AnyNovel: detection of novel concepts in evolving data streams: An application for activity recognition

A data stream is a flow of unbounded data that arrives continuously at high speed. In a dynamic streaming environment, the data changes over the time while stream evolves. The evolving nature of data causes essentially the appearance of new concepts. This novel concept could be abnormal such as fraud, network intrusion, or a sudden fall. It could also be a new normal concept that the system has not seen/trained on before. In this paper we propose, develop, and evaluate a technique for concept evolution in evolving data streams. The novel approach continuously monitors the movement of the streaming data to detect any emerging changes. The technique is capable of detecting the emergence of any novel concepts whether they are normal or abnormal. It also applies a continuous and active learning for assimilating the detected concepts in real time. We evaluate our approach on activity recognition domain as an application of evolving data streams. The study of the novel technique on benchmarked datasets showed its efficiency in detecting new concepts and continuous adaptation with low computational cost

Impacts of organic and conventional crop management on diversity and activity of free-living nitrogen fixing bacteria and total bacteria are subsidiary to temporal effects

A three year field study (2007-2009) of the diversity and numbers of the total and metabolically active free-living diazotophic bacteria and total bacterial communities in organic and conventionally managed agricultural soil was conducted at the Nafferton Factorial Systems Comparison (NFSC) study, in northeast England. The result demonstrated that there was no consistent effect of either organic or conventional soil management across the three years on the diversity or quantity of either diazotrophic or total bacterial communities. However, ordination analyses carried out on data from each individual year showed that factors associated with the different fertility management measures including availability of nitrogen species, organic carbon and pH, did exert significant effects on the structure of both diazotrophic and total bacterial communities. It appeared that the dominant drivers of qualitative and quantitative changes in both communities were annual and seasonal effects. Moreover, regression analyses showed activity of both communities was significantly affected by soil temperature and climatic conditions. The diazotrophic community showed no significant change in diversity across the three years, however, the total bacterial community significantly increased in diversity year on year. Diversity was always greatest during March for both diazotrophic and total bacterial communities. Quantitative analyses using qPCR of each community indicated that metabolically active diazotrophs were highest in year 1 but the population significantly declined in year 2 before recovering somewhat in the final year. The total bacterial population in contrast increased significantly each year. Seasonal effects were less consistent in this quantitative study