scholarly journals Neph E Club-Successful Social Media Learning Model-Six Years on with 1K Nephrologist as Members

2021 ◽  
Vol 2 (9) ◽  
pp. 771-776
Author(s):  
Ravichandran Palani ◽  
Evamaria B Kaiser ◽  
Soundarajan Periyaswamy
Keyword(s):  
Social Media ◽  
Digital Library ◽  
Cost Effective ◽  
Learning Model ◽  
Limiting Factors ◽  
Presidential Speech ◽  
Essential Components ◽  
Education Model ◽  
Media Learning ◽  
Data Collections

Introduction: Social Media (SoMe) is used for the rapid dissemination of information and learning but has its limiting factors. An integrated learning model labeled “Neph E Club” was initiated in 2015. Various social media platforms were used to achieve the goal of SoMe education and lifelong learning. A retrospective study was done to analyze this education model. Methods: Six years of experience with Neph E Club’s social media education model allowed us to look back on the essential components of the SoMe model from 2015 to 2021. Objectives, member recruitment, social media platform, content development, and sustainability were among the aspects investigated and analyzed. Its benefits and downsides were also investigated. Results: For the past 6 years, WhatsApp has been used as a SoMe platform. Other approaches such as Twitter, YouTube, dedicated server, and email methods failed to meet the target during the 6 years. There are now 1018 active members in the WhatsApp group. Downloading nephrology education resources from numerous social media learning websites and conference content to construct a 3 TB digital library and 800 GB of developed and shared content. Members were kept informed daily by sharing information gleaned from the digital library. Topics are chosen based on data analysis and group requests. Students and practitioners shared their contents which included case discussion and initiating data collections. The success of this model is reflected by having shared 2550 Audiovisual (AV) Lectures, 26700 journal articles, and 182 case discussions. Viewership numbers on average reach from 1200 to 2500 per month which indicated multiple sharing. ISN India in his presidential speech in 2017 recommended this model. Conclusion: Neph E Club - Integrated model of learning using SoMe and offline digital Nephrology Library is a cost-effective, widely accepted model of learning in India.

Factor Analysis among the variables relating to purpose of using social media websites and nonacceptance of social media in recruitment

2020 ◽  
pp. 245-248
Keyword(s):  
Social Media ◽  
Factor Analysis ◽  
Social Science Research ◽  
Cost Effective ◽  
Science Research ◽  
Recruitment Strategies ◽  
Job Seeking ◽  
Hiring Process ◽  
To Come ◽  
New Strategies

In the era of Globalization, advancement of technology and stiff competition, particularly, in the I.T. Industry, companies have to adopt new H.R. strategies and practices so as to constantly evolve and grow. In this context, existing recruitment strategies have to be replaced by new strategies. Many companies are now extensively depending on the internet to connect to larger audiences globally. Organizations are in a position to attract profiles, resumes from potential candidates by announcing their vacancies on their own websites. E-recruitment is evoking interest among the companies typically over the last few years. The spread of information technology and growth of Internet has paved way for companies willing to hunt for talent on the job seeking websites. In the years to come, social networking will soon be an indispensable part of the hiring process. It is cost effective, does not require setting up an office and forms an effective tool for recruiters. The main purpose of this study was to understand the application of factor analysis in social science research and to reduce a large number of variables into manageable smaller factors for further analysis of the employers’ perception on social media recruitment with reference to the I.T. Sector in Bangalore.

Addressing Military Sexual Violence by Proposing a Social Media Influencer Model

2021 ◽  
pp. 0306624X2199406
Author(s):  
Roberto Cancio
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Sexual Violence ◽  
Cost Effective ◽  
Military Leaders ◽  
Group Solidarity ◽  
Central Values ◽  
Reference Sections ◽  
Media Intervention ◽  
Present Systematic Review

Military sexual violence (MSV) is a prevalent issue that uniquely affects mission readiness. Although research on MSV and social media is growing, examinations of possible interventions like those employing social media in this population are scant. Given the growing interest in targeting MSV, the present systematic review was conducted. The PRISMA framework was used to conduct a systematic review of MSV and social media ( N = 71). Queries were limited to articles published between 2010 and 2020. SAGE Journals, PubMed, and JSTOR were utilized. Terms and potential combinations were entered into the databases in varying Boolean combinations. Additional recorders were identified for inclusion via the reference sections of relevant records. After removing duplicates from the query results, we selected records of suspected relevance by title and screened abstracts. Finally, articles with relevant abstracts were reviewed thoroughly to determine whether they met inclusion criteria for the review. The employments of military leaders in a social media intervention puts into practice the military’s central values and development of its leadership core. This intervention promotes group solidarity while maximizing conversations around meaningful messages. Findings in this review suggest military leaders could feasibly employ a cost-effective global intervention using social media, as a tool to help actively address MSV.

scholarly journals Genotyping Strategies Using ddRAD Sequencing in Farmed Arctic Charr (Salvelinus alpinus)

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 899
Author(s):  
Fotis Pappas ◽  
Christos Palaiokostas
Keyword(s):  
Genetic Diversity ◽  
Salvelinus Alpinus ◽  
Arctic Charr ◽  
Cost Effective ◽  
Limiting Factors ◽  
Phenotypic Traits ◽  
Production Volume ◽  
High Coverage ◽  
Genomic Relationships ◽  
Low Coverage

Incorporation of genomic technologies into fish breeding programs is a modern reality, promising substantial advances regarding the accuracy of selection, monitoring the genetic diversity and pedigree record verification. Single nucleotide polymorphism (SNP) arrays are the most commonly used genomic tool, but the investments required make them unsustainable for emerging species, such as Arctic charr (Salvelinus alpinus), where production volume is low. The requirement to genotype a large number of animals for breeding practices necessitates cost effective genotyping approaches. In the current study, we used double digest restriction site-associated DNA (ddRAD) sequencing of either high or low coverage to genotype Arctic charr from the Swedish national breeding program and performed analytical procedures to assess their utility in a range of tasks. SNPs were identified and used for deciphering the genetic structure of the studied population, estimating genomic relationships and implementing an association study for growth-related traits. Missing information and underestimation of heterozygosity in the low coverage set were limiting factors in genetic diversity and genomic relationship analyses, where high coverage performed notably better. On the other hand, the high coverage dataset proved to be valuable when it comes to identifying loci that are associated with phenotypic traits of interest. In general, both genotyping strategies offer sustainable alternatives to hybridization-based genotyping platforms and show potential for applications in aquaculture selective breeding.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

scholarly journals Two Formulas for Success in Social Media: Learning and Network Effects

2015 ◽  
Vol 32 (4) ◽  
pp. 78-108 ◽  
Author(s):  
Liangfei Qiu ◽  
Qian Tang ◽  
Andrew B. Whinston
Keyword(s):  
Social Media ◽  
Network Effects ◽  
Media Learning

Integrating Social and Traditional Media in the Client Project

2011 ◽  
Vol 74 (4) ◽  
pp. 494-504 ◽  
Author(s):  
James Melton ◽  
Nancy Hicks
Keyword(s):  
Social Media ◽  
Traditional Media ◽  
Business Functions ◽  
Media Learning ◽  
Communication Methods

Based on a client project assigned to students in two undergraduate business classes, this article argues that social media learning is best done in a context that mixes social media with more traditional kinds of media. Ideally, this approach will involve teams of students who are working on different aspects of a larger client project. This integrated setup has several benefits: It enhances the students’ understanding of social media within a real context, it complements more traditional communication methods, and it reveals the communicative aspects of key business functions.

scholarly journals P112 SMILE: Sustaining Medical Education In a Lockdown Environment.Using social media to develop a free online access medical education platform during lockdown

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
John Pascoe ◽  
Paul Foster ◽  
Muntasha Quddus ◽  
Angeliki Kosti ◽  
Francesca Guest ◽  
...  
Keyword(s):  
Social Media ◽  
Medical Education ◽  
Online Survey ◽  
Cost Effective ◽  
User Comments ◽  
Surgical Trainees ◽  
Social Media Platforms ◽  
Online Access ◽  
Education Platform

Abstract Introduction SMILE is a free online access medical education (FOAMEd) platform created by two UK surgical trainees and a medical student that delivered over 200 medical lectures during lockdown. Method The role of Social Media in the development of SMILE was interrogated using a survey sent to all SMILE participants and by analysing activity on SMILE social media platforms. Results 1306 students responded to the online survey with 57.2% saying they heard of SMILE through Facebook. Engagement using facebook remained highest with 13,819 members, over 800 user comments and >16,000 user reactions. 4% of the students heard of SMILE through Twitter or Instagram. Facebook analytics revealed the highest level of traffic when lectures were most commonly held suggesting students used Facebook to access lectures. Other educators were able to find SMILE on social media, leading to collaborations with other platforms. Throughout the survey many mentioned how social media created and maintained a community of medical students enhancing group-based learning Conclusions We demonstrate that social media platforms provide popular and cost-effective methods to promote, sustain & deliver medical education for students and educators.

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

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