Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574664
Naman Bhoj, Mayank Khari, Bishwajeet K. Pandey
With an exponential rise in the number of cases of Covid-19, researchers have been painstakingly focused towards developing an effective vaccine. Consequently, there has been ongoing discussion about the vaccine on the social media platform filled with positive and negative sentiments. In this paper, we narrow down our research space by focusing on only identifying tweets imparting negative sentiment towards vaccines on social media. This identification model holds vital importance for government and medical agencies as it can help them analyse the possible reasons or causes behind the negative sentiment via tweets. Empirical results of the experiments conducted in this paper indicated that Support Vector Machine is best suited to identify negative tweets on a balanced dataset with the highest F1-Score of 87.179, and K-Nearest Neighbour shows the highest improvement after mitigating class imbalance using Edited Nearest Neighbour, which indicates the class dependency of distance based methods.
{"title":"Improved Identification of Negative Tweets related to Covid-19 Vaccination by Mitigating Class Imbalance","authors":"Naman Bhoj, Mayank Khari, Bishwajeet K. Pandey","doi":"10.1109/CICN51697.2021.9574664","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574664","url":null,"abstract":"With an exponential rise in the number of cases of Covid-19, researchers have been painstakingly focused towards developing an effective vaccine. Consequently, there has been ongoing discussion about the vaccine on the social media platform filled with positive and negative sentiments. In this paper, we narrow down our research space by focusing on only identifying tweets imparting negative sentiment towards vaccines on social media. This identification model holds vital importance for government and medical agencies as it can help them analyse the possible reasons or causes behind the negative sentiment via tweets. Empirical results of the experiments conducted in this paper indicated that Support Vector Machine is best suited to identify negative tweets on a balanced dataset with the highest F1-Score of 87.179, and K-Nearest Neighbour shows the highest improvement after mitigating class imbalance using Edited Nearest Neighbour, which indicates the class dependency of distance based methods.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125419675","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}
Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574684
Nitesh Bharti, Shahab Nadeem Hashmi, V. Manikandan
Due to the coronavirus disease (COVID-19) pandemic, most of the public work is carrying out online. Universities all around the globe moved to online education, job interviews are mainly conducting online, many first-level health consultations are happening online, and companies hold periodic meetings entirely online. Google Meet, Microsoft Team, and other online meeting software applications are widely accessible on the market. In this work, we are addressing a topic that has a lot of practical applications. In this paper, we present a method that takes a recorded video as an input and generates a written and/or audio summary of the same as an output. The suggested method can also be used to generate lecture notes from lecture videos, meeting minutes, subtitles, or storyline production from entertainment videos, among several other things. The suggested system takes the video's audio track, which is then transformed to text. In addition, we created the text summary utilising text summarising algorithms. The system's users have the option of using the text summary or creating an audio output that matches the text summary. The proposed method is implemented in Python, and the proposed scheme is evaluated using short videos acquired from YouTube. Since there is no benchmark measure for evaluating the efficiency and there is no specific dataset available for the relevant study, the proposed method is manually validated on the downloaded video set.
{"title":"An Approach for Audio/Text Summary Generation from Webinars/Online Meetings","authors":"Nitesh Bharti, Shahab Nadeem Hashmi, V. Manikandan","doi":"10.1109/CICN51697.2021.9574684","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574684","url":null,"abstract":"Due to the coronavirus disease (COVID-19) pandemic, most of the public work is carrying out online. Universities all around the globe moved to online education, job interviews are mainly conducting online, many first-level health consultations are happening online, and companies hold periodic meetings entirely online. Google Meet, Microsoft Team, and other online meeting software applications are widely accessible on the market. In this work, we are addressing a topic that has a lot of practical applications. In this paper, we present a method that takes a recorded video as an input and generates a written and/or audio summary of the same as an output. The suggested method can also be used to generate lecture notes from lecture videos, meeting minutes, subtitles, or storyline production from entertainment videos, among several other things. The suggested system takes the video's audio track, which is then transformed to text. In addition, we created the text summary utilising text summarising algorithms. The system's users have the option of using the text summary or creating an audio output that matches the text summary. The proposed method is implemented in Python, and the proposed scheme is evaluated using short videos acquired from YouTube. Since there is no benchmark measure for evaluating the efficiency and there is no specific dataset available for the relevant study, the proposed method is manually validated on the downloaded video set.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122146598","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}
Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574693
G. Soni
The various advanced applications of 5G based wireless communications include autonomous self-driven cars, telemedicine, smart spaces (e.g., home, office, etc.), sensor networks, high speed trains, smart cities, and many more [9]. For such data intensive wireless communications, only radio frequency (RF) based wireless systems cannot meet the desired demands because RF band is susceptible to interference, has limited capacity, and requires a heavy license fee to use the spectrum [10]. Hence, other portions of the electromagnetic (EM) spectrum and new technologies are required to be considered for fulfilling the demands of wireless communication systems in the near future. FSO, an OWe-based optical wireless communication system, is one such alternate option. Increased bandwidth demands may be met using free space optical communication or wireless optics, which are both last mile options. The FSO transmits and receives multimedia data using an LED or LASER beam as a high data rate optical link. FSO may be installed for a quarter of the cost of fibre, but communication between the transmitter and receiver must be Line of Sight (LOS). The FSO not only has many advantages but also hampered by some atmospheric conditions, which degrades the link performance. This paper reviews the FSO link design and effect of different atmospheric condition like- fog, scintillation, turbulence, rain etc. In this research paper, the simulation based on optsim to carry out the performance investigation FSO link in investigation by varying the FSO beam divergence angle is being carried out.
{"title":"Performance Investigation of Free Space Optics Link Using Beam Divergence","authors":"G. Soni","doi":"10.1109/CICN51697.2021.9574693","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574693","url":null,"abstract":"The various advanced applications of 5G based wireless communications include autonomous self-driven cars, telemedicine, smart spaces (e.g., home, office, etc.), sensor networks, high speed trains, smart cities, and many more [9]. For such data intensive wireless communications, only radio frequency (RF) based wireless systems cannot meet the desired demands because RF band is susceptible to interference, has limited capacity, and requires a heavy license fee to use the spectrum [10]. Hence, other portions of the electromagnetic (EM) spectrum and new technologies are required to be considered for fulfilling the demands of wireless communication systems in the near future. FSO, an OWe-based optical wireless communication system, is one such alternate option. Increased bandwidth demands may be met using free space optical communication or wireless optics, which are both last mile options. The FSO transmits and receives multimedia data using an LED or LASER beam as a high data rate optical link. FSO may be installed for a quarter of the cost of fibre, but communication between the transmitter and receiver must be Line of Sight (LOS). The FSO not only has many advantages but also hampered by some atmospheric conditions, which degrades the link performance. This paper reviews the FSO link design and effect of different atmospheric condition like- fog, scintillation, turbulence, rain etc. In this research paper, the simulation based on optsim to carry out the performance investigation FSO link in investigation by varying the FSO beam divergence angle is being carried out.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129945311","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}
Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574669
Joel Bejar Mallma, Ciro Rodríguez, Yuri Pomachagua, C. Navarro
Plant leaf diseases usually affect agriculture a lot, which is one of the important sources of income for people, so diseases must be detected and recognized quickly and effectively. The research aims to identify these diseases automatically using a model based on deep learning known as convolutional neural networks and the K-means algorithm. The methodology applied for the detection, three previously trained networks, VGG16, VGG19, and ResNet50, were used for the extraction of characteristics, the principal component analysis algorithm was also used to reduce dimensionality, and finally, the K-means algorithm classification. The training of the models was carried out with the use of a Kaggle open database of 7771 images which contain 38 types of diseases and healthy leaves. VGG16, VGG19, and ResNet50 were trained where the accuracy of 97.43%, 98.35%, and 98.38% was obtained. The precision obtained with the VGG16 hybrid model and the K-means algorithm was 96.26%. Therefore, the hybrid model is effective for the identification of plant diseases.
{"title":"Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms","authors":"Joel Bejar Mallma, Ciro Rodríguez, Yuri Pomachagua, C. Navarro","doi":"10.1109/CICN51697.2021.9574669","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574669","url":null,"abstract":"Plant leaf diseases usually affect agriculture a lot, which is one of the important sources of income for people, so diseases must be detected and recognized quickly and effectively. The research aims to identify these diseases automatically using a model based on deep learning known as convolutional neural networks and the K-means algorithm. The methodology applied for the detection, three previously trained networks, VGG16, VGG19, and ResNet50, were used for the extraction of characteristics, the principal component analysis algorithm was also used to reduce dimensionality, and finally, the K-means algorithm classification. The training of the models was carried out with the use of a Kaggle open database of 7771 images which contain 38 types of diseases and healthy leaves. VGG16, VGG19, and ResNet50 were trained where the accuracy of 97.43%, 98.35%, and 98.38% was obtained. The precision obtained with the VGG16 hybrid model and the K-means algorithm was 96.26%. Therefore, the hybrid model is effective for the identification of plant diseases.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128856921","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}
One of the major factors that an author thinks while publishing an article is about getting high impact on the article. Impact of an article is wide and this makes the influence for making challenges to get new ideas and development. An author by knowing the impact of an article can increase the visibility and enhances the influence of published research. It improves the quality and standard of the article. Sometimes citation count can also lead to the impact of an article. Citation count refers to the number of citations established by an article. This research deals with the aim that how to increase the impact of the article to get more citations. Experimental results clearly shows that how the article visibility and the citations can be increased with different performance metrics.
{"title":"An Empirical Study on Impact of News Articles","authors":"Shaik Himani, Mugada Hemanth Kumar, M. Enduri, Shaik Shakila Begum, Gundla Rageswari, Satish Anamalamudi","doi":"10.1109/CICN51697.2021.9574670","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574670","url":null,"abstract":"One of the major factors that an author thinks while publishing an article is about getting high impact on the article. Impact of an article is wide and this makes the influence for making challenges to get new ideas and development. An author by knowing the impact of an article can increase the visibility and enhances the influence of published research. It improves the quality and standard of the article. Sometimes citation count can also lead to the impact of an article. Citation count refers to the number of citations established by an article. This research deals with the aim that how to increase the impact of the article to get more citations. Experimental results clearly shows that how the article visibility and the citations can be increased with different performance metrics.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122331820","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}
Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574695
Vasavi Avula, Rayapati Nanditha, Sateeshkrishna Dhuli, P. Ranjan
Internet of Things (IoT) is a powerful data network comprising of various objects such as sensors, radio frequency components, smart appliances, and computers that can be connected via the Internet. The Internet of Everything (IoE) is an evolution of IoT, and it is considered as a combination of data, people, process, and physical devices. Recently, IoE has drawn significant attention from research community due to its wide variety of potential applications. This paper contemplates the studies of state-of-art of IoE, which includes the IoE paradigm, Applications, Challenges, Advantages, and Disadvantages. We also discuss the sensors and the micro-controllers for IoE. This survey article is intended to serve as a guideline for research and development in the IoE.
{"title":"The Internet Of Everything: A Survey","authors":"Vasavi Avula, Rayapati Nanditha, Sateeshkrishna Dhuli, P. Ranjan","doi":"10.1109/CICN51697.2021.9574695","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574695","url":null,"abstract":"Internet of Things (IoT) is a powerful data network comprising of various objects such as sensors, radio frequency components, smart appliances, and computers that can be connected via the Internet. The Internet of Everything (IoE) is an evolution of IoT, and it is considered as a combination of data, people, process, and physical devices. Recently, IoE has drawn significant attention from research community due to its wide variety of potential applications. This paper contemplates the studies of state-of-art of IoE, which includes the IoE paradigm, Applications, Challenges, Advantages, and Disadvantages. We also discuss the sensors and the micro-controllers for IoE. This survey article is intended to serve as a guideline for research and development in the IoE.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121215963","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}
Pub Date : 2021-09-22DOI: 10.1109/cicn51697.2021.9574666
{"title":"Welcome from CICN 2021 General Chair","authors":"","doi":"10.1109/cicn51697.2021.9574666","DOIUrl":"https://doi.org/10.1109/cicn51697.2021.9574666","url":null,"abstract":"","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"83 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131376744","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}
Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574672
R. R. Sarkar, Amitabha Chakrabarty, Mohammad Zahidur Rahman
Vehicular ad-hoc networks (VANETs) have drawn the attention of the researcher and erects an auspicious research interest. Applying routing protocols in VANET has become challenging as VANET has unique and dynamic properties and the mobility of nodes. In this work, the routing protocols for VANET's (AODV, DSDV, DSR, and OLSR) are applied in Real-World mobility tracing and their performance is analyzed on packet receive, packet receives rate, Packet loss ratio, and packet delivery ratio. This Real-World Vehicular Mobility is traced from a part of Dhaka city, Bangladesh. The simulation is done by SUMO and NS3 simulators. As a propagation loss model in this simulation, Two Ray Ground and Friis Propagation loss models are considered. When the Friis propagation loss model is applied in the simulation environment along with the real-world vehicular mobility, it results in that routing protocols especially OLSR achieves a good value of receives rate and packet received. In the case of PDR, almost all the routing protocols have a good value. Among these routing protocols, AODV has performed best and achieved an excellent level of PDR. On the other hand, in the Two Ray Ground propagation loss model, almost all the routing protocols have a very low value of packet loss ratio excepts AODV.
{"title":"VANET Routing Protocols in Real-World Mobility Tracing","authors":"R. R. Sarkar, Amitabha Chakrabarty, Mohammad Zahidur Rahman","doi":"10.1109/CICN51697.2021.9574672","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574672","url":null,"abstract":"Vehicular ad-hoc networks (VANETs) have drawn the attention of the researcher and erects an auspicious research interest. Applying routing protocols in VANET has become challenging as VANET has unique and dynamic properties and the mobility of nodes. In this work, the routing protocols for VANET's (AODV, DSDV, DSR, and OLSR) are applied in Real-World mobility tracing and their performance is analyzed on packet receive, packet receives rate, Packet loss ratio, and packet delivery ratio. This Real-World Vehicular Mobility is traced from a part of Dhaka city, Bangladesh. The simulation is done by SUMO and NS3 simulators. As a propagation loss model in this simulation, Two Ray Ground and Friis Propagation loss models are considered. When the Friis propagation loss model is applied in the simulation environment along with the real-world vehicular mobility, it results in that routing protocols especially OLSR achieves a good value of receives rate and packet received. In the case of PDR, almost all the routing protocols have a good value. Among these routing protocols, AODV has performed best and achieved an excellent level of PDR. On the other hand, in the Two Ray Ground propagation loss model, almost all the routing protocols have a very low value of packet loss ratio excepts AODV.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123386588","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}
Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574682
Augusto Hayashida Marchinares, C. Rodriguez
Project Portfolio Management is relevant for the growth of companies since it favors planning. Project Portfolio Management manages the resources to plan, control, and execute projects and obtain the strategic objectives of the organizations. In Project Portfolio Management, a large amount of data is forged, important for planning new projects in companies; therefore, the need arises to create models that help process and interpret the data. In this context, Machine Learning is presented as a technological enabler that allows a system, by itself and in an automated way, to learn to discover trends, patterns, and relationships between data; it is an engine of digital transformation of business and that organizations are embracing. Therefore, this article aims to compile and review proposals made to implement machine learning in the management of the project portfolio and apply algorithms that allow the development of models that help in the management and evaluation of projects to be developed in a Software Factory. The CRISP-DM methodology is applied to process the data of costs, times, and types of Projects; the Python programming language is used, the dataset corresponds to a Software Factory. The results validate the models implemented using Machine Learning algorithms, such as regression and decision trees, and thereby obtain the best model for predictions, establishing the correlation between variables and the benefit to be achieved. It is concluded, the implementation of Machine Learning improves the IT Project Portfolio Management, helping to identify which projects are more profitable and beneficial.
{"title":"Online Solution Based on Machine Learning for IT Project Management in Software Factory Companies","authors":"Augusto Hayashida Marchinares, C. Rodriguez","doi":"10.1109/CICN51697.2021.9574682","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574682","url":null,"abstract":"Project Portfolio Management is relevant for the growth of companies since it favors planning. Project Portfolio Management manages the resources to plan, control, and execute projects and obtain the strategic objectives of the organizations. In Project Portfolio Management, a large amount of data is forged, important for planning new projects in companies; therefore, the need arises to create models that help process and interpret the data. In this context, Machine Learning is presented as a technological enabler that allows a system, by itself and in an automated way, to learn to discover trends, patterns, and relationships between data; it is an engine of digital transformation of business and that organizations are embracing. Therefore, this article aims to compile and review proposals made to implement machine learning in the management of the project portfolio and apply algorithms that allow the development of models that help in the management and evaluation of projects to be developed in a Software Factory. The CRISP-DM methodology is applied to process the data of costs, times, and types of Projects; the Python programming language is used, the dataset corresponds to a Software Factory. The results validate the models implemented using Machine Learning algorithms, such as regression and decision trees, and thereby obtain the best model for predictions, establishing the correlation between variables and the benefit to be achieved. It is concluded, the implementation of Machine Learning improves the IT Project Portfolio Management, helping to identify which projects are more profitable and beneficial.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129495465","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}
Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574677
Naman Bhoj, Adarsh Raj Dwivedi, Alpika Tripathi, Bishwajeet K. Pandey
Clickbait content on online platforms, is exaggerating content that doesn't deliver what it promises. The main motive of such content is to mislead the reader to “click” on them. These are widely responsible for delivering false information to the user and damaging their online experience. Many online creators deliberately use them to get more views and generate more revenue. In light of potential difficulties created by clickbait content, this paper aims to create a clickbait detection model for entertainment and news websites utilizing the power of the machine and deep learning models. Empirical results of our experiments indicate that LSTM models are best suited for identifying clickbait content containing text by achieving an accuracy of 95.031 % which is 1.138 times greater than the Random Forest and 1.709 times greater than the Naive Bayes model.
{"title":"LSTM Powered Identification of Clickbait Content on Entertainment and News Websites","authors":"Naman Bhoj, Adarsh Raj Dwivedi, Alpika Tripathi, Bishwajeet K. Pandey","doi":"10.1109/CICN51697.2021.9574677","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574677","url":null,"abstract":"Clickbait content on online platforms, is exaggerating content that doesn't deliver what it promises. The main motive of such content is to mislead the reader to “click” on them. These are widely responsible for delivering false information to the user and damaging their online experience. Many online creators deliberately use them to get more views and generate more revenue. In light of potential difficulties created by clickbait content, this paper aims to create a clickbait detection model for entertainment and news websites utilizing the power of the machine and deep learning models. Empirical results of our experiments indicate that LSTM models are best suited for identifying clickbait content containing text by achieving an accuracy of 95.031 % which is 1.138 times greater than the Random Forest and 1.709 times greater than the Naive Bayes model.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128782895","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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