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

Obi Chukwuemeka Nwokonkwo; Oladele Robert Buki; Adetokunbo MacGregor John-Otumu; Victor Onyekachi Aniugo

doi:doi:10.11648/ijiis.20241301.12

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

Obi Chukwuemeka Nwokonkwo, Oladele Robert Buki, Adetokunbo MacGregor John-Otumu^*, Victor Onyekachi Aniugo

Published in International Journal of Intelligent Information Systems (Volume 13, Issue 1)

Received: 2 January 2024 Accepted: 17 January 2024 Published: 1 February 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.

Published in	International Journal of Intelligent Information Systems (Volume 13, Issue 1)
DOI	10.11648/ijiis.20241301.12
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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Cite This Article

Plain Text BibTeX RIS

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://article.sciencepublishinggroup.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
AU  - Victor Onyekachi Aniugo
Y1  - 2024/02/01
PY  - 2024
N1  - https://doi.org/10.11648/ijiis.20241301.12
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
SP  - 6
EP  - 19
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
IS  - 1
ER  -

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Author Information

Obi Chukwuemeka Nwokonkwo

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

Contact Email
Oladele Robert Buki

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

Contact Email
Adetokunbo MacGregor John-Otumu

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

Contact Email
Victor Onyekachi Aniugo

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

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Sections

Plain Text BibTeX RIS

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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
AU  - Victor Onyekachi Aniugo
Y1  - 2024/02/01
PY  - 2024
N1  - https://doi.org/10.11648/ijiis.20241301.12
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
SP  - 6
EP  - 19
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
IS  - 1
ER  -

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