International Journal of Intelligent Information Systems

Research Article | | Peer-Reviewed |

Enhancing Crime Investigation and Emergency Response Through a Combined Machine Learning Approach

Received: Jan. 02, 2024    Accepted: Jan. 17, 2024    Published: Feb. 01, 2024
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Abstract

The contemporary world necessitates effective solutions for crime investigation and emergency response to ensure public safety. This research pioneers an innovative approach by creating a predictive model for an integrated Crime Investigation and Emergency Response. Utilizing data-driven analysis, advanced machine learning, and modern information technology, it aims to enhance the efficiency of law enforcement and emergency procedures. Recognizing the pivotal role of Information Systems/Information Technology (IS/IT) in disaster management and crime investigation, the study emphasizes the urgency for efficient IT solutions to manage critical incidents. This focus seeks to minimize their impact on human lives, societal norms, economic stability, and political arenas. Exploring the integration of data-centric tools and information systems highlights their potential for expediting coordinated responses across various organizational levels, from local to global scopes. Delving into the challenges facing law enforcement in analyzing crime patterns, especially in cases involving violent offenses with extensive statistical data, this research introduces a machine-learning strategy combining regression and classification techniques. The primary goal is to reveal crucial patterns, particularly in predicting perpetrator characteristics such as age, gender, and their relationship with the victim. The envisioned Crime Investigation System (CIS) aims to streamline investigative processes, championing data-centric approaches facilitated by data mining technologies. Moreover, the research underscores technology's transformative impact on reshaping emergency management and notification systems. It underscores the importance of reducing response times, involving the public more actively, and deriving practical insights from reported data. Through comprehensive data analysis spanning several years, the study sheds light on unsolved crimes, notably those involving handguns, showcasing the model's potential to enhance law enforcement capabilities. These findings highlight the significant promise of the developed predictive model in bolstering law enforcement and emergency response procedures, potentially revolutionizing public safety operations. Ultimately, this research aspires to contribute to a safer and more responsive society by leveraging predictive models and technology-driven systems within law enforcement and public safety domains.

DOI 10.11648/ijiis.20241301.12
Published in International Journal of Intelligent Information Systems ( Volume 13, Issue 1, February 2024 )
Page(s) 6-19
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning, Classification, ANN, KNN, Crime Investigation, Response System

References
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[8] Idhole, M. G. Dhawak, S. W., Rathod, R. S., Rathod, K. B., and Chavhan, K. L. (2021). Crime Report Management System, International Research Journal of Modernization in Engineering Technology and Science, 3(7): 760-764.
[9] Priya, S., Srivastava, K., and Islam S. S, (2019), Online Crime Reporting System, International Journal of Recent Technology and Engineering (IJRTE), 8(4): 2154-2157.
[10] Selvakani, S., Vasumathi, K., and Harikaran, M. (2019), Web based online crime reporting system using ASP. Net, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(10): 1220-1224.
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[12] Tabassum, K., Shaiba, H., Shamrani, S., and Otaibi, S. (2018), e-Cops: An Online Crime Reporting and Management System for Riyadh City, 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia, 1-8, doi: 10.1109/CAIS.2018.8441987.
[13] Kiruthika J., Armaan S., Syed A. F. J. (2021), Interactive Mobile Based Crime Reporting System Integrated With Map Feature and Exploratory Data Analysis, International Journal of Aquatic Science, 12(3): 1328-1335.
[14] Sathayabama, B., Savitha, R., & Iswarya, R. (2022), Development of online crime reporting system using Python and MySQL, International Journal of Computer Applications, 171(13), 1-6.
[15] Jayasinghe, K. N., & Perera, M. P. L. (2021), An automatic crime reporting and immediate response system. International Journal of Computer Trends and Technology, 69(5), 1-5.
[16] Shakir, M., Shoaib, M., Shahzad, H., & Aamir, M. (2023), Lightweight blockchain-based framework for secure and efficient online crime reporting. IEEE Access, 11, 23456-23467.
[17] Babar, M., Sahree, P., Katre, R., Ganvir, P., Sakharwade, B., & Chikate, R. (2023), Online Crime Reporting System (OCS) to modernize and streamline the process of sharing crime-related data with the public. International Journal of Computer Science and Engineering, 11(3), 1-10.
[18] Manjula, M., & Kumar, M. P. S. (2020), Reporting and management system for online crime, International Research Journal of Engineering and Technology, 7(4), 2084-2090.
[19] Anitha, R., & Sundar, M. A. (2019), Online crime report and maintenance using centralized data, Journal of Emerging Technologies and Innovative Research (JETIR), 8(3), 1-9.
[20] Nivethan, R., Gopinath, G., Santhiya, K., & Jeyanthi, V. (2022), Web-based online crime reporting system using Python and MySQL, International Journal of Scientific & Engineering Research, 13(5), 245-251.
[21] John-Otumu A. M., Nwokonkwo O. C., Izu-Okpara I. U., Dokun O. O., Konyeha S., and Oshoiribhor E. O. (2020), A Novel Smart CBT Model for Detecting Impersonators using Machine Learning Technique Proceedings of the 2020 IEEE 2nd International Conference on Cyberspace (Cyber Nigeria), pp. 21-30. DOI: 10.1109/CYBERNIGERIA51635.2021.9428814.
Cite This Article
  • APA Style

    Nwokonkwo, O. C., Buki, O. R., John-Otumu, A. M., Aniugo, V. O. (2024). Enhancing Crime Investigation and Emergency Response Through a Combined Machine Learning Approach. International Journal of Intelligent Information Systems, 13(1), 6-19. https://doi.org/10.11648/ijiis.20241301.12

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    ACS Style

    Nwokonkwo, O. C.; Buki, O. R.; John-Otumu, A. M.; Aniugo, V. O. Enhancing Crime Investigation and Emergency Response Through a Combined Machine Learning Approach. Int. J. Intell. Inf. Syst. 2024, 13(1), 6-19. doi: 10.11648/ijiis.20241301.12

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    AMA Style

    Nwokonkwo OC, Buki OR, John-Otumu AM, Aniugo VO. Enhancing Crime Investigation and Emergency Response Through a Combined Machine Learning Approach. Int J Intell Inf Syst. 2024;13(1):6-19. doi: 10.11648/ijiis.20241301.12

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  • @article{10.11648/ijiis.20241301.12,
      author = {Obi Chukwuemeka Nwokonkwo and Oladele Robert Buki and Adetokunbo MacGregor John-Otumu and Victor Onyekachi Aniugo},
      title = {Enhancing Crime Investigation and Emergency Response Through a Combined Machine Learning Approach},
      journal = {International Journal of Intelligent Information Systems},
      volume = {13},
      number = {1},
      pages = {6-19},
      doi = {10.11648/ijiis.20241301.12},
      url = {https://doi.org/10.11648/ijiis.20241301.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.ijiis.20241301.12},
      abstract = {The contemporary world necessitates effective solutions for crime investigation and emergency response to ensure public safety. This research pioneers an innovative approach by creating a predictive model for an integrated Crime Investigation and Emergency Response. Utilizing data-driven analysis, advanced machine learning, and modern information technology, it aims to enhance the efficiency of law enforcement and emergency procedures. Recognizing the pivotal role of Information Systems/Information Technology (IS/IT) in disaster management and crime investigation, the study emphasizes the urgency for efficient IT solutions to manage critical incidents. This focus seeks to minimize their impact on human lives, societal norms, economic stability, and political arenas. Exploring the integration of data-centric tools and information systems highlights their potential for expediting coordinated responses across various organizational levels, from local to global scopes. Delving into the challenges facing law enforcement in analyzing crime patterns, especially in cases involving violent offenses with extensive statistical data, this research introduces a machine-learning strategy combining regression and classification techniques. The primary goal is to reveal crucial patterns, particularly in predicting perpetrator characteristics such as age, gender, and their relationship with the victim. The envisioned Crime Investigation System (CIS) aims to streamline investigative processes, championing data-centric approaches facilitated by data mining technologies. Moreover, the research underscores technology's transformative impact on reshaping emergency management and notification systems. It underscores the importance of reducing response times, involving the public more actively, and deriving practical insights from reported data. Through comprehensive data analysis spanning several years, the study sheds light on unsolved crimes, notably those involving handguns, showcasing the model's potential to enhance law enforcement capabilities. These findings highlight the significant promise of the developed predictive model in bolstering law enforcement and emergency response procedures, potentially revolutionizing public safety operations. Ultimately, this research aspires to contribute to a safer and more responsive society by leveraging predictive models and technology-driven systems within law enforcement and public safety domains.
    },
     year = {2024}
    }
    

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  • TY  - JOUR
    T1  - Enhancing Crime Investigation and Emergency Response Through a Combined Machine Learning Approach
    AU  - Obi Chukwuemeka Nwokonkwo
    AU  - Oladele Robert Buki
    AU  - Adetokunbo MacGregor John-Otumu
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    DO  - 10.11648/ijiis.20241301.12
    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
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    PB  - Science Publishing Group
    SN  - 2328-7683
    UR  - https://doi.org/10.11648/ijiis.20241301.12
    AB  - The contemporary world necessitates effective solutions for crime investigation and emergency response to ensure public safety. This research pioneers an innovative approach by creating a predictive model for an integrated Crime Investigation and Emergency Response. Utilizing data-driven analysis, advanced machine learning, and modern information technology, it aims to enhance the efficiency of law enforcement and emergency procedures. Recognizing the pivotal role of Information Systems/Information Technology (IS/IT) in disaster management and crime investigation, the study emphasizes the urgency for efficient IT solutions to manage critical incidents. This focus seeks to minimize their impact on human lives, societal norms, economic stability, and political arenas. Exploring the integration of data-centric tools and information systems highlights their potential for expediting coordinated responses across various organizational levels, from local to global scopes. Delving into the challenges facing law enforcement in analyzing crime patterns, especially in cases involving violent offenses with extensive statistical data, this research introduces a machine-learning strategy combining regression and classification techniques. The primary goal is to reveal crucial patterns, particularly in predicting perpetrator characteristics such as age, gender, and their relationship with the victim. The envisioned Crime Investigation System (CIS) aims to streamline investigative processes, championing data-centric approaches facilitated by data mining technologies. Moreover, the research underscores technology's transformative impact on reshaping emergency management and notification systems. It underscores the importance of reducing response times, involving the public more actively, and deriving practical insights from reported data. Through comprehensive data analysis spanning several years, the study sheds light on unsolved crimes, notably those involving handguns, showcasing the model's potential to enhance law enforcement capabilities. These findings highlight the significant promise of the developed predictive model in bolstering law enforcement and emergency response procedures, potentially revolutionizing public safety operations. Ultimately, this research aspires to contribute to a safer and more responsive society by leveraging predictive models and technology-driven systems within law enforcement and public safety domains.
    
    VL  - 13
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Author Information
  • Department of Information Technology, Federal University of Technology, Owerri, Nigeria

  • Department of Information Technology, Federal University of Technology, Owerri, Nigeria

  • Department of Information Technology, Federal University of Technology, Owerri, Nigeria

  • Department of Mechatronics Engineering, Federal University of Technology, Owerri, Nigeria

  • Section