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Knowledge Management in Indian Manufacturing Industry: Identification and Modeling of Enablers
Abstract
Knowledge Management (KM) has been identified as an important research area to be researched to draw various benefits to the organization and to improve the performance of manufacturing systems. Fourteen enablers have been selected from literature review and subsequent brainstorming sessions in which experts from academia and industry participated. An interpretive structural modeling has been utilized to model these enablers and the interactions among them. Four enablers have been found out at the top, whereas one enabler at bottom. MIC MAC analysis has resulted into: six enablers as driving; four enablers as dependent; also four enablers as linkage; and no enablers as autonomous enablers. The structural model and MIC MAC analysis may be helpful in understanding: these enablers; their interactions; and their interdependence towards managing the knowledge management implementation process effectively and efficiently. Clear understanding of relations among these enablers will help firms to effective management of these identified enablers and competently to draw advantage from knowledge management.
more �9ei�o���knowledge.The aim of this work is to create a MLPT, to predict Myocardial Infraction. After getting the patient information this MLPT, forecastthat the patient is caused by heart attack or not which is performed by using three Data mining techniques: Naïve Bayes, Decision tree and WAC (Weighted Associative Classifiers). Using the medical prognosis such as chest pain type, thalassic, slope etc., it can predict the probabilities of patients getting a heart disease in the future. The prediction is performed from extracting the patient’s diachronic data or data storage. The research is mainly developed to recover the hidden information from the database. The system has been implemented in JSP and checked using the datasets that is been collected from UCI machine learning repository.
Keywords
References
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