Most commonly used channel for communication among peoples is emails. In this era where everyone is so busy in their routine and work, it is very difficult to check all email when one receives huge amount of emails. Previous research has done work on email categorization in which they have mostly done spam filtration. The problem with spam filtration is that sometimes person mistakenly mark an important email received from high authority as spam and according to previous research, this email will be filtered as spam that can cause a great threat for job of an employee. In this research, we are introducing a methodology which classifies email text into three categories i.e. order, request and general on basis of imperative sentences. This research use Word2Wec for words conversion into vector and use two approaches of deep learning i.e. Convolutional neural network and Recurrent neural network for email classification. We conduct experiment on Dataset collected from Personal Gmail account and Enron which consists of 1000 emails. The experiment result show that RNN gives better accuracy than CNN. We also compare our methods with previously used method Fuzzy ANN results and Our proposed methods CNN and RNN gives better results than Fuzzy ANN. This research has also included different experimental result in which CNN and RNN applied on different ratios of training and testing dataset. These experiment show that increasing in the ratio of training dataset results in increasing accuracy of algorithm.
Direct Energy Deposition (DED) is a technique used to fabricate metallic parts and is a subcategory of metal additive manufacturing. Despite of its vast advantages over traditional manufacturing the deployment at industrial level is still limited due to underlaying concerns of process stability and repeatability. In-situ monitoring, therefore, is indispensable while depositing via DED. The present experiment is a step towards enhancing our current understanding of the DED when coupled with a closed loop control system to control melt pool width for deposition of thin-walled structures, and as a function of scan strategy. 316L stainless steel powder was deposited on S235JR substrate. A total of 6 iterations are reported, out of many performed, of which 3 were without the closed loop control. Also, to understand the effect of scan strategy as a function of laser power. Two different scan strategies were employed for understanding of the issue i.e., unidirectional, and bidirectional. Apart from the geometrical consistency of the wall, microhardness, density calculations and microstructure were investigated. The geometric consistency was found to be almost perfect with the bidirectional scan strategy. In case of unidirectional scan strategy, the wall shows a negative slope along the other extreme regardless of the closed loop control system. Dilution zone shows the hardness greater than both the substrate and the wall. The specimens fabricated without the use of closed loop control were found to be denser than their counterparts. This was found to be true also in case of manual reduction of power during each layer.
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