Auto-selection order of Markov chain for background sequences with chi-square test

Modeling non coding background sequences appropriately is important for the detection of regulatory elements from DNA sequences. Based on the chi square statistic test, some explanations about why to choose higher order Markov chain model and how to automatically select the proper order are given in this paper. The chi square test is first run on synthetic data sets to show that it can efficiently find the proper order of Markov chain. Using chi square test, distinct higher order context dependences inherent in ten sets of sequences of yeast S.cerevisiae from other literature have been found. So the Markov chain with higher order would be more suitable for modeling the non coding background sequences than an independent model.

A New Approach of Feature Selection for Text Categorization

This paper proposes a new approach of feature selection based on the independent measure between features for text categorization. A fundamental hypothesis that occurrence of the terms in documents is independent of each other, widely used in the probabilistic models for text categorization （TC）, is discussed. However, the basic hypothesis is incom plete for independence of feature set. From the view of feature selection, a new independent measure between features is designed, by which a feature selection algorithm is given to ob rain a feature subset. The selected subset is high in relevance with category and strong in independence between features, satisfies the basic hypothesis at maximum degree. Compared with other traditional feature selection method in TC （which is only taken into the relevance account）, the performance of feature subset selected by our method is prior to others with experiments on the benchmark dataset of 20 Newsgroups.

A Participatory Assessment of the Impact of Flooding in Some Communities in Lokoja, Kogi State, Nigeria

The frequency and consequences of extreme flood events have increased in recent times, having huge impact on the socio-economic well-being of nations with the most significant impact being felt at the community level. Flooding is the most common environmental hazard in Nigeria, particularly Lokoja, with the frequency, intensity, and extent likely to increase due to the effects of global warming leading to climate change such as sea level rise, more intensive precipitation levels, and higher river discharges. While destructive impacts of flood events continue to increase, flood managers in Nigeria have continued to implement a top-down approach towards mitigating these impacts, without involving affected communities in planning and implementation of mitigation strategies. This study therefore employed a participatory approach to determine the causes and impact of flooding in the study area. Participatory research tools such as key informant interviews, focus group discussions, and questionnaire surveys using the purposive sampling method were deployed to elicit data on the perception of the communities about the causes and impact of flood events. Descriptive statistical analysis was performed to elucidate the major causes and areas of impact while qualitative analysis was carried out to corroborate the results and to make for a robust outcome. The Chi Square Test analysis was performed to empirically establish a relationship between the impacts and flooding. Results show that major causes of flooding are the release of water from dams (83% in Adankolo, 97% in Gadumo, and 100% in Ganaja), overflow of rivers, and heavy rainfall while flooding affects economic concerns, property and basic amenities. The Chi Square Test analysis determined empirically that a relationship exists between several areas of impact and flood occurrence. The research concludes that participatory flood research approach can provide flood managers and decision makers a bottom-up approach for effective and robust flood mitigation strategies.