In order to generate a report for an enterprise where there is neither the API supporting from their existing web.ite systems nor the granted datab.se access rights approval, a daily b.siness report generator system b...
In order to generate a report for an enterprise where there is neither the API supporting from their existing web.ite systems nor the granted datab.se access rights approval, a daily b.siness report generator system b.sed on web.scraping with k nearest neighb.r (kNN) classification algorithm is proposed in this paper. It covers the web.crawler technology that is to access existing web.ite system and extract b.siness data. The kNN algorithm is applied to identify the verification code on the login page, and the b.ief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the b.ief daily b.siness reports b. the kNN algorithm, which is b.tter than some lib.ary with default training set on validating the verification code.
b.gNeuron is an open community b.nch-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is r...
b.gNeuron is an open community b.nch-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data ob.ained in many neuroscience lab.ratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a sub.et of the availab.e imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enab.e generalizab.e b.nchmarking. Together with image quality features, we pooled the data in an interactive web.application that enab.es users and developers to perform principal component analysis, t-distrib.ted stochastic neighb.r emb.dding, correlation and clustering, visualization of imaging and tracing data, and b.nchmarking of automatic tracing algorithms in user-defined data sub.ets. The image quality metrics explain most of the variance in the data, followed b. neuromorphological features related to neuron size. We ob.erved that diverse algorithms can provide complementary information to ob.ain accurate results and developed a method to iteratively comb.ne methods and generate consensus reconstructions. The consensus trees ob.ained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
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