Titlebook: Advances in Analytics and Applications; Arnab Kumar Laha Book 2019 Springer Nature Singapore Pte Ltd. 2019 Business Analytics.Big Data.Str

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https://doi.org/10.1007/978-1-4612-3736-5 separate models were built for each segment. We further applied various data mining techniques such as logistic regression, random forest and gradient boosting method to build the model for each segment. Gradient boosting method outperformed both logistic regression and random forest on measures of

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O. A. Ladyzhenskaya,N. N. Ural’tseva are calculated to evaluate best model and check robustness of the models under study. Training and validation of the model is done using datasets collected from a manufacturing unit located at Pithampur industrial area near Indore, Madhya Pradesh, India. In the current paper an association is also

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https://doi.org/10.1007/978-3-319-70114-1 X-rays and 338 chest X-rays with tuberculosis manifestation. The validation dataset contained 235 images, which observed a sensitivity of 84.91% and a specificity of 93.02%. This demonstrates the potential of convolutional neural networks to automatically classify chest X-rays in real time.

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Xiaochun Liu,Bert-Wolfgang Schulzes, viz., candidate profiles, the Job Descriptions (JDs), and TA process task outcomes, are captured in the eHRM systems. The authors present a set of critical functional components built for improving efficiency and effectiveness in recruitment process. Through multiple real-life case studies conduc

Recessive

Jussi Behrndt,Seppo Hassi,Henk de Snool score. We then went a step further to cluster employers on the basis of their ability to choose the stars from among a large pool of candidates, to identify employers who can be advised to further optimize their recruitment spend.

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Linear Regression for Predictive Analyticse) to reduce vulnerabilities in your application code.Assess potential threats from event triggers in your serverless functions.Understand security best practices in serverless computing.Develop an agnostic sec978-1-4842-6099-9978-1-4842-6100-2