Читать книгу Handbook on Intelligent Healthcare Analytics - Группа авторов - Страница 37

Devi T.1* and Ramachandran A.2

1Department of Computer Science & Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India

2Department of Computer Science & Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, India

Abstract

Analytics and analysis from a massive database using various approaches and techniques are experimented, and ongoing research brings its main focus toward the domain such as big data. Economic growth and technological growth combined with its data production are also highlighted in big data approaches. Data are analyzed from social media, online stock markets, healthcare data, etc., which can be collaborated with artificial intelligence by developing the automated learning algorithms and development in cloud computing as well. Data can be either discrete or continuous, which are independent of the various processes for understanding the decision-making that relies on knowledge engineering. The proposed work converges in transforming the observed sequential data analysis from weather forecasting dataset. These systems can perform the cognitive task in improving the performance along with integrity of data using the enhanced framework. The prediction of natural disasters is a challenge for customers accessing forecast data, since fluctuations in data occur frequently, which fail to update the localization, that are identified as sensor latitude and longitude that are updated as a sequence on regular intervals from various directions. These four hidden states are the features that differentiate the probability of distributions for calculating the best cognitive tasks. Improved Bayesian Hidden Markov Frameworks (IBHMFs) have been proposed to identify the exact flow of state and detect the high congestion, which leads to earthquakes, tremor, etc. As the data from the analysis are unsupervised and features are converted to discrete and sequential data (independent variables), IBHMF can utilize in increasing the performance and produce the accuracy results in state estimation.

Keywords: Artificial intelligence, big data, Improved Bayesian Hidden Markov Frameworks (IBHMF), hidden state, knowledge engineering, weather forecasting