Pub Date : 2020-10-30DOI: 10.1080/24751839.2020.1833141
D. Mai, Trong Hop Dang, L. Ngo
ABSTRACT An interval type-2 fuzzy logic system (IT2FLS) can function well with uncertain data, with which a type-1 fuzzy logic system (T1FLS) is ineffective because its membership function rests upon crisp values. However, similar to T1FLSs, there are challenges associated with IT2FLSs in selecting parameters, which can significantly affect the accuracy of the classification results with their relatively high sensitivity. This paper discusses and proposes a hybrid model based on IT2FLS and particle swarm optimization (PSO) for prediction problems. The main objective of this paper is to find the optimal solution for the unknown fuzzy systems using labelled data for training the fuzzy system. The PSO technique was used to find the optimal parameters of the Gaussian membership functions which utilized for IT2FLSs. The authors tested two data sets for each of the two prediction problems, namely: burnt forest area prediction and wine quality prediction. The predictive results were compared with other predictive methods including random forest (RF), support vector machines (SVM), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and IT2FLS with parameters generated by using the fuzzy c-means algorithm (IT2FLS-FCM). Experiment results showed that the proposed method could significantly improve accuracy compared to several other predictive techniques.
{"title":"Optimization of interval type-2 fuzzy system using the PSO technique for predictive problems","authors":"D. Mai, Trong Hop Dang, L. Ngo","doi":"10.1080/24751839.2020.1833141","DOIUrl":"https://doi.org/10.1080/24751839.2020.1833141","url":null,"abstract":"ABSTRACT An interval type-2 fuzzy logic system (IT2FLS) can function well with uncertain data, with which a type-1 fuzzy logic system (T1FLS) is ineffective because its membership function rests upon crisp values. However, similar to T1FLSs, there are challenges associated with IT2FLSs in selecting parameters, which can significantly affect the accuracy of the classification results with their relatively high sensitivity. This paper discusses and proposes a hybrid model based on IT2FLS and particle swarm optimization (PSO) for prediction problems. The main objective of this paper is to find the optimal solution for the unknown fuzzy systems using labelled data for training the fuzzy system. The PSO technique was used to find the optimal parameters of the Gaussian membership functions which utilized for IT2FLSs. The authors tested two data sets for each of the two prediction problems, namely: burnt forest area prediction and wine quality prediction. The predictive results were compared with other predictive methods including random forest (RF), support vector machines (SVM), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and IT2FLS with parameters generated by using the fuzzy c-means algorithm (IT2FLS-FCM). Experiment results showed that the proposed method could significantly improve accuracy compared to several other predictive techniques.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"197 - 213"},"PeriodicalIF":2.7,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1833141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49052580","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 : 2020-10-21DOI: 10.1080/24751839.2020.1833137
E. Okafor, D. Udekwe, Y. Ibrahim, M. B. Mu'azu, E. Okafor
ABSTRACT The manual tuning of controller parameters, for example, tuning proportional integral derivative (PID) gains often relies on tedious human engineering. To curb the aforementioned problem, we propose an artificial intelligence-based deep reinforcement learning (RL) PID controller (three variants) compared with genetic algorithm-based PID (GA-PID) and classical PID; a total of five controllers were simulated for controlling and trajectory tracking of the ball dynamics in a linearized ball-and-plate ( ) system. For the experiments, we trained novel variants of deep RL-PID built from a customized deep deterministic policy gradient (DDPG) agent (by modifying the neural network architecture), resulting in two new RL agents (DDPG-FC-350-R-PID & DDPG-FC-350-E-PID). Each of the agents interacts with the environment through a policy and a learning algorithm to produce a set of actions (optimal PID gains). Additionally, we evaluated the five controllers to assess which method provides the best performance metrics in the context of the minimum index in predictive errors, steady-state-error, peak overshoot, and time-responses. The results show that our proposed architecture (DDPG-FC-350-E-PID) yielded the best performance and surpasses all other approaches on most of the evaluation metric indices. Furthermore, an appropriate training of an artificial intelligence-based controller can aid to obtain the best path tracking.
{"title":"Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system","authors":"E. Okafor, D. Udekwe, Y. Ibrahim, M. B. Mu'azu, E. Okafor","doi":"10.1080/24751839.2020.1833137","DOIUrl":"https://doi.org/10.1080/24751839.2020.1833137","url":null,"abstract":"ABSTRACT The manual tuning of controller parameters, for example, tuning proportional integral derivative (PID) gains often relies on tedious human engineering. To curb the aforementioned problem, we propose an artificial intelligence-based deep reinforcement learning (RL) PID controller (three variants) compared with genetic algorithm-based PID (GA-PID) and classical PID; a total of five controllers were simulated for controlling and trajectory tracking of the ball dynamics in a linearized ball-and-plate ( ) system. For the experiments, we trained novel variants of deep RL-PID built from a customized deep deterministic policy gradient (DDPG) agent (by modifying the neural network architecture), resulting in two new RL agents (DDPG-FC-350-R-PID & DDPG-FC-350-E-PID). Each of the agents interacts with the environment through a policy and a learning algorithm to produce a set of actions (optimal PID gains). Additionally, we evaluated the five controllers to assess which method provides the best performance metrics in the context of the minimum index in predictive errors, steady-state-error, peak overshoot, and time-responses. The results show that our proposed architecture (DDPG-FC-350-E-PID) yielded the best performance and surpasses all other approaches on most of the evaluation metric indices. Furthermore, an appropriate training of an artificial intelligence-based controller can aid to obtain the best path tracking.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"179 - 196"},"PeriodicalIF":2.7,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1833137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49203932","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 : 2020-10-10DOI: 10.1080/24751839.2020.1829388
Raluca Chitic, Franck Leprévost, Nicolas Bernard
ABSTRACT The range of applications of Neural Networks encompasses image classification. However, Neural Networks are vulnerable to attacks, and may misclassify adversarial images, leading to potentially disastrous consequences. Pursuing some of our previous work, we provide an extended proof of concept of a black-box, targeted, non-parametric attack using evolutionary algorithms to fool both Neural Networks and humans at the task of image classification. Our feasibility study is performed on VGG-16 trained on CIFAR-10. For any category of CIFAR-10, one chooses an image classified by VGG-16 as belonging to . From there, two scenarios are addressed. In the first scenario, a target category is fixed a priori. We construct an evolutionary algorithm that evolves to a modified image that VGG-16 classifies as belonging to . In the second scenario, we construct another evolutionary algorithm that evolves to a modified image that VGG-16 is unable to classify. In both scenarios, the obtained adversarial images remain so close to the original one that a human would likely classify them as still belonging to .
{"title":"Evolutionary algorithms deceive humans and machines at image classification: an extended proof of concept on two scenarios","authors":"Raluca Chitic, Franck Leprévost, Nicolas Bernard","doi":"10.1080/24751839.2020.1829388","DOIUrl":"https://doi.org/10.1080/24751839.2020.1829388","url":null,"abstract":"ABSTRACT The range of applications of Neural Networks encompasses image classification. However, Neural Networks are vulnerable to attacks, and may misclassify adversarial images, leading to potentially disastrous consequences. Pursuing some of our previous work, we provide an extended proof of concept of a black-box, targeted, non-parametric attack using evolutionary algorithms to fool both Neural Networks and humans at the task of image classification. Our feasibility study is performed on VGG-16 trained on CIFAR-10. For any category of CIFAR-10, one chooses an image classified by VGG-16 as belonging to . From there, two scenarios are addressed. In the first scenario, a target category is fixed a priori. We construct an evolutionary algorithm that evolves to a modified image that VGG-16 classifies as belonging to . In the second scenario, we construct another evolutionary algorithm that evolves to a modified image that VGG-16 is unable to classify. In both scenarios, the obtained adversarial images remain so close to the original one that a human would likely classify them as still belonging to .","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"121 - 143"},"PeriodicalIF":2.7,"publicationDate":"2020-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1829388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44822763","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 : 2020-10-04DOI: 10.1080/24751839.2020.1824153
Yu Agusa, T. Fujihashi, Keiichi Endo, H. Kuroda, Shin-ya Kobayashi
ABSTRACT Obtaining accurate information about the seawater temperature and visualizing it in real time is of great importance for aquaculture fishers, as water temperature fluctuations due to tide inflows greatly affect the development of fish and shellfish. Fishery researchers install a lot of multiple depth water temperature continuous observation equipment in the sea, and then, they collect and accumulate the data on the seawater temperature to realize its visualization. In this research, we develop a novel system to visualize the current conditions of seawater and its variations over time based on the collected water temperature data. Our system allows representing the most recent seawater temperature information and its variation over time in the forms of a table, graph and a three-dimensional temperature distribution chart through a web browser. In addition, in this system, it is possible to download the seawater temperature information measured during a period in the past. Our system can be used to improve the efficiency of feeding, thereby reducing the costs associated with aquaculture maintenance; moreover, it can serve to avoid damages caused by red tide and to prevent fish diseases.
{"title":"Development of seawater temperature announcement system for improving efficiency of fishery industry","authors":"Yu Agusa, T. Fujihashi, Keiichi Endo, H. Kuroda, Shin-ya Kobayashi","doi":"10.1080/24751839.2020.1824153","DOIUrl":"https://doi.org/10.1080/24751839.2020.1824153","url":null,"abstract":"ABSTRACT Obtaining accurate information about the seawater temperature and visualizing it in real time is of great importance for aquaculture fishers, as water temperature fluctuations due to tide inflows greatly affect the development of fish and shellfish. Fishery researchers install a lot of multiple depth water temperature continuous observation equipment in the sea, and then, they collect and accumulate the data on the seawater temperature to realize its visualization. In this research, we develop a novel system to visualize the current conditions of seawater and its variations over time based on the collected water temperature data. Our system allows representing the most recent seawater temperature information and its variation over time in the forms of a table, graph and a three-dimensional temperature distribution chart through a web browser. In addition, in this system, it is possible to download the seawater temperature information measured during a period in the past. Our system can be used to improve the efficiency of feeding, thereby reducing the costs associated with aquaculture maintenance; moreover, it can serve to avoid damages caused by red tide and to prevent fish diseases.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"62 - 82"},"PeriodicalIF":2.7,"publicationDate":"2020-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1824153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45136963","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 : 2020-10-01DOI: 10.1080/24751839.2020.1824154
Christopher Medrano-Berumen, M. Akbaş
ABSTRACT The autonomous vehicle technology is considered as a significant market disruptor for multiple industries. However, to reach this potential and to be accepted by the public, autonomous vehicles must be proven to be reliable and safe. Therefore, validation is essential for improving the public trust for autonomous vehicles and deploying them for everyday transportation activities. There have been a number of significant efforts on validation of autonomous vehicles; and real-life testing and test tracks have been the major platforms for these activities. However, simulation has also been gaining popularity due to its advantages in cost, time and safety. In this paper, we present a simulation scenario generation methodology with pseudo-random test generation to validate the decision-making system of autonomous vehicles. The methodology separates the validation concerns and focuses on generating scenarios that test the decisions taken by the vehicle. The implementation demonstrates the capabilities and the efficiency of the approach.
{"title":"Validation of decision-making in artificial intelligence-based autonomous vehicles","authors":"Christopher Medrano-Berumen, M. Akbaş","doi":"10.1080/24751839.2020.1824154","DOIUrl":"https://doi.org/10.1080/24751839.2020.1824154","url":null,"abstract":"ABSTRACT The autonomous vehicle technology is considered as a significant market disruptor for multiple industries. However, to reach this potential and to be accepted by the public, autonomous vehicles must be proven to be reliable and safe. Therefore, validation is essential for improving the public trust for autonomous vehicles and deploying them for everyday transportation activities. There have been a number of significant efforts on validation of autonomous vehicles; and real-life testing and test tracks have been the major platforms for these activities. However, simulation has also been gaining popularity due to its advantages in cost, time and safety. In this paper, we present a simulation scenario generation methodology with pseudo-random test generation to validate the decision-making system of autonomous vehicles. The methodology separates the validation concerns and focuses on generating scenarios that test the decisions taken by the vehicle. The implementation demonstrates the capabilities and the efficiency of the approach.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"83 - 103"},"PeriodicalIF":2.7,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1824154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47935353","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 : 2020-09-29DOI: 10.1080/24751839.2020.1819634
Swagatika Sahoo, Raju Halder
ABSTRACT In the era of big data, modern data marketplaces have received much attention as they allow not only large enterprises but also individuals to trade their data. This new paradigm makes the data prone to various threats, including piracy, illegal reselling, tampering, illegal redistribution, ownership claiming, forgery, theft, misappropriation, etc. Although digital watermarking is a promising technique to address the above-mentioned challenges, the existing solutions in the literature are deemed to be incompetent in big data scenarios due to the following factors: V's of big data, involvement of multiple owners, incremental watermarking, large cover-size and limited watermark-capacity, non-interference, etc. In this paper, we propose a novel big data watermarking technique that leverages the power of blockchain technology and provides a transparent immutable audit trail for data movement in big data monetizing scenarios. In this context, we address all the crucial challenges mentioned above. We present a prototype implementation of the system as a proof of concept using Solidity on Ethereum platform, and we perform experimental evaluation to demonstrate its feasibility and effectiveness in terms of execution gas costs. To the best of our knowledge, this is the first proposal which deals with watermarking issues in the context of big data.
{"title":"Traceability and ownership claim of data on big data marketplace using blockchain technology","authors":"Swagatika Sahoo, Raju Halder","doi":"10.1080/24751839.2020.1819634","DOIUrl":"https://doi.org/10.1080/24751839.2020.1819634","url":null,"abstract":"ABSTRACT In the era of big data, modern data marketplaces have received much attention as they allow not only large enterprises but also individuals to trade their data. This new paradigm makes the data prone to various threats, including piracy, illegal reselling, tampering, illegal redistribution, ownership claiming, forgery, theft, misappropriation, etc. Although digital watermarking is a promising technique to address the above-mentioned challenges, the existing solutions in the literature are deemed to be incompetent in big data scenarios due to the following factors: V's of big data, involvement of multiple owners, incremental watermarking, large cover-size and limited watermark-capacity, non-interference, etc. In this paper, we propose a novel big data watermarking technique that leverages the power of blockchain technology and provides a transparent immutable audit trail for data movement in big data monetizing scenarios. In this context, we address all the crucial challenges mentioned above. We present a prototype implementation of the system as a proof of concept using Solidity on Ethereum platform, and we perform experimental evaluation to demonstrate its feasibility and effectiveness in terms of execution gas costs. To the best of our knowledge, this is the first proposal which deals with watermarking issues in the context of big data.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"35 - 61"},"PeriodicalIF":2.7,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1819634","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60140746","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 : 2020-09-24DOI: 10.1080/24751839.2020.1819633
A. Jebali, S. Sassi, A. Jemai
ABSTRACT With the emergence of cloud computing paradigms, secure data outsourcing has become one of the crucial challenges which strongly imposes itself. Data owners place their data among cloud service providers in order to increase flexibility, optimize storage, enhance data manipulation and decrease processing time. Nevertheless, from a security point of view, access control is a major challenge in this situation seeing that the security policy of the data owner must be preserved when data is moved to the cloud. Nonetheless, the lack of a comprehensive and systematic review motivated us to construct this reviewing paper on this research problem. Here, we discuss current and emerging research on privacy and confidentiality concerns in data outsourcing and pinpoint potential issues that are still unresolved.
{"title":"Secure data outsourcing in presence of the inference problem: issues and directions","authors":"A. Jebali, S. Sassi, A. Jemai","doi":"10.1080/24751839.2020.1819633","DOIUrl":"https://doi.org/10.1080/24751839.2020.1819633","url":null,"abstract":"ABSTRACT With the emergence of cloud computing paradigms, secure data outsourcing has become one of the crucial challenges which strongly imposes itself. Data owners place their data among cloud service providers in order to increase flexibility, optimize storage, enhance data manipulation and decrease processing time. Nevertheless, from a security point of view, access control is a major challenge in this situation seeing that the security policy of the data owner must be preserved when data is moved to the cloud. Nonetheless, the lack of a comprehensive and systematic review motivated us to construct this reviewing paper on this research problem. Here, we discuss current and emerging research on privacy and confidentiality concerns in data outsourcing and pinpoint potential issues that are still unresolved.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"16 - 34"},"PeriodicalIF":2.7,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1819633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46856338","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 : 2020-09-17DOI: 10.1080/24751839.2020.1774153
Kulsawasd Jitkajornwanich, Neelabh Pant, M. Fouladgar, R. Elmasri
ABSTRACT Spatio-temporal data serves as a foundation for most location-based applications nowadays. To handle spatio-temporal data, an appropriate methodology needs to be properly followed, in which space and time dimensions of data must be taken into account ‘altogether’ – unlike spatial (or temporal) data management tools which consider space (or time) separately and assumes no dependency on one another. In this paper, we conducted a survey on spatial, temporal, and spatio-temporal database research. Additionally, to use an original example to illustrate how today’s technologies can be used to handle spatio-temporal data and applications, we categorize the current technologies into two groups: (1) traditional, mainstay tools (e.g. SQL ecosystem) and (2) emerging, data-intensive tools (e.g. deep learning). Specifically, in the first group, we use our spatio-temporal application based on SQL system, ‘hydrological rainstorm analysis’, as an original example showing how analysis and mining tasks can be performed on the conceptual storm stored in a spatio-temporal RDB. In the second group, we use our spatio-temporal application based on deep learning, ‘users’ future locations prediction based on historical trajectory GPS data using hyper optimized ANNs and LSTMs’, as an original example showing how deep learning models can be applied to spatio-temporal data.
{"title":"A survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using SQL ecosystem and deep learning","authors":"Kulsawasd Jitkajornwanich, Neelabh Pant, M. Fouladgar, R. Elmasri","doi":"10.1080/24751839.2020.1774153","DOIUrl":"https://doi.org/10.1080/24751839.2020.1774153","url":null,"abstract":"ABSTRACT Spatio-temporal data serves as a foundation for most location-based applications nowadays. To handle spatio-temporal data, an appropriate methodology needs to be properly followed, in which space and time dimensions of data must be taken into account ‘altogether’ – unlike spatial (or temporal) data management tools which consider space (or time) separately and assumes no dependency on one another. In this paper, we conducted a survey on spatial, temporal, and spatio-temporal database research. Additionally, to use an original example to illustrate how today’s technologies can be used to handle spatio-temporal data and applications, we categorize the current technologies into two groups: (1) traditional, mainstay tools (e.g. SQL ecosystem) and (2) emerging, data-intensive tools (e.g. deep learning). Specifically, in the first group, we use our spatio-temporal application based on SQL system, ‘hydrological rainstorm analysis’, as an original example showing how analysis and mining tasks can be performed on the conceptual storm stored in a spatio-temporal RDB. In the second group, we use our spatio-temporal application based on deep learning, ‘users’ future locations prediction based on historical trajectory GPS data using hyper optimized ANNs and LSTMs’, as an original example showing how deep learning models can be applied to spatio-temporal data.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"4 1","pages":"524 - 559"},"PeriodicalIF":2.7,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1774153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43673272","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 : 2020-07-14DOI: 10.1080/24751839.2020.1790793
L. Nemes, A. Kiss
ABSTRACT In today's world, the social media is everywhere, and everybody come in contact with it every day. With social media datas, we are able to do a lot of analysis and statistics nowdays. Within this scope of article, we conclude and analyse the sentiments and manifestations (comments, hastags, posts, tweets) of the users of the Twitter social media platform, based on the main trends (by keyword, which is mostly the ‘covid’ and coronavirus theme in this article) with Natural Language Processing and with Sentiment Classification using Recurrent Neural Network. Where we analyse, compile, visualize statistics, and summarize for further processing. The trained model works much more accurately, with a smaller margin of error, in determining emotional polarity in today's ‘modern’ often with ambiguous tweets. Especially with RNN. We use this fresh scraped data collections (by the keyword's theme) with our RNN model what we have created and trained to determine what emotional manifestations occurred on a given topic in a given time interval.
{"title":"Social media sentiment analysis based on COVID-19","authors":"L. Nemes, A. Kiss","doi":"10.1080/24751839.2020.1790793","DOIUrl":"https://doi.org/10.1080/24751839.2020.1790793","url":null,"abstract":"ABSTRACT In today's world, the social media is everywhere, and everybody come in contact with it every day. With social media datas, we are able to do a lot of analysis and statistics nowdays. Within this scope of article, we conclude and analyse the sentiments and manifestations (comments, hastags, posts, tweets) of the users of the Twitter social media platform, based on the main trends (by keyword, which is mostly the ‘covid’ and coronavirus theme in this article) with Natural Language Processing and with Sentiment Classification using Recurrent Neural Network. Where we analyse, compile, visualize statistics, and summarize for further processing. The trained model works much more accurately, with a smaller margin of error, in determining emotional polarity in today's ‘modern’ often with ambiguous tweets. Especially with RNN. We use this fresh scraped data collections (by the keyword's theme) with our RNN model what we have created and trained to determine what emotional manifestations occurred on a given topic in a given time interval.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"5 1","pages":"1 - 15"},"PeriodicalIF":2.7,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1790793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47598920","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 : 2020-07-02DOI: 10.1080/24751839.2020.1786923
Keiichi Endo, T. Fujihashi, Shin-ya Kobayashi
ABSTRACT This paper deals with utilizing tablets for university education incorporating group activities. Ideathons for undergraduates were held in 2017 and 2018 at Ehime University, Japan. When holding an ideathon, we had to meet some requirements such as appropriate and prompt team organization considering interest and characteristics of the students. In order to meet those requirements, tablets were distributed to students and faculty members. The tablets were used for organizing teams, answering a survey, sharing files, and using the Internet. As a result, we received positive feedback from the students in the survey conducted after the ideathon.
{"title":"Tablet-assisted education incorporating group activities in a university","authors":"Keiichi Endo, T. Fujihashi, Shin-ya Kobayashi","doi":"10.1080/24751839.2020.1786923","DOIUrl":"https://doi.org/10.1080/24751839.2020.1786923","url":null,"abstract":"ABSTRACT This paper deals with utilizing tablets for university education incorporating group activities. Ideathons for undergraduates were held in 2017 and 2018 at Ehime University, Japan. When holding an ideathon, we had to meet some requirements such as appropriate and prompt team organization considering interest and characteristics of the students. In order to meet those requirements, tablets were distributed to students and faculty members. The tablets were used for organizing teams, answering a survey, sharing files, and using the Internet. As a result, we received positive feedback from the students in the survey conducted after the ideathon.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"4 1","pages":"282 - 294"},"PeriodicalIF":2.7,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24751839.2020.1786923","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43125501","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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