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Social media is Internet-based by design, allowing people to share content quickly via electronic means. People can openly express their thoughts on social media sites such as Twitter, which can then be shared with other people. During the recent COVID-19 outbreak, public opinion analytics provided useful information for determining the best public health response. At the same time, the dissemination of misinformation, aided by social media and other digital platforms, has proven to be a greater threat to global public health than the virus itself, as the COVID-19 pandemic has shown. The public's feelings on social distancing can be discovered by analysing articulated messages from Twitter. The automated method of recognizing and classifying subjective information in text data is known as sentiment analysis. In this research work, we have proposed to use a combination of preprocessing approaches such as tokenization, filtering, stemming, and building N-gram models. Deep belief neural network (DBN) with pseudo labelling is used to classify the tweets. Top layers of the base classifiers are boosted in the pseudo labelling strategy, whereas lower levels of the base classifiers share weights for feature extraction. By introducing the pseudo boost mechanism, our suggested technique preserves the same time complexity as a DBN while achieving fast convergence to optimality. The pseudo labelling improves the performance of the classification. It extracts the keywords from the tweets with high precision. The results reveal that using the DBN classifier in conjunction with the bigram in the N-gram model outperformed other models by 90.3 percent. The proposed approach can also aid medical professionals and decision-makers in determining the best course of action for each location based on their views regarding the pandemic.

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Currently, model based development of web services is being actively researched for its usefulness in developing a system. Various techniques like EPC, petrinet, process algebra, CPN and UML have been proposed for web service modeling. FSM is found appropriate to model Business requirements because a business process on execution moves forward from one state to other. We have shown uses of FSM (Finite State Machine) based models in development of web services [1]; also have developed a tool HUMSAT to model service specification in FSM and to generate executable codes in BPEL and WSDL. However, automated code generation depends on correct specification of models as an improperly specified model leads to errors and bugs in resultant code. Hence model checking plays an important role in eliminating the flaws at design phase itself. In this paper, we propose a technique for model verification that identifies structural flaws e.g. unreachability, deadlock and temporal inconsistencies. Further, HUMSAT is augmented with the model verification features.

Abstract. The secure preservation of biomedical image data is a primary concern in today's technology enabled medical world. The new advancements in the technology are insisting us to outsource our digital data to a third party server and bring as and when needed. In this regard, efficient storage and transmission of this large medical data set becomes an important concern. In this paper we studied different compression techniques as a significant step of data preparation for implementing searchable encryption of medical data privacy preservation. We also shown texture based feature extraction for enabling privacy preserving query search. The simulation results obtained using different modalities of CT and MRI images with the performance comparison of wavelet and contourlet transform in peak signal to noise ratio for different compression ratios.

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