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The objective of this paper is to perform clustering based on data consisting of both linear and circular variables, that is the data that lie on the surface of a cylinder. There are many circular–linear distributions available in the literature. We use the pragmatic approach of specifying the conditional rather than the marginal, which is often easier. Adopting Arnold et al. (Lecture Notes in Statistics: Conditionally Specified Distributions, Springer Verlag Publisher, Berlin Heidelberg, 1992), we provide the conditional distribution of θ given x and that of x given θ. Here, a mixture model approach based on the joint distribution of the linear and the circular variable is proposed. In particular, two types of such mixture models are used. One is based on the joint distribution of the marginal distribution of the linear variable and the conditional distribution of the circular variable given the linear variable and the other vice versa. Convergence property of Expectation Maximization (EM) algorithm for the members of the curved exponential family used for our models is studied. A real-life application on meteorological data is made of the proposed approaches. Comparison of the two models is done based on this example. The distinctive and important feature of preserving the geometry of the cylindrical manifold by our clustering method and its marked deviation from that for data on \(\Re ^p\) is also revealed by this example.

In this manuscript, a deep learning based approach has been investigated for land-cover classification capable of handling situations where there is an imbalance in the class-wise samples in the training set under a novel semi-supervised learning framework. This problem is persistent when the presence of some of the major land-cover classes affects the other recessive classes in a particular region and due to this the generated training set contains a fewer number of samples from the latter group of classes. Here, an adversarial auto-encoder has been used to generate synthetic samples from each of the minority classes so that each of the classes is well represented in the training set. A new training set has then been designed by including the ‘most confident’ artificial samples from the minority classes. This newly formed balanced training set is then shown to be more effective to classify the test samples as compared to the initially available imbalanced training set. The validation of the proposed approach has been performed using patterns collected from two multi-spectral satellite images captured by Ikonos-2 and landsat-8 satellites over different regions of India. The results show significant improvement in test class prediction when compared to that of other state-of-the-art imbalance handling schemes in land-cover classification.

Advancements in wireless communication and availability of miniaturized, battery powered micro electronics devices have revolutionized the trend of computation and communication activities to the generation of smart computing where spatially distributed autonomous devices with sensors forming wireless sensor network (WSN) are utilized to measure physical or environmental conditions. WSNs have emerged as one of the most interesting areas of research due to its diverse application areas such as healthcare, utilities, remote monitoring, smart cities, and smart home which not only perform effective monitoring but also improve quality of living. Even the sensor nodes can be strategically placed in, on, or around human body to measure vital physiological parameters as well. Such sensor network which is formed over human body is termed as wireless body area network (WBAN) which could be beneficial for numerous applications such as eldercare, detection of chronic diseases, sports, and military. Hence, both network applications deal with sensitive data which requires utmost security and privacy. Thus, the security and privacy issues and challenges related to WSN and WBAN along with the defense measures in place should be studied in detail which not only is beneficial for effective application but also will motivate the researcher to find their own path for exercising better protection/defense. Accordingly, in this chapter a brief overview of both networks is presented along with their inherent characteristics, and the need for security and privacy in either networks is illustrated as well. Besides, study has been made regarding potential threats to security and privacy in both networks and existing measures to handle these issues. Finally the open research challenges are identified to draw the attention of the researcher to investigate further in this field.

With the evolution of wireless communication and advent of low power, miniaturized, intelligent computing devices, sensor network technology initiates the era of Wireless Body Area Network (WBAN) for medical applications. This new trend of healthcare empowers continuous supervision of vital physiological parameters under free living conditions. However, the potency of WBAN applications are subject to reliable data delivery. Inherent challenges of WBAN such as scarce energy resource, varying link quality, propensity of tissue damage necessitate optimal routing strategy to combat with hostilities. In addition, coexistence of multiple WBANs within proximity results in severe degradation of throughput as well. In this paper, a cost-based energy efficient routing protocol has been designed which ensures satisfactory performance without fostering thermal effect and adapts itself in adverse situations like intra BAN as well as inter BAN interference. The performance of the proposed algorithm is analyzed through comprehensive simulations. The protocol is analyzed for different mobility models signifying relative body movement due to posture change. The simulation results demonstrate that our proposed protocol out performs other protocols with respect to energy efficiency while maintaining a stable packet delivery ratio under interference.

Online Social Networks (OSN), such as Facebook, Twitter, Youtube and so on, are important sources of online content today. These platforms are used by millions of people world-wide, to share information and express their sentiment and opinion on various social issues. Sentiment analysis of online content – automatically inferring whether a particular textual content reflects a positive (e.g., happy) or negative (e.g., sad) sentiment of the person who posted the content – is an important research problem today, and has several potential applications such as analysing public opinion on various products or social issues. In this paper, we propose a simple but effective methodology of inferring the sentiment of textual content posted in online social media. Our approach is based on first identifying the positive / negative polarity of terms, i.e., whether a certain term (e.g., a word) is normally used in a positive or negative context, and then to infer the sentiment of a given text based on the polarity of the terms present in the text. A key challenge in this approach is that in online social media, different users use different words while expressing similar opinion. To address this, we use the well-known lexical database WordNet to identify groups of words which are synonymous to each other. We apply our proposed methodology on a large publicly available dataset containing content from six different online social media, which has been labeled as positive / negative by human annotators, and find that our methodology achieves better performance than several approaches developed earlier.

The Twitter microblogging site is one of the most popular websites in the Web today, where millions of users post real-time messages (tweets) on different topics of their interest. The content that becomes popular in Twitter (i.e., discussed by a large number of users) on a certain day can be used for a variety of purposes, including recommendation of popular content and marketing and advertisement campaigns. In this scenario, it would be of great interest to be able to predict what content will become popular topics of discussion in Twitter in the recent future. This problem is very challenging due to the inherent dynamicity in the Twitter system, where topics can become hugely popular within short intervals of time. The Twitter site periodically declares a set of trending topics, which are the keywords (e.g., hashtags) that are at the center of discussion in the Twitter network at a given point of time. However, the exact algorithm that Twitter uses to identify the trending topics at a certain time is not known publicly. In this paper, we aim to predict the keywords (hashtags) that are likely to become trending in Twitter in the recent future. We model this prediction task as a machine learning classification problem, and analyze millions of tweets from the Twitter stream to identify features for distinguishing between trending hashtags and non-trending ones. We train classifiers on features measured over one day, and use the classifiers to distinguish between trending and non-trending hashtags on the next day. The classifiers achieve very high precision and reasonably high recall in identifying the hashtags that are likely to become trending.