Machine learning is a popular approach in the field of healthcare. Healthcare is an important industry that provides service to millions of people and as well as at the same time becoming top revenue earners in many countries. Machine learning in healthcare helps to analyze thousands of different data points and suggest outcomes, provide timely risk factors, optimize resource allocation. Machine learning is playing a critical role in patient care, billing processing to set the target to marketing and sales team, and medical records for patient monitoring and readmission, etc. Machine learning is allowing healthcare specialists to develop alternate staffing models, intellectual property management, and using the most effective way to capitalize on developed intellectual property assets. Machine learning approaches provide smart healthcare and reduce administrative and supply costs. Today healthcare industry is committed to deliver quality, value, and satisfactory outcomes.
Emotions, a complex state of feeling results in physical and psychological changes that influence human behavior. Thus, in order to extract the emotional key phrases from psychological texts, here, we have presented a phrase level emotion identification and classification system. The system takes pre-defined emotional statements of seven basic emotion classes (anger, disgust, fear, guilt, joy, sadness and shame) as input and extracts seven types of emotional trigrams. The trigrams were represented as Context Vectors. Between a pair of Context Vectors, an Affinity Score was calculated based on the law of gravitation with respect to different distance metrics (e.g., Chebyshev, Euclidean and Hamming). The words, Part-Of-Speech (POS) tags, TF-IDF scores, variance along with Affinity Score and ranked score of the vectors were employed as important features in a supervised classification framework after a rigorous analysis. The comparative results carried out for four different classifiers e.g., NaiveBayes, J48, Decision Tree and BayesNet show satisfactory performances.
The present paper describes the identification of prominent characters and their adjectives from Indian mythological epic, Mahabharata, written in English texts. However, in contrast to the traditional approaches of named entity identification, the present system extracts hidden attributes associated with each of the characters (e.g., character adjectives). We observed distinct phrase level linguistic patterns that hint the presence of characters in different text spans. Such six patterns were used in order to extract the characters. On the other hand, a distinguishing set of novel features (e.g., multi-word expression, nodes and paths of parse tree, immediate ancestors etc.) was employed. Further, the correlation of the features is also measured in order to identify the important features. Finally, we applied various machine learning algorithms (e.g., Naive Bayes, KNN, Logistic Regression, Decision Tree, Random Forest etc.) along with deep learning to classify the patterns as characters or noncharacters in order to achieve decent accuracy. Evaluation shows that phrase level linguistic patterns as well as the adopted features are highly active in capturing characters and their adjectives.
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