Unavoidable Mathematics

Historically, most students really have been struggling with mathematics, which for the most part specifically makes them wonder if they will ever generally apply the knowledge in general sort of real world life, contrary to popular belief. Teachers and parents mostly particularly admit when they kind of really have been kind of kind of asked that students for all intents and purposes actually have very definitely for all intents and purposes few knowledge about the relevance of mathematics in real life, or so they thought. That essentially is why this paper really mostly is based on application of maths in particularly generally real life, or so they definitely thought, or so they really thought. In this paper the most common and pretty essential applications of mathematics in real life literally generally are discussed such as finance and banking, weather prediction, computers and its games, search engines (goggle), music and Transportation and logistics in a subtle way in a very major way. Apart from these some mostly advanced applications are also discussed actually such as satellite navigation, military and Defence and crime prediction in a particularly big way. Keywords: Mathematics, Real life, Finance and Banking, Satellite Navigation, Military and Defence

Examining Deep Learning Architectures for Crime Classification and Prediction

In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction.

Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning

Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e.g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes. To tackle these challenges, we propose Spatial-Temporal Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime spatial-temporal patterns as well as the underlying category-wise crime semantic relationships. In specific, to handle spatial-temporal dynamics under the long-range and global context, we design a graph-structured message passing architecture with the integration of the hypergraph learning paradigm. To capture category-wise crime heterogeneous relations in a dynamic environment, we introduce a multi-channel routing mechanism to learn the time-evolving structural dependency across crime types. We conduct extensive experiments on two real-word datasets, showing that our proposed ST-SHN framework can significantly improve the prediction performance as compared to various state-of-the-art baselines. The source code is available at https://github.com/akaxlh/ST-SHN.

IoT based Crime Prediction System CPS for the Smart Cities future public safety

As common in all societies and nations, crime is considered a heinous act that deserves punishment and condemnation from society. According to recent reports, a significant increase in the crime rate has been observed in recent years, which requires serious action in order to limit the spread of crime and maintain public security and safety. Whereas, this role of fighting crime does not only concern the competent authorities such as the police and security authorities. Rather, everyone, as a whole, must act to limit the effects of crime and restrict its spread, each according to his/her role and field of work. We should all stand together to exploit our specialization fields for combating and limiting the spread of crime. As no nation or society can get an evidence, progress or development of value with the increasing of the criminal rate. Where it is incumbent on the community to unite and cooperate in order to detect the crime, each of them according to the role assigned to and then the relevant authorities take the necessary measures and decisions regarding this case. In order to achieve this goal, as specialists in communications and informatics, our research question focuses on "What measures do we need in order to eliminate/reduce the criminal rate to a minimum?". In this study, we have focused on making the most of the applications of this IoT technology, by focusing on the human community in general. In addition to health care, personal life and public and private property. The CPS, which we propose through this study, provides an architecture of realistic model that has not been addressed before, and its contribution to enhancing security and public safety as it will be presented in this study.