Gadgets installed in smart home ease human life and can be controlled from remote locations. These gadgets are sensor-based and generate different types of data. These data need to be processed faster to provide a quick response in a smart home application. Edge nodes dwell at the edges of IoT gadgets for faster processing of data. In this paper, we have used a data classifier strategy to overcome a few existing issues in smart home applications. Certain rules are derived based on which the proposed classifier classify the data. Then, data are forwarded to edge nodes for processing. The performance of a proposed data classifier is studied using different parameters. From the simulation study, it is found that the proposed strategy gives better results in terms of average execution time, service latency and resource utilization.
A major part of the Indian economy relies on agriculture, thus identification of any diseased crop in the initial phase is very important as these diseases cause a significant drop in agricultural production and also affect the economy of the country. Tomato crops are susceptible to various diseases which may be caused due to transmission of diseases through Air or Soil. We have tried to automate the procedure of detection of diseases in the Tomato Plant by studying several attributes related to the leaf of the plant. Using various machine learning algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), ResNet, and InceptionV3 we have trained the model, and based on the results obtained we have evaluated and compared the performance of these algorithms on different features set. For the dataset we had 10 classes (healthy and other unhealthy classes) having a total of 18,450 images for the training of the models. After implementing all of the algorithms and comparing their results we found that the ResNet was most appropriate for extracting distinct attributes from images. The trained models can be used to detect diseases in Tomato Plant timely and automatically.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.