CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing

We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech based label annotations. Notice that these multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the growing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network based architecture which is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multi-spectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases

Appointment scheduling model in healthcare using clustering algorithms

In this study we provided a scheduling procedure which is combination of machine learning and mathematical programming. Outpatients who request for appointment in healthcare facilities have different priorities. Determining the priority of outpatients and allocating the capacity based on the priority classes are important concepts that have to be considered in scheduling of outpatients. Two stages are defined for scheduling an incoming patient. In the first stage, We applied and compared different clustering methods such as k-mean clustering and agglomerative hierarchical clustering methods to classify outpatients into priority classes and suggested the best pattern to cluster the outpatients. In the second stage, we modeled the scheduling problem as a Markov Decision Process (MDP) problem that aims to decrease waiting time of higher priority outpatients. Due to the curse of dimensionality, we used fluid approximation method to estimate the optimal solution of the MDP. We applied our methodology on a dataset of Shaheed Rajaei Medical and Research Center in Iran, and we showed how our models work in prioritizing and scheduling of outpatients