Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
Article Fingerprint
ReserarchID
T80PG
The application of state-of-the-art in deep learning detection algorithms, such as You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD), presents a significant opportunity for enhancing crime prevention and control strategies. This research focuses on leveraging the SSD algorithm to detect common crime weapons on individuals in both pre-crime video scenes and real-world crime scenarios. By thoroughly understanding the operational principles of the SSD algorithm, we adapted it for the identification of dangerous weapons commonly associated with violent crimes. Our detection model, which targets both weapons and individuals, establishes a robust foundation for an artificial intelligence (AI) system that accurately predicts individuals at high risk. The model first identifies the presence of a person and subsequently checks for any of the specified weapons. If a weapon is detected, the system further analyzes the individual’s movement and speed within the frame of reference. Should the individual exceed a predetermined movement threshold, the system flags them as high risk. For this study, the SSD model utilized a VGG16 backbone and was trained on a dataset comprising 3,317 images, featuring four distinct weapon categories: handgun, shotgun, rifle, and knife. The dataset was collected from UCF, via Kaggle and complimented with additional weapons from Google download all, all the sources are secondary, open sources and loyalty-free. We achieved a mean average precision of 84.19% across five classes after training for 59 epochs The findings of this research demonstrate the effectiveness of the SSD algorithm in crime prevention and control, contributing to the ongoing discourse surrounding the application of detection algorithms for crime prediction. This work aims to provide technological innovations that can assist local law enforcement agencies in their operational duties. Additionally, the insights gained from this study may enhance the detection of abnormal behavior within the broader field of artificial intelligence.
Yusuff Adeniyi Giwa. 2026. \u201cAdvancing Real-Time Crime Weapon Detection and High-Risk Person Classification in Pre-Crime Scenes: A Comprehensive Machine Vision Approach Utilizing SSD Detector\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 25 (GJCST Volume 25 Issue D1): .
Article file ID not found.
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Neural Networks and Rules-based Systems used to Find Rational and
A Comparative Study of the Effeect of Promotion on Employee
The Problem Managing Bicycling Mobility in Latin American Cities: Ciclovias
Impact of Capillarity-Induced Rising Damp on the Energy Performance of
The application of state-of-the-art in deep learning detection algorithms, such as You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD), presents a significant opportunity for enhancing crime prevention and control strategies. This research focuses on leveraging the SSD algorithm to detect common crime weapons on individuals in both pre-crime video scenes and real-world crime scenarios. By thoroughly understanding the operational principles of the SSD algorithm, we adapted it for the identification of dangerous weapons commonly associated with violent crimes. Our detection model, which targets both weapons and individuals, establishes a robust foundation for an artificial intelligence (AI) system that accurately predicts individuals at high risk. The model first identifies the presence of a person and subsequently checks for any of the specified weapons. If a weapon is detected, the system further analyzes the individual’s movement and speed within the frame of reference. Should the individual exceed a predetermined movement threshold, the system flags them as high risk. For this study, the SSD model utilized a VGG16 backbone and was trained on a dataset comprising 3,317 images, featuring four distinct weapon categories: handgun, shotgun, rifle, and knife. The dataset was collected from UCF, via Kaggle and complimented with additional weapons from Google download all, all the sources are secondary, open sources and loyalty-free. We achieved a mean average precision of 84.19% across five classes after training for 59 epochs The findings of this research demonstrate the effectiveness of the SSD algorithm in crime prevention and control, contributing to the ongoing discourse surrounding the application of detection algorithms for crime prediction. This work aims to provide technological innovations that can assist local law enforcement agencies in their operational duties. Additionally, the insights gained from this study may enhance the detection of abnormal behavior within the broader field of artificial intelligence.
We are currently updating this article page for a better experience.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
