• • Part of the book series (LNCS, volume 10752) Abstract Our world is becoming more interconnected and intelligent, huge amount of data has been generated newly. Home appliances’ energy usage is the basis of home energy management and highly depends on weather condition and environment. Using weather in context, it is theorized that usage of home energy would be higher in cold days. Hp Laserjet 1160 Driver For Windows 7 32 Bit Free Download. Time series and contextual data collected from sensors can be monitored and controlled in home appliances network. The aim of this work is to propose a deep neural network architecture and apply it to a contextual and multivariate time series data.

Introduction Jacek M. The field of artificial neural networks. CASSIS lists 262 neural network patents since 1969. Figure 2 shows nearly all of. Prediction of Disease Level Using Multilayer Perceptron of Artificial Neural Network for Patient Monitoring. Development of appropriate project management factors for the construction industry in Kenya. International Journal of Soft Computing and Engineering (IJSCE), ISSN:2231-2307,Vol 4,Issue 1. Zurada, J.M., Introduction to Artificial.

Long short-term memory (LSTM) models are powerful neural networks based on past behaviours in long sequences. LSTM networks have been demonstrated to be particularly useful for learning sequences containing longer-term patterns of unknown length, due to their ability to maintain long-term memory. In this work, we incorporate contextual features into the LSTM model because of ability of keeping context of data for a long-time, and for analysing it we integrated two different datasets; the first dataset contains measurements about house temperature and humidity measured over a period of 4.5 months by a 10 min intervals using a ZigBee wireless sensor network. Canon India Software Development Center.

The second dataset contains measurements about individual household electric power consumptions gathered over a period of 47 months. From the wireless network, the data from the kitchen, laundry and living room were ranked the highest in importance for the energy prediction. Li, D., Park, H.W., Ishag, M.I.M., Batbaatar, E., Ryu, K.H.: Design and partial implementation of health care system for disease detection and behavior analysis by using DM techniques. In: 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. IEEE, August 2016 Copyright information.