Randomized Ensemble Approach with ID3 Algorithm For the Prediction datasets with Imbalance Problem

Nowadays, it is significant to make accurate prediction model by handling imbalance problem. When the larger dataset has been used in the prediction model, that data should be classified into classes which gives ‘0’ and ‘1’ to indicate negative and positive results. While classifying this target value, the larger number of instances can reside in one class and the remaining lower number of instances can be stored in another class. Because of this unequal distribution of data, the machine can be biased and there is high possibility to give wrong predictions. An inaccurate Dataset leads to misprediction. Hence, the imbalanced prediction dataset has been taken. This paper gives a proper information on Randomized ensemble approach with ID3 classifier for the imbalanced prediction dataset

Closing the gap between existing large‐area imaging research and marine conservation needs

Emerging technology has immense potential to increase the scale and efficiency of marine conservation. One such technology is large-area imaging (LAI), which relies on structure-from-motion photogrammetry to create composite products, including 3-dimensional (3-D) environmental models, that are larger in spatial extent than the individual images used to create them. Use of LAI has become widespread in certain fields of marine science, primarily to measure the 3D structure of benthic ecosystems and track change over time. However, the use of LAI in the field of marine conservation appears limited. We conducted a review of the coral reef literature on the use of LAI to identify research themes and regional trends in applications of this technology. We also surveyed 135 coral reef scientists and conservation practitioners to determine community familiarity with LAI, evaluate barriers practitioners face in using LAI, and identify applications of LAI believed to be most exciting or relevant to coral conservation. Adoption of LAI was limited primarily to researchers at institutions based in advanced economies and was applied infrequently to conservation, although conservation practitioners and survey respondents from emerging economies indicated they expect to use LAI in the future. Our results revealed disconnect between current LAI research topics and conservation priorities identified by practitioners, highlighting the need for more diverse, conservation-relevant research using LAI. We provide recommendations for how early adopters of LAI (typically Global North scientists from well-resourced institutions) can facilitate access to this conservation technology. These recommendations include developing training resources, creating partnerships for data storage and analysis, publishing standard operating procedures for LAI workflows, standardizing methods, developing tools for efficient data extraction from LAI products, and conducting conservation-relevant research using LAI