Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

Clustering Lab value working with medical data

Data mining is a relatively new field of research that its objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available [27]. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this thesis is to evaluate data mining tools in medical and health care applications to develop a tool that can help make rather accurate decisions. In this thesis, the goal is finding a pattern among patients who got pneumonia by clustering of lab data values which have been recorded every day. By this pattern we can generalize it to the patients who did not have been diagnosed by this disease whose lab values shows the same trend as pneumonia patients does. There are 10 tables which have been extracted from a big data base of a hospital in Jena for my work .In ICU (intensive care unit), COPRA system which is a patient management system has been used. All the tables and data stored in German Language database.

Baking Dalarna's biking cake : Collaboration as a means for destination competitiveness, a case study of “Biking Dalarna"

In the contemporary tourism industry, the competitive game is between destinations. Tourism operations struggle to remain competitive on the international market and their success depends to a large extent on other complementary and competing tourism organizations at the destination. It is the sum of the total tourism offerings at the destination which determines its attractiveness. This research explores tourism collaboration process as a means of generating destination competitiveness. The focus of the research is on the enhancing factors which contribute to the success of the collaboration and to the development of quality tourism products. The research studies the case of Biking Dalarna, a collaboration of different organizations at five biking destinations in Dalarna, Sweden. Its purpose is to develop biking tourism in the region and to make Dalarna into Sweden’s leading biking destination. It is a qualitative research; the empirical data was collected through in depth interviews with representatives of six Biking Dalarna member organizations. The qualitative data collected from the participants provides inside look into the members reflections and experience of collaborating. The findings of this research demonstrate how collaboration has improved the biking product in Dalarna and promoted solutions to development problems. The research finds the good relationship between the collaborating actors and the involvement and leadership of the regional tourism management organization as the most contributing factors to the success of Biking Dalarna. The research also suggests that a third desired outcome of collaboration, improved marketing attributes was yet to be achieved in the case of Biking Dalarna.