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An optimized whale-based modular neural framework to predict crime events

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Abstract

Nowadays, the surveillance system is an important asset for Crime detection. Hence, many methods were implemented to detect the crime in different ways, such as based on activities, handling tools, facial recognition, etc. In addition, to execute this method, several neural approaches were developed in a different environment; hence the noise content and video image complexity have made the crime detection task difficult. Therefore, to improve crime detection, the present work has aimed to design a novel Whale-based Modular Neural Framework (WbMNF) for attaining the best prediction results. In addition, the crime events were predicted by analyzing the face. Initially, the videos were collected and imported to the system. Hereafter, the training flaws were eliminated in the pre-processing layer. Moreover, the pre-processed data is entered into the classification layer of WbMNF to extract the face feature and to predict the criminals. Hence, the incorporation of whale fitness has provided the finest prediction outcome. Subsequently, the result of the developed technique is compared with state of the art schemes and has earned improved crime detection accuracy than the existing models.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, 501 218, India

    K. Kishore Kumar & H. Venkateswara Reddy

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  1. K. Kishore Kumar
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  2. H. Venkateswara Reddy
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Correspondence to K. Kishore Kumar.

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Kumar, K.K., Reddy, H.V. An optimized whale-based modular neural framework to predict crime events. Multimed Tools Appl 82, 30855–30873 (2023). https://doi.org/10.1007/s11042-023-14660-2

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