Influencing operational policing strategy by predictive service analytics

Everyday there are growing pressures to ensure that services are delivered efficiently, with high levels of quality and with acceptability of regulatory standards. For the Police Force, their service requirement is to the public, with the police officer presence being the most visible product of this criminal justice provision. Using historical data from over 10 years of operation, this research demonstrates the benefits of using data mining methods for knowledge discovery in regards to the crime and incident related elements which impact on the Police Force service provision. In the UK, a Force operates over a designated region (macro-level), which is further subdivided into Beats (micro-level). This research also demonstrates differences between the outputs of micro-level and macro-level analytics, where the lower level analysis enables adaptation of the operational Policing strategy. The evidence base provided through the analysis supports decisions regarding further investigations into the capability of flexible neighbourhood policing practices; alongside wider operations i.e. optimal officer training times

Assessment of Performances of Spam Detection Capabilities of Nearest Mean Classifier and Gaussian Method

oai:ojs2.ijaset.org:article/1The potency of nearest mean classifiers and Gaussian in detecting SPAM has been investigated and the findings are presented in this paper. The outcomes take the form of probabilities of error traces and the duration of classification. Due to the difficulty of detecting SPAM, these automated techniques will save a great deal of resources and time necessary to manage messages in the inbox messages. Due to the difficulty of detecting SPAM, these automated techniques will save a great effort and expense of time required to manage email messages

Data Mining and Life Science: A Survey

As we are into the age of digital information, the problem of data overload emerges so worryingly ahead. Our ability to analyze and understand immense datasets wrap extreme behind our ability together and stores the data. But a new age group of computational techniques and tools is required to support the extraction of useful knowledge from the rapidly increasing volumes of data. These techniques and tools are the focus of emerging fields of Knowledge Discovery in Databases (KDD) and also called data mining. Data mining is highly noticeable in the fields like marketing, e-commerce or e-business or the fame of its use in KDD in other sectors or industries also. Among these sectors that are just discovering data mining are the fields of medicine and public health also. This research paper provides a survey of current technique of data mining/KDD for healthcare

Business Intelligence Dashboard Implementation on a Travel Agency in Jakarta

Information is growing at an alarming rate. As the development of information, organizations need to manage them and make them can be processed are growing as well. So this makes the problem to get the right information at the right place for the right people. And this fact is important for the company to be successful. This is what causes the Business Intelligence (BI) in the preferences of today\u27s technology. BI is a process from raw data to be read. BI solutions help transform raw data into actionable information that can help support business decision making. This can help companies develop new opportunities. By identifying new opportunities and implement effective strategies, it will result in a competitive market advantage and stable in the long term. In this study, analysis and visualization of large amounts of data from a travel agency in Jakarta to help make the right business decisions using BI tools

Evolving rules-based control

An approach to control non-linear objects based on evolving Rule-based (eR) models is presented in the paper. Fuzzy rules, representing the structure of the controller are generated based on data collected during the process of control using newly introduced technique for on-line identification of Takagi-Sugeno type of fuzzy rule-based models. Initially, the process is supposed to be controlled for few time steps by any other conventional type of controller (P, PID or a fuzzy one with a fixed structure determined off-line). Then, in on-line mode the output of the plant under control (including its dynamic) and the respective control signal applied has been memorised and stored. These data has been used to train in a non-iterative way the eR model representing the fuzzy controller, which aim is to control the plant at a given set point. The indirect adaptive control approach has been used in combination with the newly introduced on-line identification technique based on unsupervised learning of antecedent and consequent parts separately. This approach exploits the quasi-linear nature of Takagi-Sugeno models and builds-up the control rule-base structure and adapts it in on-line mode. The method is illustrated with an example from air-conditioning systems, though it has wider potential applications

Supervised Learning Using Instance-based Patterns

This paper introduces a new classification algorithm of the instance-based learning type. Training records are converted into patterns associated with a known class label, and stored permanently into a trie1-like tree structure along with other helpful information. Classifying new records is done selecting from the trie two best patterns as solutions hypotheses. Best pattern selection is done using standard distance metrics, a strength function and an exclusive values concept. Classification tests done on several data files have shown very accurate results