Data science in public mental health : a new analytic framework

Understanding public mental health issues and finding solutions can be complex and requires advanced techniques, compared to conventional data analysis projects. It is important to have a comprehensive project management process to ensure that project associates are competent and have enough knowledge to implement the process. Therefore, this paper presents a new framework that mental health professionals can use to solve challenges they face. Although a large number of research papers have been published on public mental health, few have addressed the use of data science in public mental health. Recently, Data Science has changed the way we manage, analyze and leverage data in healthcare industry. Data science projects differ from conventional data analysis, primarily because of the scientific approach used during data science projects. One of the motives for introducing a new framework is to motivate healthcare professionals to use "Data Science" to address the challenges of mental health. Having a good data analysis framework and clear guidelines for a comprehensive analysis is always a plus point. It also helps to predict the time and resources needed in the early in the process to get a clear idea of the problem to be solved

Beaware!—situation awareness, the ontology-driven way.

Abstract Information overload is a severe problem for human operators of large-scale control systems as, for example, encountered in the domain of road traffic management. Operators of such systems are at risk to lack situation awareness, because existing systems focus on the mere presentation of the available information on graphical user interfaces-thus endangering the timely and correct identification, resolution, and prevention of critical situations. In recent years, ontologybased approaches to situation awareness featuring a semantically richer knowledge model have emerged. However, current approaches are either highly domain-specific or have, in case they are domain-independent, shortcomings regarding their reusability. In this paper, we present our experience gained from the development of BeAware!, a framework for ontology-driven information systems aiming at increasing an operator's situation awareness. In contrast to existing domain-independent approaches, BeAware!'s ontology introduces the concept of spatio-temporal primitive relations between observed real-world objects thereby improving the reusability of the framework. To show its applicability, a prototype of BeAware! has been implemented in the domain of road traffic management. An overview of this prototype and lessons learned for the development of ontology-driven information systems complete our contribution

Towards forecasting and prediction of faults in electricity distribution network : a novel data mining & machine learning approach

The electricity supply system includes a large-scale power generation installation and a convoluted network of electrical circuits that work together to efficiently and reliably supply electricity to consumers. Faults in the electricity distribution network have a direct effect on its stability, availability and maintenance. Consequently, quick elimination, prevention and avoidance of faults and the causes that generated them, is of special interest . The possible opportunity to both analyse the distribution of faults and predict future failures that may arise can significantly help electricity distribution operators who are accountable for the detection and repair of such problems. Such information is also crucial for any future planning and design of electricity distribution networks as it would significantly help to prevent problematic areas or and identify any additional measures necessary for the protection of underground and overground cables and equipment. The derived information would also be very useful to avoid any potential penalties associated with future network faults imposed by the regulators.Any network component faults result in an outage of power not only in the area fed by them but also in the neighbouring area. Fault prediction in distribution systems has always been of immense importance to utilities to ensure reliable power supply. This research aim is to develop data mining, and machine learning models to accurately predict and forecast Electricity Distribution Network Faults. The specific research objectives are to gain a deeper understanding of Electricity Distribution Network faults and to accurately predict network faults using the National Fault and Interruption Reporting Scheme (NAFIRS) database. Furthermore, this research not only proposes solutions but also provides an in-depth discussion of the associated technical, data gathering and data processing challenges.This research employed multiple case research design, as this allows more opportunities for multiple experiments and cross observation . This research has proposed a new method that analyses historical fault data and seeks to understand the impact of faults with other factors such as the Main Equipment Involved, Component and Direct Cause. This proposed data mining model may be used to safeguard the electrical power distribution system’s key equipment which can be severely damaged by some upcoming faults. The author of this thesis has proposed a new fault segmentation framework which distributed network operators can use to perform fault segmentation. This approach gives the option of performing multidimensional segmentation using various fault characteristics such as a number of faults, a number of minutes lost, and a number of customers affected. Multidimensional segmentation is a powerful conceptual model for the analysis of large and complex datasets.This study provides an in-depth discussion of equipment failure related network faults and compares the performance of a range of forecasting methods with a variety of accuracy measures. The study also provides an in-depth analysis of visual data mining concepts and discusses how using 2D and 3D calendar heat map methods can help provide a relatively new perspective in evaluating temporal patterns in electricity distribution network faults.Finally, the research discusses how external factors, such as local population density, affects electricity distribution network faults. Various classification algorithms were used to build prediction models. Those models were validated and compared for accuracy. The author has also sought to accurately understand the behaviour of Customer Minutes Lost (CML) performance indicators and sought to predict the annual CML figure using other annual financial and network performance indicators such as a number of customers affected, Totex, and Network load.It is anticipated that the work presented within this thesis will to lead to several original contributions to the scientific community who are working with data mining, machine learning and electricity distribution networks

A distributed architecture for symbolic data fusion

This paper presents a distributed knowledge representation and data fusion system designed for highly integrated Ambient Intelligence applications. The architecture, based on the idea of an ecosystem of interacting artificial entities, is a framework for collaborating agents to perform an intelligent multi-sensor data fusion. In particular, we focus on the cognitive layers leading the overall data acquisition process. The approach has been thoroughly tested in simulation, and part of it has been already exploited in successful applications.