The infants admitted in the Neonatal Intensive Care Unit (NICU) always need a Hygienic environment and round the clock observations. Infants or the just born babies always express their physical and emotional needs through cry. Thus, the detection of the reasons behind the infant cry plays a vital role in monitoring the health of the babies in the NICU. In this paper, we have proposed a novel approach for detecting the reasons for Infant's cry. In the proposed approach the cry signal of the infant is captured and from this signal, the unique set of features are extracted using MFCCs, LPCCs, and Pitch. This set of features is used to differentiates the patters signals to recognize the reasons for the cry. The reasons for cry such as hunger, pain, sleep, and discomfort are used to represent different classes. The Neural Network Multilayer classifier is designed to recognize the reasons for the cry using the standard dataset of infant cry. The proposed classifier can achieve accuracy of 93.24% from the combined features of MFCCs, LPCCs and Pitch using

Cluster analysis is a data mining approach for unsupervised learning. However, the use of clustering as a data mining tool has been a cause of growing concern as the use of this technology is violating individual privacy. This paper presents a method for privacy preserving clustering through cluster bulging. In this method, the objects of the database are first aligned into clusters based on a similarity measure. The data in these clusters is perturbed in a controlled manner by modifying the values of various objects, so that, in the perturbed data set, the clusters are bulged in comparison to those in the original data set. In order to perform this perturbation, every cluster is displaced along the line joining its centroid to the centroid of the whole data set. And, then, every object in each cluster is shifted along the line joining that object to the centroid of the cluster. The word bulging used here refers to both positive and negative bulging. The method in essence manipulates the similarity measures and recomputes the new perturbed objects of the respective clusters. Thus, every object in the bulged cluster represents its corresponding object from the original cluster. After the application of this method, the objects get perturbed, while the number of member objects and shape of each cluster remain the same as those of the original clusters, thereby the information in the two instances of the data sets is sustained, while, the privacy of sensitive data is preserved.

The Internet of Things (IoT) is one of the most rapidly emerging technologies. It is observed that while many devices/machines get connected in an application, it is a challenge for the IoT application designer to keep the application scalable. Scalability is the ability of a device/application to adapt to the changes in the environment and meet the changing needs in the future. The paper presents a layered IoT architecture and discusses issues related to the scalability of each layer. The best open-source technologies are explored. A novel system architecture of a scalable IoT framework is conceptualized in this paper. An application covering vehicle accident reporting is designed with the proposed framework. The application is tested in real-time using the standalone hardware and its ability to report the incidents is confirmed. The scalability metrics of the proposed framework are evaluated and the results are reported.