Multi-Touch Attribution Based Budget Allocation in Online Advertising

Budget allocation in online advertising deals with distributing the campaign (insertion order) level budgets to different sub-campaigns which employ different targeting criteria and may perform differently in terms of return-on-investment (ROI). In this paper, we present the efforts at Turn on how to best allocate campaign budget so that the advertiser or campaign-level ROI is maximized. To do this, it is crucial to be able to correctly determine the performance of sub-campaigns. This determination is highly related to the action-attribution problem, i.e. to be able to find out the set of ads, and hence the sub-campaigns that provided them to a user, that an action should be attributed to. For this purpose, we employ both last-touch (last ad gets all credit) and multi-touch (many ads share the credit) attribution methodologies. We present the algorithms deployed at Turn for the attribution problem, as well as their parallel implementation on the large advertiser performance datasets. We conclude the paper with our empirical comparison of last-touch and multi-touch attribution-based budget allocation in a real online advertising setting.Comment: This paper has been published in ADKDD 2014, August 24, New York City, New York, U.S.

A Comparative Analysis of Artificial Neural Network and Deep Learning Techniques in Heart Disease

The use of Computers has made the drastic change in the lifestyle of human beings, by making them dependantto solvemany complex applications which was unable to solve him previously. There are many real time applications which needs the joint interaction of human &machine to obtainthe results in fruitful manner. The Artificial Neural Network which is based on the concept of biological brain plays a major role in this, but still suffers from many limitations. To overcome this, a new approach called Deep Learning, based on same human biological systemslike Artificial Neural Network, came into existence to find the best solution in the field of medical science. The main aim of this papers is to review the comparative analysis ofthese two techniques being used in the detection of Heart Disease

Application of Liquid Rank Reputation System for Content Recommendation

An effective content recommendation on social media platforms should be able to benefit both creators to earn fair compensation and consumers to enjoy really relevant, interesting, and personalized content. In this paper, we propose a model to implement the liquid democracy principle for the content recommendation system. It uses a personalized recommendation model based on reputation ranking system to encourage personal interests driven recommendation. Moreover, the personalization factors to an end users' higher-order friends on the social network (initial input Twitter channels in our case study) to improve the accuracy and diversity of recommendation results. This paper analyzes the dataset based on cryptocurrency news on Twitter to find the opinion leader using the liquid rank reputation system. This paper deals with the tier-2 implementation of a liquid rank in a content recommendation model. This model can be also used as an additional layer in the other recommendation systems. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.Comment: Accepted in 2022 Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, Yekaterinburg, Russi

Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin

Analyzing social media trends can create a win-win situation for both creators and consumers. Creators can receive fair compensation, while consumers gain access to engaging, relevant, and personalized content. This paper proposes a new model for analyzing Bitcoin trends on Twitter by incorporating a 'liquid democracy' approach based on user reputation. This system aims to identify the most impactful trends and their influence on Bitcoin prices and trading volume. It uses a Twitter sentiment analysis model based on a reputation rating system to determine the impact on Bitcoin price change and traded volume. In addition, the reputation model considers the users' higher-order friends on the social network (the initial Twitter input channels in our case study) to improve the accuracy and diversity of the reputation results. We analyze Bitcoin-related news on Twitter to understand how trends and user sentiment, measured through our Liquid Rank Reputation System, affect Bitcoin price fluctuations and trading activity within the studied time frame. This reputation model can also be used as an additional layer in other trend and sentiment analysis models. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.Comment: Under publication in 2024 Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, Yekaterinburg, Russi

IN VITRO ANTIBACTERIAL ACTIVITY OF GREEN TEA EXTRACT AGAINST MULTIDRUG-RESISTANT BACTERIA

Objectives: This study aimed to evaluate the antimicrobial activity of ethanolic extract of green tea (Camellia sinensis) against multidrug-resistant strains of the pathogenic bacteria: Extended-spectrum-β-lactamase (ESBL) producing Enterobacteriaceae, Carbapenem-resistant Enterobacteriaceae (CRE), and Metallo-β-lactamase (MBL) producing Pseudomonas aeruginosa strains. Methods: In this cross-sectional study, the antibacterial activity of ethanolic extract of commercial green tea against the 23 multidrug-resistant test strains was evaluated by the Agar well diffusion method, and the minimum inhibitory concentration of the extract for the test strains was determined by Agar plate dilution method. Results: Ethanolic extract of green tea was found to exhibit a remarkably significant antimicrobial activity against the ATCC (American type culture collection) control strains: E. coli ATCC 25922 and P. aeruginosa ATCC 27853 with slightly higher activity against later as compared to the former. The extract exhibited a significant antibacterial activity against multidrug-resistant bacterial strains. The highest activity was shown against ESBL producing strains, followed by CRE strains and the least activity against MBL producers. Conclusion: This study strongly depicts that the ethanolic extract of green tea exhibits significant antibacterial activity even against multidrug-resistant strains. Hence, such plant extracts could be a potential source of bioactive lead compounds that could be utilized in developing herbal antimicrobials as an alternative strategy for tackling the problem of antimicrobial resistance

A hybrid scheme for prime factorization and its experimental implementation using IBM quantum processor

We report a quantum-classical hybrid scheme for factorization of bi-prime numbers (which are odd and square-free) using IBM's quantum processors. The hybrid scheme proposed here involves both classical optimization techniques and adiabatic quantum optimization techniques, and is build by extending a previous scheme of hybrid factorization [Pal et al., Pramana 92, 26 (2019) and Xu et al., Phys. Rev. Lett. 108, 130501 (2012)]. The quantum part of the scheme is very general in the sense that it can be implemented using any quantum computing architecture. Here, as an example, we experimentally implement our scheme for prime factorization using IBM's QX4 quantum processor and have factorised 35.Comment: 8 pages, 4 figures, 2 table

Evaluation of plant based natural coagulants for surface water treatment of Pratapgarh District Uttar Pradesh, India

The efficacy of three plant-based natural coagulants, namely papaya seed powder, banana peel powder, and lemon peel powder, was evaluated in this study for their ability to remove high electric conductivity turbidity, hardness, fluoride, and nitrate from surface water. The experiments were conducted at room temperature without any adjustment to the initial pH. The results indicated that banana peel powder exhibited the highest turbidity removal rate, achieving 55.6% removal when used at a dosage of 0.4 g/L. Furthermore, banana peel powder demonstrated excellent removal efficiency for fluoride and nitrate, with 85% removal observed at the same dosage. Lemon peel powder also exhibited significant effectiveness, achieving 60% removal. Papaya seed powder proved to be the most efficient coagulant for removing hardness, demonstrating a removal rate of 69.66%. The study further revealed a noteworthy linear relationship between the removal of turbidity and hardness, as evidenced by correlation coefficients (R2) ranging from 0.67 to 0.88. Similar linear relationships were observed for turbidity removals, with R2 values ranging from 0.68 to 0.8. An additional advantage of using these natural coagulants was that they did not cause any pH alteration in the treated surface water. Moreover, Fourier-transform infrared (FTIR) analysis of banana peels indicated the presence of functional groups such as carboxylic acid, hydroxyl, and aliphatic amines. These functional groups likely play a crucial role in facilitating coagulation and flocculation by neutralizing the charges of impurities in the water. This study suggests that inexpensive natural coagulants hold promise for surface water treatment, offering a viable alternative to conventional methods

Digital Media and Media literacy. An Analysis of the Contribution and Effect of social media in Media Literacy

In today’s digital world every one is significantly involved in consuming media content with their interest and intent. Now it is proven that most of the time we are accessing the media content through mobile phone or other handy devices through several applications and websites. According to one survey an average kindergartener can access 70 media messages everyday and teens are using one-third of the day in media messages.[1] India is world’s second-largest population and second-largest digital market which is growing drastically in both urban and rural areas. Now India has more than 500 million Internet users and over 450 million smartphone users and one in every three people is consuming video content online. Affordable and easy access to technology and the growth of regional language usage in media content is wonderfully mix the early and new Internet users, which make this market ripe for opportunity in digital media content.[2] India is a 2nd largest number of internet users in the world.[3] As a result a large number of population is shifting towards online and digital platform and it may possible that population is not that much media literate to analyze the critically think on the dissemination and consumption of media message. So this has become necessary to critically insight the term, types and effect of media literacy.Lattice Science Publication (LSP) © Copyright: All rights reserved

Physiology and Pathology of Multidrug-Resistant Bacteria: Phage-Related Therapy

Multidrug-resistant bacteria (MDR) are spreading rapidly across the world that outpace development of new antibiotics. Options other than antibiotics treatment are urgently needed. In this chapter, we review the current status of nonantibiotics-based strategies including phage therapy and phage-derived protein therapy for targeting Gram-positive strains (methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus faecium) and MDR Gram-negative strains (Acinetobacter baumannii and Pseudomonas aeruginosa)