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The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring.

As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created.

Although the achieved prediction results were acceptable, there is need for more work to apply and test these results in real environment.

Artificial neural network (ANN) was implemented as the predictive model while extreme learning machine (ELM) and back propagation (BP) learning schemes were used as training algorithms for the ANN. BP learning algorithm minimizes the error function by using the gradient descent method. ELM training algorithm is based on selecting of the input weights randomly of the ANN network and the output weight of the network are determined analytically.

The purpose of this paper is to identify the information and communication technology management enablers (ICTMEs) and establish the hierarchical relationship among them using interpretive structural modelling (ISM) and analyse their driving and dependence power, using integrated ISM fuzzy-MICMAC analyses.

For identifying the ICTMEs, along with extensive literature review a large number of academicians and practitioners of repute are consulted. The contextual relationships between ICTMEs are established with the help of a well-established ISM methodology and further ICTMEs are analysed on the basis of their driving and dependence power and classified them into four different clusters by using fuzzy-MICMAC.

This paper has identified 25 key ICTMEs related to human resource, organization culture, technology, strategic planning, ICTM practices and organizational performance measurement and created a diagraph representing hierarchical relationship among them. Further these enablers are analysed and classified into four clusters on the basis of their driving and dependence power.

The developed relational model is based on the inputs of academicians and practitioners and any biasing from the person judging the ICTM enablers might influence the power of this model.

Top management of the organization could formulate and execute their strategies keeping in mind these identified critical enablers and relationship among them which will finally result into higher performance of ICTM.

This is the first kind of study which has identified 25 key enablers of ICTM, established hierarchical relationship among them and analysed them on the basis of their driving and dependence power using integrative ISM fuzzy-MICMAC analysis.