualizing crime-prone areas. By leveraging data mining techniques, it becomes possible to uncover previously unknown yet valuable insights from unstructured datasets. These insights are derived by analyzing existing crime data to forecast future trends. Crime remains a serious and widespread social issue, adversely impacting the quality of life, economic s

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Brindha, R., and Dr M. Thillaikarasi. "Crime Data Forecasting Using Machine Learning and Big Data Analytics." Webology 18, Special Issue 04 (2021): 591–606. http://dx.doi.org/10.14704/web/v18si04/web18284.

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Big data analytics (BDA) is a system based method with an aim to recognize and examine different designs, patterns and trends under the big dataset. In this paper, BDA is used to visualize and trends the prediction where exploratory data analysis examines the crime data. “A successive facts and patterns have been taken in following cities of California, Washington and Florida by using statistical analysis and visualization”. The predictive result gives the performance using Keras Prophet Model, LSTM and neural network models followed by prophet model which are the existing methods used to find

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Stupnyk, Y. V., and O. O. Dolynko. "Analytical forecasting in the field of crime prevention: information tools and methods." Analytical and Comparative Jurisprudence 2, no. 3 (2025): 468–74. https://doi.org/10.24144/2788-6018.2025.03.2.75.

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The article examines the essence and components of information and analytical support for law enforcement agencies in the fight against crime. The methodological approaches and main tasks of information and analytical activities of operational units are revealed. It is determined that proper information support is an integral part of the effective functioning of any social system, including mechanisms for combating criminal manifestations. Special attention is paid to modern trends in the development of information technologies, which are actively integrated into the field of combating crime.

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YADAV, SUNNY. "Using Existing CCTV Network for Crowd Management, Crime Prevention, And Work Monitoring Using AIML." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47551.

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Abstract The fusion of Artificial Intelligence (AI) and Machine Learning (ML) with the existing Indian Railways CCTV network provides revolutionary potential to improve safety, security, and operational efficiency. This research showcases the creation of an AI-based railway surveillance system that is meant to solve issues related to controlling congested stations, deterring crimes, and ensuring efficient workforce monitoring. Through the use of sophisticated video analytics, deep learning algorithms, and computer vision technology, the system facilitates real-time anomaly detection, preventio

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Vivian Ofure Eghaghe, Olajide Soji Osundare, Chikezie Paul-Mikki Ewim, and Ifeanyi Chukwunonso Okeke. "Fostering international AML cooperation: The role of analytical tools in enhancing cross-border regulatory frameworks." Computer Science & IT Research Journal 5, no. 10 (2024): 2371–402. http://dx.doi.org/10.51594/csitrj.v5i10.1652.

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In an increasingly interconnected world, the necessity for robust Anti-Money Laundering (AML) frameworks that transcend national borders has become paramount. This paper examines the critical role of analytical tools in fostering international AML cooperation and enhancing cross-border regulatory frameworks. Effective AML measures rely on comprehensive data analysis to identify and mitigate financial crime risks. Analytical tools, including big data analytics, machine learning, and artificial intelligence, facilitate the collection, processing, and interpretation of vast amounts of financial d

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McElreath, David H., Sherri DioGuardi, and Daniel Adrian Doss. "Pre-Crime Prediction." International Journal of Service Science, Management, Engineering, and Technology 13, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/ijssmet.298672.

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This paper considered the emerging use of predictive analytics in the justice domain with respect to potential bias. It discussed predictive algorithms and methods from the perspectives of reported crime and community safety in the United States. Although predictive algorithms, techniques, and implementation contexts are emerging, imperfection exists with respect to their use. Despite any effectiveness or efficiency of using predictive algorithms, such use should neither deny human rights nor transgress societal laws. Regardless, the emergence of predictive policing fuels and enhances the clas

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Kumaran, T. Vasantha, O. M. Murali, and R. Joseph. "Spatial Data Analytics and the Role of Geoinformatics Technologies." Journal of Development Economics and Management Research Studies 12, no. 23 (2025): 13–40. https://doi.org/10.53422/jdms.2025.122303.

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With the power of GIS, remote sensing, and spatial databases, organisations can unlock valuable insights that drive effective decision-making across numerous domains. It continues to grow and the role of Geoinformatics becomes increasingly vital in addressing complex challenges and enhancing our ability to manage resources sustainably, plan urban environments, and monitor environmental changes. In the context of avenue trees, spatial data science has enabled the monitoring and early detection of tree degradation through remote sensing, LiDAR, and citizen science initiatives. Regarding crime an

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Asor, Jonardo R. "Implementation of Predictive Crime Analytics in Municipal Crime Management System in Calauan, Laguna, Philippines." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.3 (2020): 150–57. http://dx.doi.org/10.30534/ijatcse/2020/2291.32020.

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Dinesh,, Dr Chandrasekharan. "Criminal Investigation System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33811.

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The Criminal Investigation System with represents a significant advancement in the field of law enforcement technology. By leveraging the power of data and predictive analytics, it empowers investigators to solve cases faster, identify suspects more accurately, and ultimately, enhance public safe. Based on the historical crime data suggest that Hashing algorithm. The digital system functions by gathering and classifying different kinds of information, such as crime scene reports, witness statements, forensic evidence, and suspect profiles. It facilitates the work of an officer by allowing him

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Murauskas, Donatas. "Predictive analytics in crime prevention and the European Convention on Human Rights: tackling risks in privacy and fair trial frameworks." Acta Universitatis Lodziensis. Folia Iuridica 97 (December 30, 2021): 225–50. http://dx.doi.org/10.18778/0208-6069.97.13.

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In this paper, I discuss whether the European Convention on Human Rights provides safeguards to individuals affected by predictive analytics in crime prevention. I start with depicting a conceptual issue that worries legal scholars – the trend of law-enforcement authorities to increase their attention to crime prevention rather than traditional criminal investigations. Then, I dive into the right to privacy case-law of the European Court of Human Rights looking for the Court’s references to the threats of data processing. Lastly, I select concrete cases of the European Court of Human Rights on

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Utkina, Maryna. "DIGITAL IDENTIFICATION AND FINANCIAL MONITORING: NEW TECHNOLOGIES IN THE FIGHT AGAINST CRIME." Scientific Journal of Polonia University 58, no. 3 (2023): 303–8. http://dx.doi.org/10.23856/5842.

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The purpose of the research is to explore the role of emerging technologies in combating criminal activities, particularly in digital identification and financial monitoring. The article aims to highlight how these new technologies can enhance the effectiveness of law enforcement agencies and financial institutions in detecting and preventing crime. Methods. The methodological basis of the research was a set of methods of scientific knowledge. In particular, the phenomenological method allowed the author to analyse national, international, and foreign law legal sources. The analytical method w

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Helbich, Marco, and Michael Leitner. "Frontiers in Spatial and Spatiotemporal Crime Analytics—An Editorial." ISPRS International Journal of Geo-Information 6, no. 3 (2017): 73. http://dx.doi.org/10.3390/ijgi6030073.

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Queiroz Neto, José F., Emanuele Santos, Creto Augusto Vidal, and David S. Ebert. "A Visual Analytics Approach to Facilitate Crime Hotspot Analysis." Computer Graphics Forum 39, no. 3 (2020): 139–51. http://dx.doi.org/10.1111/cgf.13969.

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Aarav Singhania. "White collar crime identification in India: A Critical study." Indian Journal of Law 2, no. 3 (2024): 32–37. http://dx.doi.org/10.36676/ijl.v2.i3.31.

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White-collar crime poses a significant challenge to the economic and social fabric of India, as it involves non-violent financial offenses committed by individuals or organizations in positions of trust and authority. This research paper critically examines the landscape of white-collar crime identification in India, shedding light on the types of offenses prevalent, the mechanisms employed for detection, and the complex challenges faced by law enforcement and regulatory bodies. The study begins with an exploration of the various types of white-collar crimes observed in the Indian context, inc

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Navale, Dr Mahesh. "RescueNow: Real-time SOS and Predictive Women's Safety System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem38086.

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This paper presents a comprehensive women’s safety application designed to address the growing concerns of personal safety through technological intervention. The system integrates multiple advanced features such as real-time SOS alerts, geofencing to detect high-risk areas, AI-driven crime predictions, and multiple SOS activation methods, including voice commands, shake detection, and button press. Additionally, the app employs silent recording functionality to gather evidence in distress situations and incorporates secure communication channels for contacting law enforcement. The application

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Xiao, Jia, and Xiaolu Zhou. "Crime Exposure Along My Way Home: Estimating Crime Risk Along Personal Trajectory by Visual Analytics." Geographical Analysis 52, no. 1 (2019): 49–68. http://dx.doi.org/10.1111/gean.12187.

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Shanmugapriya, Dr V., and Santhoshan .M.M. "ONLINE CRIME JUDGEMENT SYSTEM." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02417.

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The rise of digital crimes has necessitated the development of online crime judgmental systems to ensure efficient, transparent, and timely justice. Traditional legal systems often struggle with delays, resource limitations, and complexities in handling cybercrimes, leading to a demand for technology-driven judicial solutions. Online crime judgmental systems integrate artificial intelligence (AI), blockchain, cloud computing, and digital forensics to streamline case proceedings, enhance evidence validation, and improve accessibility to justice. This paper explores the role of AI in legal decis

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Sukhodolov, Alexander, and Anna Bychkova. "Artificial Intelligence in Crime Counteraction, Prediction, Prevention and Evolution." Всероссийский криминологический журнал 12, no. 6 (2018): 753–66. http://dx.doi.org/10.17150/2500-4255.2018.12(6).753-766.

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Crime prediction, prevention and counteraction with the use of modern technologies should, according to the authors, become a priority task for the state, along with the development of economy, education, medicine and the enhancement of defense capacity. The article describes the concepts of «artificial intelligence», «machine learning», «big data», «deep learning», «neural networks» from the standpoint of how they are used both by criminals and by law enforcement bodies and courts. The authors examine the application of technologies which use artificial intelligence, hi tech crime (fishing, d

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Padmaja, K. Vinaya, Kalyani T, T. Rushalani, and Dr M. Ramasubramanian. "A Data Analytics Approach to the Cyber Crime Underground Economy." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 293–96. http://dx.doi.org/10.22214/ijraset.2022.45908.

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Abstract: Despite the rapid escalation of cyber threats, there has still been little research into the foundations of the subject or methodologies that could serve to guide Information Systems researchers and practitioners who deal with cyber security. In addition, little is known about Crime-as-a-Service (CaaS), a criminal business model that underpins the cybercrime underground. This research gap and the practical cybercrime problems we face have motivated us to investigate the cybercrime underground economy by taking a data analytics approach from a design science perspective. To achieve th

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., Saoumya. "A PREDICTIVE MODEL FOR MAPPING CRIME USING BIG DATA ANALYTICS." International Journal of Research in Engineering and Technology 04, no. 04 (2015): 344–48. http://dx.doi.org/10.15623/ijret.2015.0404061.

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Sarkar, Amrita. "Real-Time Crime Prediction in India Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 641–46. https://doi.org/10.22214/ijraset.2025.73053.

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This paper presents a machine learning-based system for predicting crime patterns across Indian cities using historical crime data from 2010-2024. By integrating Random Forest regression with geospatial analysis, the model achieves 92.7% accuracy (R² score) in forecasting crime rates per 100,000 population. The system processes 15+ crime categories across 19 cities, including Mumbai, Delhi, and Bengaluru, using features like population density, crime type, and temporal trends. A webbased dashboard provides interactive crime heatmaps, prediction visualizations, and comparative analytics for law

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Archana, S., and R. Keren. "Crime Hotspot Mapping and Behavioral Analysis System." Research and Reviews: Advancement in Cyber Security 2, no. 3 (2025): 1–5. https://doi.org/10.5281/zenodo.15421539.

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<em>The Crime Hotspot Mapping and Behavioral Analysis System is designed to enhance public safety by leveraging data analytics, artificial intelligence, and geospatial technologies. This system aims to identify crime-prone areas through historical data patterns and visualize them on an interactive map, aiding law enforcement in proactive decision-making. It integrates behavioral analysis to assess potential threats and criminal tendencies, enabling predictive policing strategies. The system supports various stakeholders including police departments, civic authorities, and public policy makers

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Vivian Ofure Eghaghe, Olajide Soji Osundare, Chikezie Paul-Mikki Ewim, and Ifeanyi Chukwunonso Okeke. "Advancing AML tactical approaches with data analytics: Transformative strategies for improving regulatory compliance in banks." Finance & Accounting Research Journal 6, no. 10 (2024): 1893–925. http://dx.doi.org/10.51594/farj.v6i10.1644.

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The growing complexity of financial crimes necessitates advanced Anti-Money Laundering (AML) strategies that leverage data analytics to improve regulatory compliance in banks. As traditional AML methods face challenges in detecting sophisticated money laundering schemes, data analytics offers transformative solutions by enabling real-time monitoring, enhanced risk detection, and predictive analysis. This review explores the integration of data analytics in AML systems and its impact on regulatory compliance, focusing on strategies that banks can adopt to mitigate risks and adhere to evolving r

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P, Sajitha. "Crime Type and Occurrence Prediction Using Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 450–56. http://dx.doi.org/10.22214/ijraset.2024.64542.

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This project is entitled as “Crime Type and Occurrence Prediction Using Machine Learning Algorithm” Crime is still a major worry and a serious problem in our society. As a result, crime prevention is a significant issue that needs to be examined methodically. Detecting and preventing crime requires effective crime analytics, which is also crucial for assessing how well criminal investigations are working. To create precise forecasts, inferences are drawn from trained data. a system that uses user input to forecast different characteristics of crime. Users select the year, offense type, and cit

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Mohan, Ashok Kumar, Sethumadhavan Madathil, and Lakshmy K. V. "Holistic Analytics of Digital Artifacts." International Journal of Digital Crime and Forensics 13, no. 5 (2021): 78–100. http://dx.doi.org/10.4018/ijdcf.20210901.oa5.

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Investigation of every crime scene with digital evidence is predominantly required in identifying almost all atomic files behind the scenes that have been intentionally scrubbed out. Apart from the data generated across digital devices and the use of diverse technology that slows down the traditional digital forensic investigation strategies. Dynamically scrutinizing the concealed or sparse metadata matches from the less frequent archives of evidence spread across heterogeneous sources and finding their association with other artifacts across the collection is still a horrendous task for the i

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Rivera Ruiz, Daniel, and Alisha Sawant. "Quantitative Analysis Of Crime Incidents In Chicago Using Data Analytics Techniques." Computers, Materials & Continua 59, no. 2 (2019): 389–96. http://dx.doi.org/10.32604/cmc.2019.06433.

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Tao, Shu Wei, Ooi Chong Yang, Mohamed Sahil Mohamed Salim, and Wahidah Husain. "A Proposed Bi-layer Crime Prevention Framework Using Big Data Analytics." International Journal on Advanced Science, Engineering and Information Technology 8, no. 4-2 (2018): 1453. http://dx.doi.org/10.18517/ijaseit.8.4-2.6802.

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Boppuru, Prathap Rudra, and Ramesha K. "Geo-Spatial Crime Analysis Using Newsfeed Data in Indian Context." International Journal of Web-Based Learning and Teaching Technologies 14, no. 4 (2019): 49–64. http://dx.doi.org/10.4018/ijwltt.2019100103.

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Social media is the platforms where users communicate, interact, share ideas, career interest, pictures, video, etc. Social media gives an opportunity to analyze the human behavior. Crime analysis using data from social media such as Newsfeeds, Facebook, Twitter, etc., is becoming one of the emerging areas of research for law enforcement organizations across the world. The intelligence gathered through data is used for identifying future attacks and plan for reinforcements. This article focuses on the implementation of textual data analytics by collecting the data from different newsfeeds and

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Ismail, Ismail, Felecia Felecia, Anisa Kurniatul Azizah, and Diana Rahmawati. "Artificial Intelligence and Big Data Analytics to Break Drug Networks: Lessons from Law Enforcement in Indonesia." West Science Law and Human Rights 3, no. 02 (2025): 124–31. https://doi.org/10.58812/wslhr.v3i02.1816.

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The rapid advancement of Artificial Intelligence (AI) and Big Data Analytics has transformed various sectors, including law enforcement and criminal investigation. This study aims to explore how Indonesian law enforcement agencies are utilizing AI and Big Data to combat drug trafficking networks. Using a qualitative approach, data was collected through in-depth interviews with five key informants, including law enforcement officials, legal experts, and technology practitioners. The results show that AI technologies, such as predictive analytics, surveillance systems, and facial recognition, ha

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Adewuyi, Oluleye M. "The Double-Edged Ledger: Cryptocurrency, Financial Crime, and the Potential of Blockchain Forensics." International Journal of Research and Innovation in Social Science IX, no. V (2025): 5739–42. https://doi.org/10.47772/ijriss.2025.905000442.

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The swift rise of cryptocurrencies has created both groundbreaking opportunities and unique challenges regarding financial crime. The decentralized and pseudo-anonymous characteristics of these digital currencies can facilitate illegal activities; however, the transparency inherent in blockchain technology also provides groundbreaking methods for detection and prevention. This paper investigates the connection between cryptocurrencies and financial crime by outlining common types of crypto-related offenses. Additionally, it explores the growing field of blockchain forensics and assesses the ef

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Dass, Pranav, Vedika Gupta, Shreya Dhingra, Rohan Arora, Piyush Katariya, and Adarsh Kumar. "A deep learning model to predict and classify crime rate using Tweets." Journal of Discrete Mathematical Sciences and Cryptography 27, no. 8 (2024): 2283–99. https://doi.org/10.47974/jdmsc-1822.

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Crime is a detrimental socioeconomic issue that impacts individuals globally. Crime significantly affects a nation’s standard of living, financial well-being, and standing. Over the past few years, there has been a significant increase in the crime rate. Law enforcement must implement proactive measures to mitigate crime rates. Enhanced technologies and innovative methods are required to bolster crime analytics and safeguard communities. Precise and up-to-date crime predictions can reduce crime rates, but they provide a complex problem for scientists due to the multiple factors that drive crim

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Gombiro, Cross, Mmaki Jantjies, and Nehemiah Mavetera. "A conceptual framework for detecting financial crime in mobile money transactions." Journal of Governance and Regulation 4, no. 4 (2015): 727–34. http://dx.doi.org/10.22495/jgr_v4_i4_c6_p8.

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Mobile money has made it possible for the unbanked to access financial service to areas previous not accessibly to traditional banking systems. Africa in particular, has indeed seen a growth in use of such services owing to the high penetration of mobile phones. While traditional banking services have been well regulated and secured, mobile money services are still new and vulnerable. Also, attacks and crimes targeting the internet, new technologies and new methods of payments have become sophisticated. This scenario requires novel proactive, real time techniques and solutions to detect financ

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Salam, Hassan Ahmad Ali Kawar Mohammed Mousa. "The role of forensic DNA experts and Artificial Intelligence in enhancing the effectiveness of wildlife crime investigations in Cyprus." Journal of Wildlife and Biodiversity 9, no. 2 (2025): 77–94. https://doi.org/10.5281/zenodo.15510998.

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The increasing prevalence of wildlife crimes globally and in Cyprus presents an urgent need for more effective investigation methods. This study explores the factors that influence the effectiveness of wildlife crime investigations, focusing on the role of forensic DNA experts, different forensic methods, inter-agency relationships, and the incorporation of Artificial Intelligence (AI)-based tools. Employing a quantitative approach and a sample of 560 respondents, the study finds that all the posited variables involvement of forensic DNA experts, types of forensic methods used, organizational

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