An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach

Twitter Mining for Syndromic Surveillance

Enormous amounts of personalised data is generated daily from social media platforms today. Twitter in particular, generates vast textual streams in real-time, accompanied with personal information. This big social media data offers a potential avenue for inferring public and social patterns. This PhD thesis investigates the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome - asthma/difficulty breathing. We seek to develop means of extracting reliable signals from the Twitter signal, to be used for syndromic surveillance purposes. We begin by outlining our data collection and preprocessing methods. However, we observe that even with keyword-based data collection, many of the collected tweets are not relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition. In light of this, we set out to identify relevant tweets to collect a strong and reliable signal. We first develop novel features based on the emoji content of Tweets and apply semi-supervised learning techniques to filter Tweets. Next, we investigate the effectiveness of deep learning at this task. We pro-pose a novel classification algorithm based on neural language models, and compare it to existing successful and popular deep learning algorithms. Following this, we go on to propose an attentive bi-directional Recurrent Neural Network architecture for filtering Tweets which also offers additional syndromic surveillance utility by identifying keywords among syndromic Tweets. In doing so, we are not only able to detect alarms, but also have some clues into what the alarm involves. Lastly, we look towards optimizing the Twitter syndromic surveillance pipeline by selecting the best possible keywords to be supplied to the Twitter API. We developed algorithms to intelligently and automatically select keywords such that the quality, in terms of relevance, and quantity of Tweets collected is maximised

Specialized IoT systems: Models, Structures, Algorithms, Hardware, Software Tools

Монография включает анализ проблем, модели, алгоритмы и программно- аппаратные средства специализированных сетей интернета вещей. Рассмотрены результаты проектирования и моделирования сети интернета вещей, мониторинга качества продукции, анализа звуковой информации окружающей среды, а также технология выявления заболеваний легких на базе нейронных сетей. Монография предназначена для специалистов в области инфокоммуникаций, может быть полезна студентам соответствующих специальностей, слушателям факультетов повышения квалификации, магистрантам и аспирантам

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov