Use of smart home technology offers significant potential for everyday energy savings for each citizen. There are numerous schemes successfully implemented in western countries related to energy conservation. In Indian context, the issues encountered are that of diversity and consumer literacy about electricity consumption. In this paper, a novel approach is presented to address this issue on an interface designed to provide users with the real time energy consumption of appliance specific data. The proposed system uses wireless sensor networking for sensing and transmitting remotely monitored data gathered from an intelligent hardware system comprising of current sensors followed by a current calculating unit and a cloud server for communication and storage of data. XBee® transceivers, which are economical and reliable, are installed on electric appliances and a commonly available open source platform of Arduino micro-controller which calculates the current readings gathered by Hall Effect current sensor ACS712. The software system transfers the UID (Unique Identification) of each appliance to the cloud in order to uniquely identify each appliance. A user-friendly interface is developed to provide timely updates of the electricity consumption of the connected appliances. The system provides a real time analysis of the electricity consumed by individual appliances connected to the system.

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Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is utilized. These blood cell images are subjected to various data analyzing techniques that count and classify the different types of blood cells. Nowadays, deep learning-based methods are in practice to analyze the data. These methods are less time-consuming and require less sophisticated equipment. This paper implements a deep learning (D.L) model that uses the DenseNet121 model to classify the different types of white blood cells (WBC). The DenseNet121 model is optimized with the preprocessing techniques of normalization and data augmentation. This model yielded an accuracy of 98.84%, a precision of 99.33%, a sensitivity of 98.85%, and a specificity of 99.61%. The proposed model is simulated with four batch sizes (BS) along with the Adam optimizer and 10 epochs. It is concluded from the results that the DenseNet121 model has outperformed with batch size 8 as compared to other batch sizes. The dataset has been taken from the Kaggle having 12,444 images with the images of 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. With such results, these models could be utilized for developing clinically useful solutions that are able to detect WBC in blood cell images.

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