Study of Network Traffic Analysis and Prediction

Network traffic analysis is the way toward chronicle, evaluating and examining system traffic with the end goal of execution, security as well as general system tasks and the executives. Analysis and prediction of network traffic has applications in wide far reaching set of zones and has recently pulled in noteworthy number of studies. Various types of trials are directed and condensed to distinguish different issues in existing PC arrange applications. System traffic examination and forecast is a proactive way to deal with guarantee secure, dependable and subjective system correspondence. Different systems are proposed and tested for analyzing system traffic including neural network based strategies to data mining methods. So also, different Linear and non-linear models are proposed for system traffic prediction. A few intriguing mixes of system examination and forecast strategies are actualized to achieve proficient and compelling outcomes [3]

The Flexible Substitution Logit: Uncovering Category Expansion and Share Impacts of Marketing Instruments

Different instruments are relevant for different marketing objectives (category demand expansion or market share stealing). To help brand managers make informed marketing mix decisions, it is essential that marketing mix models appropriately measure the different effects of marketing instruments. Discrete choice models that have been applied to this problem might not be adequate because they possess the Invariant Proportion of Substitution (IPS) property, which imposes counter-intuitive restrictions on individual choice behavior. Indeed our empirical application to prescription writing choices of physicians in the hyperlipidemia category shows this to be the case. We find that three commonly used models that all suffer from the IPS restriction - the homogeneous logit model, the nested logit model, and the random coefficient logit model - lead to counter-intuitive estimates of the sources of demand gains due to increased marketing investments in Direct-to-Consumer Advertising (DTCA), detailing, and Meetings and Events (M&E). We then propose an alternative choice model specification that relaxes the IPS property - the so-called "flexible substitution" logit (FSL) model. The (random coefficient) FSL model predicts that sales gains from DTCA and M&E come primarily from the non-drug treatment (87.4% and 70.2% respectively), whereas gains from detailing come at the expense of competing drugs (84%). By contrast, the random coefficient logit model predicts that gains from DTCA, M&E and detailing all would come largely from competing drugs.

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction

Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem. The State-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which are Long-Short-Term-Memory networks (LSTMs) \cite{hochreiter1997long}. Deep neural networks, due to their large number of free parameters relies heavily on large annotated corpora for learning the end task. But in real-world, it is hard to get large labeled data, mainly due to heavy cost associated with manual annotation. Towards this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction.Comment: Accepted at DTMBIO workshop, CIKM 2017. To appear in BMC Bioinformatics. Pls cite that versio