An Automated System to Mitigate Loss of Life at Unmanned Level Crossings

AbstractEvery life is precious and is worth saving. This paper proposes the design and implementation of a system to mitigate the loss of life at unmanned railway level crossings. This system uses the advancements in Communication, Embedded Systems and Internet of Things to develop a real-time, early warning system for unmanned level crossings across India. The outcome of this work is to provide an audio-visual indication to the commuter warning about an approaching train. The need for such systems and its design implementation and feasibility is discussed in this paper

Bayesian nonparametric Multiple Instance Regression

Multiple Instance Regression jointly models a set of instances and its corresponding real-valued output. We present a novel multiple instance regression model that infers a subset of instances in each bag that best describes the bag label and uses them to learn a predictive model in a unified framework. We assume that instances in each bag are drawn from a mixture distribution and thus naturally form groups, and instances from one of this group explain the bag label. The largest cluster is assumed to be correlated with the label. We evaluate this model on the crop yield prediction and aerosol depth prediction problems. The predictive accuracy of our model is better than the state of the art MIR methods