In this research work, we propose a method for human action recognition based on the combination of structural and temporal features. The pose sequence in the video is considered to identify the action type. The structural variation features are obtained by detecting the angle made between the joints during the action, where the angle binning is performed using multiple thresholds. The displacement vector of joint locations is used to compute the temporal features. The structural variation features and the temporal variation features are fused using a neural network to perform action classification. We conducted the experiments on different categories of datasets, namely, KTH, UTKinect, and MSR Action3D datasets. The experimental results exhibit the superiority of the proposed method over some of the existing state-of-the-art techniques.

This paper proposes a system of part of speech tagging for the South Indian language Kannada using supervised machine learning. POS tagging is an important step in Natural Language Processing and has varied applications such as word sense disambiguation, natural language understanding etc. Based on extensive research into methods used for POS tagging, Conditional Random fields have been chosen as our algorithm. CRFs are used for sequence modeling in POS tagging, named entity recognition and as an alternative to Hidden Markov Models. Three very large corpora are used and their results are compared. The feature sets for all three corpora are also varied. The best method for the task is determined using these results.

In current scenario, all the manufacturing industries are placing constant efforts for their endurance in the current global volatile economy. Manufacturing industries are annoying to implement new and professional techniques in their regular production processes. Some of the recognized tools are applied and their awareness has been growing among the industries, especially in production sector. Last two decades have witnessed an explosion in the area of quality and productivity improvement initiatives in the manufacturing industries by different tools and techniques such as lean manufacturing, total quality management, total productive maintenance, six sigma implementation etc. The objective of this study is to enlighten the importance of lean techniques implementation in a medium scale belt manufacturing industry. This research study helps to exhibit the existing hidden potential in the selected industry as well as a selection of appropriate techniques for productivity improvements. Also, it aims to eradicate wastes and non-value added activities at every stage in order to enhance the overall productivity. From the results after implementation of appropriate lean techniques it was found that, the lead time was reduced about 1256 minutes and the overall production was increased by about 9%.

No abstract

No abstract