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Titlebook: Biologically Inspired Techniques in Many-Criteria Decision Making; International Confer Satchidananda Dehuri,Bhabani Shankar Prasad Mishra

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Machine Learning Models for Stock Prediction Using Real-Time Streaming Dataata has also been proven to be a perennial source of data analysis collected in real-time, which basically deals with the continuous flow of data carrying information from sources like websites, mobile phone applications, server logs, social websites, trading floors, etc. The classifying model made
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Ensemble Classification Approach for Cancer Prognosis and Predictionmore complex with large number of genes and small samples are available with noise and redundant information. Meaningful correlated information from dataset is the first and most important steps to be extracted for better diagnosis through artificial intelligence (AI). Accordingly, recent work for A
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Extractive Odia Text Summarization System: An OCR Based Approachly for the low-resource, morphologically complex languages the availability of summarization data set is rare and difficult to construct. In this work, we propose a novel technique to extract Odia text from the image files using optical character recognition (OCR) and summarize the obtained text usi
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Predicting Sensitivity of Local News Articles from Odia Dailies, sports, politics, editorial, readers view, sports etc. This paper purposes to categorize positive, negative and neutral local news and later on prediction of sensitivity from negative local news articles. The purpose of sensitivity analysis of negative local news articles is to set priority of act
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Operations on Picture Fuzzy Numbers and Their Application in Multi-criteria Group Decision Making Prons FST is not capable to play crucial rule. In such situations Picture fuzzy set (PFS) come into the picture which is the direct extension of FST and Intuitionistic fuzzy set (IFS). Although different studies on FST and IFS have been done including their algebraic structure, however these studies a
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Conference proceedings 2020ld problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers
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Darya Tarasowa,Christoph Lange,Sören Auerd from UCI repository for the said purpose. The performance is compared for both ANN and ANN with GWO. 93.38% classification accuracy is obtained by using ANN-GWO classifier. As compare to earlier work it is found that the proposed approach is much better in terms of accuracy.
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