Ensemble-based extreme learning machine model for occupancy detection with ambient attributes

Context-aware computing is a growing research domain in present circumstances due to technological advancements in the area of sensors technology, big data, artificial intelligence and robotics and automation. It has many applications for making the daily life of human beings sustainable, comfortable, and smooth. Context ware computing also includes ambient intelligence and applications such as occupancy detection, prediction, user recognition etc. Occupancy detection and recognition help in developing intelligent applications which help the energy management, intelligent decision making, that results in cost reduction and fault and failure prevention of services and products in advance. Several studies have been conducted to detect the occupancy with a different set of methodologies and approaches using varying types of data such as environmental parameters, image and video-based attributes, wireless or sensor based parameters, and noise-based parameters. This paper proposes a reliable, more accurate and efficient model based on the statistical analysis of the sensor based data for occupancy detection. Detailed quantification of the relationship of the ambient attributes is presented and the ensemble model is developed based on machine learning technique extreme learning machine to achieve the significant level of improvement in accuracy, efficiency, generalization and reliability. In addition to this, the paper also proposes one online and adaptive model-based online sequential extreme learning machine to perform occupancy detection on real-time data when complete data is not available and learning is done with recent data points coming in the form of streams. Results are compared with existing work in the domain and it is observed that proposed model perform better in terms of efficiency and accuracy over existing literature work.