A survey on prediction approaches for epidemic disease outbreaks based on social media data

S, Ravi Kumar

doi:10.30534/ijatcse/2019/86832019

Cited by 4 publications

(3 citation statements)

References 54 publications

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“…These are sporadically tested, since medical practitioners are also newfangled for corona like new virus. Moreover, there has been no particular ministration for these kind of pervasive viruses [50]. The Sentimental mining and Opinion mining are pivotal research areas because due to the massive number of daily posts on social media networks, extracting people's opinions or behaviors is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Analysing COVID-19 News Impact on Social Media Aggregation

Dheeraj¹

2020

IJATCSE

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The pandemic coronavirus so called COVID-19 outbreak, which was recognized in late 2019, requires special consciousness because of its future epidemics and possible worldwide menace. Besides its clinical approaches and treatments. As Artificial Intelligence (AI) assures a new prototype for healthcare. There are various AI tools that are built upon Machine Learning (ML) algorithms are included to examine the data and decision-making processes. People spend many hours every-day on social media websites to share their views, ideas, opinions, and expressions with others, so in this paper, I have analysed the sentiments on reddit news regarding coronavirus disease(COVID-19) jeopardy, because most of the peoples from various countries are affected by coronavirus that is very condemnatory issue at present days, so analyze the sentiments of various people's opinion for this disease, we are fetching the reddit streaming articles connected to coronavirus using reddit API and scrutinized these articles using machine learning methods and tools as positive, negative and neutral. In this paper, I have run experiments through Python programming on various articles related to pandemic corona virus using reddit API and NLTK library is used for pre-processing of articles and then scrutinizing the articles dataset by using Textblob and it pointers to stimulating outcomes as positive, negative, neutral sentiments over different conceptions. The outcomes show that on reddit related to COVID-19 articles about 50% percentage articles are neutral, 22% articles are positive and about 28 % was negative.

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Section: Introductionmentioning

confidence: 99%

Analysing COVID-19 News Impact on Social Media Aggregation

Dheeraj¹

2020

IJATCSE

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“…Social media has been used and utilized for many domains, including natural disasters [2] and epidemic disease outbreak [3]. Social media provide information for decision-making and as source information before, during, and after big events, including natural disasters.…”

Section: Introductionmentioning

confidence: 99%

Social Media Disaster Relevance Classification for Situation Awareness during Emergency Response in Indonesia

Irawan¹

2020

IJETER

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Natural disasters are expected to increase in number and severity on a global scale. Social media analysis has become an essential tool in natural disaster management on tracking disaster events, impact, and other relevant critical information. However, the high volume of tweets data produces noise, and not all tweets are relevant to gain situational awareness for disaster response. This paper presents a disaster-relevance classification for Indonesian language tweets using machine learning with Naïve Bayes, support vector machine, and logistic regression with a focus on twitter data generated during the Sulawesi Earthquake, Indonesia 2018. With the result accuracy of 83.5%, our labeled data can be used for capturing disaster-relevant tweets in any future disaster event in Indonesia.

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“…Disease identification [3] and providing required nutrients to them become an important part of farming. When the diagnosis is done using naked eye and therefore there are high chances of wrong diagnosis.…”

Section: Introductionmentioning

confidence: 99%

SasyaSneha – An Approach for Plant Leaf Disease Detection

Mathai¹

2019

IJATCSE

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The productivity in agricultural field is the one on which economy is highly determined. It may be the reason why disease identification in plants plays a good role in the field of agriculture. If we are not giving proper care, then it creates serious problems on plants and due to this the product quality, quantity or productivity will be affected. In a large portion of the fields, real gains have been acquired by the properties of computational intensity of Deep Learning systems for settling master errands. Deep Learning technology is used in the area of agriculture to solve the problem of diagnosing the plant disease based on the image taken in Smartphone. The images of leaves of healthy and diseased plants were used for plant disease detection and recognition by making use of convolutional neural network models as the learning tool. The total preparing of the models was finished with the utilization of a dataset of 1000 pictures, containing 4 distinct plants including healthy plants. The proposed system is expected to give a significant high success rate. This can be used as an advisory or early warning tool, and this approach could be further expanded to support an advanced plant disease identification system to operate in real cultivation conditions.

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A survey on prediction approaches for epidemic disease outbreaks based on social media data

Cited by 4 publications

References 54 publications

Analysing COVID-19 News Impact on Social Media Aggregation

Analysing COVID-19 News Impact on Social Media Aggregation

Social Media Disaster Relevance Classification for Situation Awareness during Emergency Response in Indonesia

SasyaSneha – An Approach for Plant Leaf Disease Detection

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