Sentiment Analysis includes methods and techniques for businesses to understand and analyze customer reviews, feedback and opinion on a particular product or service. Sentiment Analysis uses Natural Language Processing (NLP) tools to analyze feelings or emotions, attitudes, opinions, thoughts, etc. behind the words. Sentiments such as positive, negative and neutral are associated with a particular product. Sentiment analysis is applicable in multi-domains such as customer feedback for a particular product, movie reviews, social and political comments. This survey basically focuses on different aspect-based word embedding models and aspect-based sentiment classification techniques, where the goal is to extract key features from the sentences and classify sentiment on entities at document level. Aspect Based Sentiment Analysis (ABSA) is a technique that concentrates not only the entire sentence but analyses key terms explicitly to predict the polarity as a whole. ABSA model accepts aspect categories and its corresponding aspect terms to generate sentiment corresponding to each aspect from the text corpus. This article provides a comprehensive survey on different word embedding models under CNN framework for aspect extraction and different machine learning techniques applicable for sentiment classification purpose.
{"title":"A Comprehensive Survey on Aspect Based Word Embedding Models and Sentiment Analysis Classification Approaches","authors":"Monika Agrawal, Nageswara Rao Moparthi","doi":"10.3233/apc210175","DOIUrl":"https://doi.org/10.3233/apc210175","url":null,"abstract":"Sentiment Analysis includes methods and techniques for businesses to understand and analyze customer reviews, feedback and opinion on a particular product or service. Sentiment Analysis uses Natural Language Processing (NLP) tools to analyze feelings or emotions, attitudes, opinions, thoughts, etc. behind the words. Sentiments such as positive, negative and neutral are associated with a particular product. Sentiment analysis is applicable in multi-domains such as customer feedback for a particular product, movie reviews, social and political comments. This survey basically focuses on different aspect-based word embedding models and aspect-based sentiment classification techniques, where the goal is to extract key features from the sentences and classify sentiment on entities at document level. Aspect Based Sentiment Analysis (ABSA) is a technique that concentrates not only the entire sentence but analyses key terms explicitly to predict the polarity as a whole. ABSA model accepts aspect categories and its corresponding aspect terms to generate sentiment corresponding to each aspect from the text corpus. This article provides a comprehensive survey on different word embedding models under CNN framework for aspect extraction and different machine learning techniques applicable for sentiment classification purpose.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123200313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Vijay Kumar, Arvind Yadav, B. Vishnupriya, M. Naga Lahari, J. Smriti, D. Samved Reddy
In this era everything is digitalized we can find a large amount of digital data for different purposes on the internet and relatively it’s very hard to summarize this data manually. Automatic Text Summarization (ATS) is the subsequent big one that could simply summarize the source data and give us a short version that could preserve the content and the overall meaning. While the concept of ATS is started long back in 1950’s, this field is still struggling to give the best and efficient summaries. ATS proceeds towards 2 methods, Extractive and Abstractive Summarization. The Extractive and Abstractive methods had a process to improve text summarization technique. Text Summarization is implemented with NLP due to packages and methods in Python. Different approaches are present for summarizing the text and having few algorithms with which we can implement it. Text Rank is what to extractive text summarization and it is an unsupervised learning. Text Rank algorithm also uses undirected graphs, weighted graphs. keyword extraction, sentence extraction. So, in this paper, a model is made to get better result in text summarization with Genism library in NLP. This method improves the overall meaning of the phrase and the person reading it can understand in a better way.
在这个一切都数字化的时代，我们可以在互联网上找到大量不同用途的数字数据，相对而言，人工汇总这些数据是非常困难的。自动文本摘要(Automatic Text Summarization, ATS)是随后发展起来的一个大技术，它可以简单地对源数据进行总结，然后给出一个既能保留内容又能保留整体含义的简短版本。虽然ATS的概念早在20世纪50年代就开始了，但该领域仍在努力给出最佳和有效的总结。ATS有两种方法:抽取和抽象总结。抽取和抽象方法对文本摘要技术有一个改进的过程。由于Python中的包和方法，文本摘要使用NLP实现。目前有不同的方法来总结文本，但我们可以实现它的算法很少。文本排序是提取文本摘要的方法，是一种无监督学习。文本排序算法也使用无向图、加权图。关键词提取，句子提取。为此，本文建立了一个基于Genism库的文本摘要模型，以获得较好的文本摘要效果。这种方法提高了短语的整体意义，阅读它的人可以更好地理解它。
{"title":"Text Summarizing Using NLP","authors":"G. Vijay Kumar, Arvind Yadav, B. Vishnupriya, M. Naga Lahari, J. Smriti, D. Samved Reddy","doi":"10.3233/apc210179","DOIUrl":"https://doi.org/10.3233/apc210179","url":null,"abstract":"In this era everything is digitalized we can find a large amount of digital data for different purposes on the internet and relatively it’s very hard to summarize this data manually. Automatic Text Summarization (ATS) is the subsequent big one that could simply summarize the source data and give us a short version that could preserve the content and the overall meaning. While the concept of ATS is started long back in 1950’s, this field is still struggling to give the best and efficient summaries. ATS proceeds towards 2 methods, Extractive and Abstractive Summarization. The Extractive and Abstractive methods had a process to improve text summarization technique. Text Summarization is implemented with NLP due to packages and methods in Python. Different approaches are present for summarizing the text and having few algorithms with which we can implement it. Text Rank is what to extractive text summarization and it is an unsupervised learning. Text Rank algorithm also uses undirected graphs, weighted graphs. keyword extraction, sentence extraction. So, in this paper, a model is made to get better result in text summarization with Genism library in NLP. This method improves the overall meaning of the phrase and the person reading it can understand in a better way.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130464694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To maintain the security of vulnerable network is the most essential thing in network system; for network protection or to eliminate unauthorized access of internal as well as external connections, various architectures have been suggested. Various existing approaches has developed different approaches to detect suspicious attacks on victimized machines; nevertheless, an external user develops malicious behaviour and gains unauthorized access to victim machines via such a behaviour framework, referred to as malicious activity or Intruder. A variety of supervised machine algorithms and soft computing algorithms have been developed to distinguish events in real-time as well as synthetic network log data. On the benchmark data set, the NLSKDD most commonly used data set to identify the Intruder. In this paper, we suggest using machine learning algorithms to identify intruders. A signature detection and anomaly detection are two related techniques that have been suggested. In the experimental study, the Recurrent Neural Network (RNN) algorithm is demonstrated with different data sets, and the system’s output is demonstrated in a real-time network context.
{"title":"An Intrusion Detection System for Network Security Using Recurrent Neural Network","authors":"K. Jadhav, Mohit Gangwar","doi":"10.3233/apc210243","DOIUrl":"https://doi.org/10.3233/apc210243","url":null,"abstract":"To maintain the security of vulnerable network is the most essential thing in network system; for network protection or to eliminate unauthorized access of internal as well as external connections, various architectures have been suggested. Various existing approaches has developed different approaches to detect suspicious attacks on victimized machines; nevertheless, an external user develops malicious behaviour and gains unauthorized access to victim machines via such a behaviour framework, referred to as malicious activity or Intruder. A variety of supervised machine algorithms and soft computing algorithms have been developed to distinguish events in real-time as well as synthetic network log data. On the benchmark data set, the NLSKDD most commonly used data set to identify the Intruder. In this paper, we suggest using machine learning algorithms to identify intruders. A signature detection and anomaly detection are two related techniques that have been suggested. In the experimental study, the Recurrent Neural Network (RNN) algorithm is demonstrated with different data sets, and the system’s output is demonstrated in a real-time network context.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130360759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brain Tumor detection using Convolutional Neural Network (CNN) is used to discover and classify the types of Tumor. Over a amount of years, many researchers are researched and planned ways throughout this area. We’ve proposed a technique that’s capable of detecting and classifying different types of tumor. For detecting and classifying tumor we have used MRI because MRI images gives the complete structure of the human brain, without any operation it scans the human brain and this helps in processing of image for the detection of the Tumor. The prediction of tumor by human from the MRI images leads to misclassification. This motivates us to construct the algorithm for detection of the brain tumor. Machine learning helps and plays a vital role in detecting tumor. In this paper, we tend to use one among the machine learning algorithm i.e. Convolutional neural network (CNN), as CNNs are powerful in image processing and with the help of CNN and MRI images we designed a framework for detection of the brain tumor and classifying its Different types.
{"title":"Analysis of Brain Tumor Disease Detection Using Convolutional Neural Network","authors":"Yogesh S. Deshmukh, Samiksha Dahe, Tanmayeeta Belote, Aishwarya Gawali, Sunnykumar Choudhary","doi":"10.3233/apc210246","DOIUrl":"https://doi.org/10.3233/apc210246","url":null,"abstract":"Brain Tumor detection using Convolutional Neural Network (CNN) is used to discover and classify the types of Tumor. Over a amount of years, many researchers are researched and planned ways throughout this area. We’ve proposed a technique that’s capable of detecting and classifying different types of tumor. For detecting and classifying tumor we have used MRI because MRI images gives the complete structure of the human brain, without any operation it scans the human brain and this helps in processing of image for the detection of the Tumor. The prediction of tumor by human from the MRI images leads to misclassification. This motivates us to construct the algorithm for detection of the brain tumor. Machine learning helps and plays a vital role in detecting tumor. In this paper, we tend to use one among the machine learning algorithm i.e. Convolutional neural network (CNN), as CNNs are powerful in image processing and with the help of CNN and MRI images we designed a framework for detection of the brain tumor and classifying its Different types.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122285243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Hubert Tony Raj, R. Sivakumar, R. Akash, M. Anandha Chakravarthi
Renewable energy provisions must be extracted in a more resourceful way, with a power converter added to the mix. If the supply-demand curve rises with the seasons, it becomes clear that renewable energy sources are used to provide clean energy. This clean energy cannot be used on load directly due to fluctuating conditions, to solve this problem a modified DC to DC converter with a ripple-free output is introduced. The Vertical Axis Wind Turbine (VAWT) and Solar PV were combined to achieve a constant DC output in a hybrid renewable energy conversion system. For renewable energy applications, a redesigned converter with ripple-free output is used. The simulation is made under MATLAB/SIMULINK and experimental parameters were measured using a nominal prototype.
{"title":"Reduction of Voltage Ripple in DC Link of Wind Energy Conservation System Using Modified DC-DC Boost Converter","authors":"L. Hubert Tony Raj, R. Sivakumar, R. Akash, M. Anandha Chakravarthi","doi":"10.3233/apc210302","DOIUrl":"https://doi.org/10.3233/apc210302","url":null,"abstract":"Renewable energy provisions must be extracted in a more resourceful way, with a power converter added to the mix. If the supply-demand curve rises with the seasons, it becomes clear that renewable energy sources are used to provide clean energy. This clean energy cannot be used on load directly due to fluctuating conditions, to solve this problem a modified DC to DC converter with a ripple-free output is introduced. The Vertical Axis Wind Turbine (VAWT) and Solar PV were combined to achieve a constant DC output in a hybrid renewable energy conversion system. For renewable energy applications, a redesigned converter with ripple-free output is used. The simulation is made under MATLAB/SIMULINK and experimental parameters were measured using a nominal prototype.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129549479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vibhanshu Pant, Deepak Sharma, Annapurani. K, R. Sundar
Keeping up the attendance record with everyday exercises is a difficult task. The conventional method of marking staff attendance is by tapping their ID card and then using fingerprint scanner. But due to COVID-19 pandemic the attendance system of using fingerprint scanner is stalled and currently not in use. The following system depends on face recognition and intranet connectivity to keep up attendance record of facilities and staff. The paper discusses the attendance marking system that is passive (no direct contact with the scanner or sensor) and restricting the users within certain network. The main goal of this system is divided in two steps, in initial step face is snare from the front camera of the smart phone and it is then recognized in the picture and in the second step these distinguished appearances and features are contrasted with stored information in data set for confirmation.
{"title":"Online Attendance Marking System Using Facial Recognition and Intranet Connectivity","authors":"Vibhanshu Pant, Deepak Sharma, Annapurani. K, R. Sundar","doi":"10.3233/apc210247","DOIUrl":"https://doi.org/10.3233/apc210247","url":null,"abstract":"Keeping up the attendance record with everyday exercises is a difficult task. The conventional method of marking staff attendance is by tapping their ID card and then using fingerprint scanner. But due to COVID-19 pandemic the attendance system of using fingerprint scanner is stalled and currently not in use. The following system depends on face recognition and intranet connectivity to keep up attendance record of facilities and staff. The paper discusses the attendance marking system that is passive (no direct contact with the scanner or sensor) and restricting the users within certain network. The main goal of this system is divided in two steps, in initial step face is snare from the front camera of the smart phone and it is then recognized in the picture and in the second step these distinguished appearances and features are contrasted with stored information in data set for confirmation.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130700815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The term “COVID” is breaking the hearts of the entire human community. The Corona virus is more infectious and is exceptionally irresistible, it is vital to isolate the patients and yet the specialists need to screen Corona virus patients as well. With the expanding increase in the number of Corona cases, the doctors find it difficult to keep track on the medical issue of isolated patients. To address this issue, we designed a distant IOT based screen framework, that considers observing of numerous Corona virus patients over the web. The system uses temperature sensor, respiratory sensor and pulse oximeter to measure the health parameters of the patients. If any oddity is detected in patient’s health, the patient presses the emergency help button which we installed in our system. This will alert the doctor and the care taker over IOT remotely. Our system thus provides a safe health monitoring design, in order to prevent the disease spreading through Corona virus and monitoring the individual health of each patient.
{"title":"Covid Patient Health Monitoring Using IoT During Quarantine","authors":"Lavanya Dhanesh, Meena.T, Chrisntha.B, Gayathri.S, Devapriya.M.D","doi":"10.3233/apc210264","DOIUrl":"https://doi.org/10.3233/apc210264","url":null,"abstract":"The term “COVID” is breaking the hearts of the entire human community. The Corona virus is more infectious and is exceptionally irresistible, it is vital to isolate the patients and yet the specialists need to screen Corona virus patients as well. With the expanding increase in the number of Corona cases, the doctors find it difficult to keep track on the medical issue of isolated patients. To address this issue, we designed a distant IOT based screen framework, that considers observing of numerous Corona virus patients over the web. The system uses temperature sensor, respiratory sensor and pulse oximeter to measure the health parameters of the patients. If any oddity is detected in patient’s health, the patient presses the emergency help button which we installed in our system. This will alert the doctor and the care taker over IOT remotely. Our system thus provides a safe health monitoring design, in order to prevent the disease spreading through Corona virus and monitoring the individual health of each patient.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129438108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mrs. D. Thamizhselvi, Ms. S. V. Supraja, Ms. G. Priyadharshini
The income of the famers has decreased drastically over the past years as they do not have the proper channel for marketing their produce. This has also proved to be the factor that favors the landlords and money lenders to gain possession over their agricultural products at a very low cost and obtain a large profit from it. This also reflects the inability of farmers to obtain the righteous profit from their produce. The main aim of our project is to organizing and uniting the farmers under one umbrella, to reduce the unbalanced accumulation of the profit from perishable farm produces for the traders and the sellers and help maximise the income level of the farmers by self-marketing. This system has been implemented by considering the entire supply-demand eco system and it also helps avoid product wastage.
{"title":"Help Farmers – Farm Era App","authors":"Mrs. D. Thamizhselvi, Ms. S. V. Supraja, Ms. G. Priyadharshini","doi":"10.3233/apc210184","DOIUrl":"https://doi.org/10.3233/apc210184","url":null,"abstract":"The income of the famers has decreased drastically over the past years as they do not have the proper channel for marketing their produce. This has also proved to be the factor that favors the landlords and money lenders to gain possession over their agricultural products at a very low cost and obtain a large profit from it. This also reflects the inability of farmers to obtain the righteous profit from their produce. The main aim of our project is to organizing and uniting the farmers under one umbrella, to reduce the unbalanced accumulation of the profit from perishable farm produces for the traders and the sellers and help maximise the income level of the farmers by self-marketing. This system has been implemented by considering the entire supply-demand eco system and it also helps avoid product wastage.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133267939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abhishek Kumar, Rini Dey, G. Madhukar Rao, Saravanan Pitchai, K. Vengatesan, V. D. Ambeth Kumar
This paper proposes the and justify how we can enhance the quality of medical education through immersive learning and AI (Artificial Intelligence) use in education. A Multimodal Approach for Immersive Teaching and learning through Animation, AR (Augmented Reality) & VR (Virtual Reality) is aimed at providing specifically medical students with knowledge, skills, and understanding. It is important to understand the current challenge involved in medical education. This paper reports the findings of a novel study on the technology enable teaching with Animation, AR and VR by and MR impact. A case study was conducted involving 521 participants from different states of India. The data was analyzed by their feedback after using this Virtual reality-based teaching procedure in classroom. Recommendations from this paper that are expected to effectively improving the quality of medical education in faster way.
{"title":"3D Animation and Virtual Reality Integrated Cognitive Computing for Teaching and Learning in Higher Education","authors":"Abhishek Kumar, Rini Dey, G. Madhukar Rao, Saravanan Pitchai, K. Vengatesan, V. D. Ambeth Kumar","doi":"10.3233/apc210252","DOIUrl":"https://doi.org/10.3233/apc210252","url":null,"abstract":"This paper proposes the and justify how we can enhance the quality of medical education through immersive learning and AI (Artificial Intelligence) use in education. A Multimodal Approach for Immersive Teaching and learning through Animation, AR (Augmented Reality) & VR (Virtual Reality) is aimed at providing specifically medical students with knowledge, skills, and understanding. It is important to understand the current challenge involved in medical education. This paper reports the findings of a novel study on the technology enable teaching with Animation, AR and VR by and MR impact. A case study was conducted involving 521 participants from different states of India. The data was analyzed by their feedback after using this Virtual reality-based teaching procedure in classroom. Recommendations from this paper that are expected to effectively improving the quality of medical education in faster way.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116395165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Sreedevi, G. Vijay Kumar, K. Kiran Kumar, B. Aruna, Arvind Yadav
Social networking sites will attract millions of users around the globe. Internet media is becoming popular for news consumption because of its ease, simple access and fast spreading of data takes to consume news from social media. Fake news on social media is making an appearance that is attracting a huge attention. This kind of situation could bring a great conflict in real time. The false news impacts extremely negative on society, particularly in social, commercial, political world, also on individuals. Hence detection of fake news on social media became one of the emerging research topic and technically challenging task due to availability of tools on social media. In this paper various machine learning techniques are used to predict fake news on twitter data. The results shown by using these techniques are more accurate with better performance.
{"title":"Prediction of Fake Tweets Using Machine Learning Algorithms","authors":"M. Sreedevi, G. Vijay Kumar, K. Kiran Kumar, B. Aruna, Arvind Yadav","doi":"10.3233/apc210195","DOIUrl":"https://doi.org/10.3233/apc210195","url":null,"abstract":"Social networking sites will attract millions of users around the globe. Internet media is becoming popular for news consumption because of its ease, simple access and fast spreading of data takes to consume news from social media. Fake news on social media is making an appearance that is attracting a huge attention. This kind of situation could bring a great conflict in real time. The false news impacts extremely negative on society, particularly in social, commercial, political world, also on individuals. Hence detection of fake news on social media became one of the emerging research topic and technically challenging task due to availability of tools on social media. In this paper various machine learning techniques are used to predict fake news on twitter data. The results shown by using these techniques are more accurate with better performance.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133899131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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