This paper presents an approach for automation of interpretable feature selection for Internet Of Things Analytics (IoTA) using machine learning (ML) techniques. Authors have conducted a survey over di erent people involved in di erent IoTA based application development tasks. The survey reveals that feature selection is the most time consuming and niche skill demanding part of the entire work ow. This paper shows how feature selection is successfully automated without sacri cing the decision making accuracy and thereby reducing the project completion time and cost of hiring expensive resources. Several pattern recognition principles and state of art (SoA) ML techniques are followed to design the overall approach for the proposed automation. Three data sets are considered to establish the proof-of-concept. Experimental results show that the proposed automation is able to reduce the time for feature selection to 2 days instead of 4 − 6 months which would have been required in absence of the automation. This reduction in time is achieved without any sacri ce in the accuracy of the decision making process. Proposed method is also compared against Multi Layer Perceptron (MLP) model as most of the state of the art works on IoTA uses MLP based Deep Learning. Moreover the feature selection method is compared against SoA feature reduction technique namely Principal Component Analysis (PCA) and its variants. The results obtained show that the proposed method is e ective.

Analysis of heart sounds is a popular research area for non invasive identification of several heart diseases. This paper proposes a set of 88 time-frequency features along with five different methodologies for classifying normal and abnormal heart sounds. State of the art approach was applied for segregating the fundamental heart sounds. Apart from a baseline two class classifier, separate classifiers for long and short heart sounds were also explored in order to get rid of the dependency of features on the duration of the recordings. Finally, a three class classifier was explored to deal with the noisy data present in the dataset. Both balanced and unbalanced sets were considered for crating of the training models. A comparative analysis showed that, out of all the methodologies, the three class classifier based approach produces the most optimum performance by simultaneously yielding high values of both sensitivity and specificity.

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In the existing ride sharing scenario, the ride taker has to cope with uncertainties since the ride giver may be delayed or may not show up due to some exigencies. A solution to this problem is discussed in this paper. The solution framework is based on gathering information from multiple streams such as traffic status on the ride giver's routes and the ride giver's GPS coordinates. Also, it maintains a list of alternative ride givers so as to almost guarantee a ride for the ride taker. This solution uses a SPARQLbased continuous query framework that is capable of sensing fastchanging real-time situation. It also has reasoning capabilities for handling ride taker's preferences. The paper introduces the concept of user-managed windows that is shown to be required for this solution. Finally we show that the performance of the application is enhanced by designing the application with short incremental queries.

With governments and administrations releasing open linked data, and with the gradual rise of sensor deployments across the world, semantic queries on the combined sensor and linked data has become a need to provide several intelligent smart city services and applications. The data is represented in form of triples (RDF), concepts and relations in form of ontologies (OWL) and the corresponding query language is SPARQL as per standards of Semantic Web. In this paper, a system for sensor exploration is presented, which takes a set of keywords, context, data, learned and background knowledge as input and produces the intentioned result as output. The system tries to keep the underlying semantic web technologies transparent to the end user. The relevant challenges and the scope of future work is also discussed.