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A significant degree of uncertainty surrounds security events in Mexico because of the interaction of multiple socioeconomic and political elements. Conventional probabilistic and logical approaches frequently fall short of properly handling this uncertainty. To effectively navigate and manage security incidents in Mexico, this study suggests applying neutrosophic logic, an extension of classical logic that allows for indeterminacy and partial knowledge. Neutrosophic logic provides a more adaptable framework for making decisions in uncertain contexts by simultaneously handling true, false, and indeterminate values. In this study, we have created a neutrosophic-based model that includes metrics like social unrest, economic instability, and crime rates to provide a thorough evaluation of the security environment. When the model is put to the test using actual data from different parts of Mexico, it outperforms conventional techniques in terms of prediction and interpretation of security occurrences. The findings demonstrate how neutrosophic logic helps security analysts and policymakers make more thoughtful and nuanced judgments by offering insightful information about the ambiguous nature of security occurrences. The ability to foresee and address possible risks is much improved when neutrosophic logic is incorporated into security event analysis. This not only improves overall security management but also creates new opportunities for its application in other sectors where uncertainty is crucial. [ABSTRACT FROM AUTHOR]

The process of installing and configuring wireless sensors in diverse environments to gather data and monitor particular characteristics is known as wireless sensor deployment. Implementing wireless sensor networks requires evaluating their effectiveness, difficulties, and room for development. A thorough analysis of wireless sensor deployment can help pinpoint problem areas, provide solutions to problems, and improve the network's functionality for a certain application or environment. It entails evaluating the state, difficulties, developments, and possible advancements of Wireless Sensor Networks (WSNs). The performance, efficiency, and reliability of wireless sensor networks in a variety of application domains can be improved by doing a thorough analysis of WSNs, which will help to identify difficulties and explore potential improvements. Due to broad range of potential applications in a future of remote monitoring and automation, Wireless sensor Network (WSN) has drawn the attention of researchers in recent years. Since the performance and lifespan of any WSN are largely dependent on the effective positioning of Sensor Nodes, this is a significant area. Researchers have put up a number of models for the deployment of SNs across expansive open areas. The classification, operation, and comparative study of these models are the main goals of this paper. [ABSTRACT FROM AUTHOR]

Deep learning, a subset of machine learning, aims to imbue machines with human-like perception, learning, and intelligence through advanced technology. It has made significant strides in fields such as speech recognition, computer vision and NLP. GAN (Generative Adversarial Network) is an innovative domain within deep learning that has made significant growth in areas like image processing, art, music, data analysis, drug discovery, and gaming industry applications. Biometric systems, which use distinct physiological features like iris patterns, fingerprints, and facial characteristics for authentication, have increasingly integrated deep learning models. As biometric technology becomes widespread, ensuring information security in sectors like banking, education, and airports is paramount. Deep learning-based generative networks have revolutionized synthetic biometric data generation, produced high-quality artificial data while preserved the statistical characteristics of the original dataset. These synthetic biometric datasets are invaluable for testing and developing biometric systems, especially under high-demand conditions. The purpose of this paper is to present a broad overview of GANs, its loss function, highlighting the popularly used architectures and application domains of the most well-known variations. The optimal biometric application area and the efficacy of various model designs will be discussed. [ABSTRACT FROM AUTHOR]