Apriori algorithm mines the data from the large scale data warehouse using association rule mining. In this paper a new algorithm named as Dynamic Apriori (D-Apriori) algorithm is presented. The proposed D-Apriori algorithm incorporates the dynamism in classical Apriori for efficiently mining the frequent itemsets from a large scale database. With the help of experimental results, it is shown that the D-Apriori algorithm performs better than the existing Apriori algorithm with respect to execution time for the dynamic behavior of data itemset.
For gauging the drowsiness, various algorithms have been proposed, but the maximum of approaches tries to gauge the facial expression and even change in the skin. In this paper, gauging the landmarks that have been discovered precisely enough to reliably predict the drowsiness was tried using the degree of eye-opening. The suggested algorithm within the paper finds that using the landmarks and eye aspect ratio (EAR) characterizes a person’s eye-opening in each frame. A simple mathematical expression i.e. Euclidean distance finds eye blinks being a blueprint for EAR. Thus, these values help us detect drowsiness. Once the detection happens, other problems arise as to how to send the data to a larger place, as a person may be critical and there is no internet at that place. Then messages cannot be received to a family member. To avoid this situation, nRF is used to facilitate multi-hop communication. Second, this paper focuses on the sleep pattern of an elderly, which is indeed an important aspect of gauging drowsiness. The lack of sleep is very common in the old person. Thus, this aspect helped us to accurately predict the physical condition of the elderly and indeed act as a piece of very useful information in restricting the fall of the elderly. So, for predicting the health of the elderly, we have applied the machine learning model and able to predict the condition of the elderly. The model on real-world scenarios was tested and the results are indeed accurate.
Abstract-Microdata-Information collected by different organizations is published for analysis to the analyst, decision makers, policy makers and researchers. Original data is not published due to some privacy issues. So, techniques are needed to preserve privacy of data. This paper includes the comparative study of various techniques available to preserve the privacy of published data.
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