The security risk management framework is an essential part of strategic management for government agencies. It allows a government to systematically identify and address the risks associated with its activities to achieve sustainability for different activities of security risk management. The goal of security risk management is to add sustainable value to government activities and reduce the chance of security breaches. Applying security risk management techniques used to government projects can increase the chances of success, help achieve objectives, and assist in finding preventive solutions for future projects. The application of security risk management is profitable for government agencies because it sets specific risk management objectives that are based on the broader overall strategy. It contributes to the achievement of strategic objectives with mechanisms like Spearman's rank correlation coefficient and simple linear regression. These techniques can improve decision-making, planning and implementation of government activities, as well as reduce the negative consequences of present threats. It is recommended to apply the integrated security risk management framework proposed in this paper to increase the effectiveness of security risk management in government agencies. Also using quantitative and intelligent techniques in the analysis and estimation of security risks can help managers to make decisions regarding security issues in government agencies.
Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.
When applied to an entire field, automation and autonomous systems are among the rare creative superpowers capable of catapulting progress at an exponential rate. The arrival of machine intelligence will give such automated machines the intelligence to perform their tasks with power of outcome, drastically reducing the need for human intervention in redundant processes. Large-scale technological progress can be traced back to responsibilities that are simplified and, as a result, more easily distinguished by means of automation. In accordance with these guidelines, we propose creating a product that eliminates or significantly reduces the need for human intervention in primary issue statements that can be automated and processed. The public safety infrastructure of today relies on surveillance cameras, but these devices are merely video recorders; they have no intelligence of their own. Automated video streams are now required for automatic event detection thanks to the massive amount of data produced by surveillance cameras. The project's main objective is to increase public safety through the mechanization of crime measurement and review using actual Closed-Circuit Television footage (CCTV). This is achieved by assigning the task of recognizing criminal behavior to a system that can do so automatically, allowing for more precise tracking. In this study, we present a model with a precision of 0.95 for assault and 0.97 for abuse.
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