A Real-Time Twitter Trend Analysis and Visualization Framework

Author(s):  
Jamuna S Murthy ◽  
Siddesh G.M. ◽  
Srinivasa K.G.

Trend analysis over Twitter offers organizations a fast and effective way of predicting the future trends. In the recent years, a wide range of indicators and methods were used for predicting the trend on Twitter with varying results, unfortunately most of the research focused only on the emerging trends which has gained long-term attention on the Twitter platform. This article depicts trend variations, i.e. to predict whether the trend on Twitter will gain attention or not in the next few hours. Hence a novel method called: “Twitter Trend Momentum (TTM)” is introduced for trend prediction which is the enhancement of a well-known stock market indicator called moving average convergence divergence (MACD). Reason analysis for trend variation is also carried out as an extension to the authors' research work. An evaluation of the framework showed the best results which are applied to build a real-time web application called “TwitTrend.” The application acts as a real-time update and recommendation system of top trends to users.

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2951 ◽  
Author(s):  
Daniel Carreres-Prieto ◽  
Juan T. García ◽  
Fernando Cerdán-Cartagena ◽  
Juan Suardiaz-Muro

Local administrations demand real-time and continuous pollution monitoring in sewer networks. Spectroscopy is a non-destructive technique that can be used to continuously monitor quality in sewers. Covering a wide range of wavelengths can be useful for improving pollution characterization in wastewater. Cost-effective and in-sewer spectrophotometers would contribute to accomplishing discharge requirements. Nevertheless, most available spectrometers are based on incandescent lamps, which makes it unfeasible to place them in a sewerage network for real-time monitoring. This research work shows an innovative calibration procedure that allows (Light-Emitting Diode) LED technology to be used as a replacement for traditional incandescent lamps in the development of spectrophotometry equipment. This involves firstly obtaining transmittance values similar to those provided by incandescent lamps, without using any optical components. Secondly, this calibration process enables an increase in the range of wavelengths available (working range) through a better use of the LED’s spectral width, resulting in a significant reduction in the number of LEDs required. Thirdly, this method allows important reductions in costs, dimensions and consumptions to be achieved, making its implementation in a wide variety of environments possible.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liaoyan Zhang

Streaming media server is the core system of audio and video application in the Internet; it has a wide range of applications in music recommendation. As song libraries and users of music websites and APPs continue to increase, user interaction data are generated at an increasingly fast rate, making the shortcomings of the original offline recommendation system and the advantages of the real-time streaming recommendation system more and more obvious. This paper describes in detail the working methods and contents of each stage of the real-time streaming music recommendation system, including requirement analysis, overall design, implementation of each module of the system, and system testing and analysis, from a practical scenario. Moreover, this paper analyzes the current research status and deficiencies in the field of music recommendation by analyzing the user interaction data of real music websites. From the actual requirements of the system, the functional and performance goals of the system are proposed to address these deficiencies, and then the functional structure, general architecture, and database model of the system are designed, and how to interact with the server side and the client side is investigated. For the implementation of data collection and statistics module, this paper adopts Flume and Kafka to collect user behavior data and uses Spark Streaming and Redis to count music popularity trends and support efficient query. The recommendation engine module in this paper is designed and optimized using Spark to implement incremental matrix decomposition on data streams, online collaborative topic model, and improved item-based collaborative filtering algorithm. In the system testing section, the functionality and performance of the system are tested, and the recommendation engine is tested with real datasets to show the discovered music themes and analyze the test results in detail.


2019 ◽  
Vol 11 (2) ◽  
pp. 323 ◽  
Author(s):  
Raheleh Hassannia ◽  
Ali Vatankhah Barenji ◽  
Zhi Li ◽  
Habib Alipour

The purpose of the study is to design and develop a recommended system based on agent and web technologies, which utilizes a hybrid recommendation filtering for the smart tourism industry. A hybrid recommendation system based on agent technology is designed by considering the online communication with other sectors in the tourism industry, such as the tourism supply chain, agency etc. However, online communication between the sectors via agents is designed and developed based on the contract net protocol. Furthermore, the design system is developed on the java agent development framework and implemented as a web application. Case study-based results considering two scenarios involving 100 customers illustrated that the proposed web application improves the rate of the recommendation for the customers. In the first scenario without disturbances, this rate was improved by 20% and the second scenario with disturbances yielded a 30% rate of acceptable recommendation. In addition, based on the second scenario, real time data communication on the system occurred, thus the proposed system supported real time data communication.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Anisha P. Rodrigues ◽  
Roshan Fernandes ◽  
Adarsh Bhandary ◽  
Asha C. Shenoy ◽  
Ashwanth Shetty ◽  
...  

Twitter is a popular microblogging social media, using which its users can share useful information. Keeping a track of user postings and common hashtags allows us to understand what is happening around the world and what are people’s opinions on it. As such, a Twitter trend analysis analyzes Twitter data and hashtags to determine what topics are being talked about the most on Twitter. Feature extraction and trend detection can be performed using machine learning algorithms. Big data tools and techniques are needed to extract relevant information from continuous steam of data originating from Twitter. The objectives of this research work are to analyze the relative popularity of different hashtags and which field has the maximum share of voice. Along with this, the common interests of the community can also be determined. Twitter trends plan an important role in the business field, marketing, politics, sports, and entertainment activities. The proposed work implemented the Twitter trend analysis using latent Dirichlet allocation, cosine similarity, K means clustering, and Jaccard similarity techniques and compared the results with Big Data Apache SPARK tool implementation. The LDA technique for trend analysis resulted in an accuracy of 74% and Jaccard with an accuracy of 83% for static data. The results proved that the real-time tweets are analyzed comparatively faster in the Big Data Apache SPARK tool than in the normal execution environment.


To estimate the reliability of software numerous statistical methods are in practice. To accomplish the software reliability prediction in more accurate way there is a huge demand for data sets. The data sets that can be acquired as a result of testing the software can be used for predicting the reliability. The research work focuses on creating a layer of software design and testing method namely web software testing. The main purpose is to collect the erroneous data from real time. The reliability of software can be measured in different aspects like traffic handling capability when there are a greater number of users, the security level for cracking the passwords and the possibility of different combinations of errors that occurs when inputting the data. This proposed software tool will read the software description, and will generate test patterns according to the input types and collects testing results, predicting the software reliability in real time and suggesting the possible ways to improve the software. For designing purpose PHP for web application will be used to give the testing results.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nicholas G. Reich ◽  
Matthew Cornell ◽  
Evan L. Ray ◽  
Katie House ◽  
Khoa Le

AbstractForecasting has emerged as an important component of informed, data-driven decision-making in a wide array of fields. We introduce a new data model for probabilistic predictions that encompasses a wide range of forecasting settings. This framework clearly defines the constituent parts of a probabilistic forecast and proposes one approach for representing these data elements. The data model is implemented in Zoltar, a new software application that stores forecasts using the data model and provides standardized API access to the data. In one real-time case study, an instance of the Zoltar web application was used to store, provide access to, and evaluate real-time forecast data on the order of 108rows, provided by over 40 international research teams from academia and industry making forecasts of the COVID-19 outbreak in the US. Tools and data infrastructure for probabilistic forecasts, such as those introduced here, will play an increasingly important role in ensuring that future forecasting research adheres to a strict set of rigorous and reproducible standards.


2020 ◽  
Vol 99 (5) ◽  
pp. 493-497
Author(s):  
M. M. Aslanova ◽  
T. V. Gololobova ◽  
K. Yu. Kuznetsova ◽  
Tamari R. Maniya ◽  
D. V. Rakitina ◽  
...  

Introduction. The purpose of our work was to justify the need to improve the legislative, regulatory and methodological framework and preventative measures in relation to the spread of parasitic infections in the provision of medical care. There is a wide range of pathogens of parasitic infestations that are transmitted to humans through various medical manipulations and interventions carried out in various medical institutions. Contaminated care items and furnishings, medical instruments and equipment, solutions for infusion therapy, medical personnel’s clothing and hands, reusable medical products, drinking water, bedding, suture and dressing materials can serve as a major factor in the spread of parasitic infections in the provision of medical care. Purpose of research is the study of the structure and SMP of parasitic origin, circulating on the objects of the production environment in multi-profile medical and preventive institutions of stationary type in order to prevent the occurrence of their spread within medical institutions. Material and methods. The material for the study was flushes taken from the production environment in 3 multi-profile treatment and prevention institutions of inpatient type: a multi-specialty hospital, a maternity hospital and a hospital specializing in the treatment of patients with intestinal diseases for the eggs of worms and cysts of pathogenic protozoa. Results. During the 2-year monitoring of medical preventive institutions, a landscape of parasitic contamination was found to be obtained from the flushes taken from the production environment objects in the premises surveyed as part of the research work. Discussions. In the course of research, the risk of developing ISMP of parasitic origin was found to be determined by the degree of epidemiological safety of the hospital environment, the number and invasiveness of treatment and diagnostic manipulations and various medical technologies. Conclusion. It is necessary to conduct an expert assessment of regulatory and methodological documents in the field of epidemiological surveillance and sanitary and hygienic measures for the prevention of medical aid related infections of parasitic origin, to optimize the regulatory and methodological base, to develop a number of preventive measures aimed at stopping the spread of parasitic infections in the medical network.


2020 ◽  
Vol 5 (3) ◽  
pp. 224-235
Author(s):  
Harshal A. Pawar ◽  
Bhagyashree D. Bhangale

Background: Lipid based excipients have increased acceptance nowadays in the development of novel drug delivery systems in order to improve their pharmacokinetic profiles. Drugs encapsulated in lipids have enhanced stability due to the protection they experience in the lipid core of these nano-formulations. Phytosomes are newly discovered drug delivery systems and novel botanical formulation to produce lipophilic molecular complex which imparts stability, increases absorption and bioavailability of phytoconstituent. Curcumin, obtained from turmeric (Curcuma longa), has a wide range of biological activities. The poor solubility and wettability of curcumin are responsible for poor dissolution and this, in turn, results in poor bioavailability. To overcome these limitations, the curcumin-loaded nano phytosomes were developed to improve its physicochemical stability and bioavailability. Objective: The objective of the present research work was to develop nano-phytosomes of curcumin to improve its physicochemical stability and bioavailability. Methods: Curcumin-loaded nano phytosomes were prepared by using phospholipid Phospholipon 90 H using a modified solvent evaporation method. The developed curcumin nano phytosomes were evaluated by particle size analyzer and differential scanning calorimetry (DSC). Results: Results indicated that phytosomes prepared using curcumin and lipid in the ratio of 1:2 show good entrapment efficiency. The obtained curcumin phytosomes were spherical in shape with a size less than 100 nm. The prepared nano phytosomal formulation of curcumin showed promising potential as an antioxidant. Conclusion: The phytosomal complex showed sustained release of curcumin from vesicles. The sustained release of curcumin from phytosome may improve its absorption and lowers the elimination rate with an increase in bioavailability.


Author(s):  
Simeon J. Yates ◽  
Jordana Blejmar

Two workshops were part of the final steps in the Economic and Social Research Council (ESRC) commissioned Ways of Being in a Digital Age project that is the basis for this Handbook. The ESRC project team coordinated one with the UK Defence Science and Technology Laboratory (ESRC-DSTL) Workshop, “The automation of future roles”; and one with the US National Science Foundation (ESRC-NSF) Workshop, “Changing work, changing lives in the new technological world.” Both workshops sought to explore the key future social science research questions arising for ever greater levels of automation, use of artificial intelligence, and the augmentation of human activity. Participants represented a wide range of disciplinary, professional, government, and nonprofit expertise. This chapter summarizes the separate and then integrated results. First, it summarizes the central social and economic context, the method and project context, and some basic definitional issues. It then identifies 11 priority areas needing further research work that emerged from the intense interactions, discussions, debates, clustering analyses, and integration activities during and after the two workshops. Throughout, it summarizes how subcategories of issues within each cluster relate to central issues (e.g., from users to global to methods) and levels of impacts (from wider social to community and organizational to individual experiences and understandings). Subsections briefly describe each of these 11 areas and their cross-cutting issues and levels. Finally, it provides a detailed Appendix of all the areas, subareas, and their specific questions.


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