Pub Date : 2023-01-28DOI: 10.5121/csit.2023.130213
Hui Hu
Resource Description Framework (RDF) is designed as a standard metadata model for data interchange on the Internet. Because of machine comprehensibility, it has been successfully used in many areas, such as the intelligent processing of numerous data. While the generation of RDF with relational database (RDB) receives much attention, little effort has been put into the automatic construction of RDF with HBase due to its flexible data structure. Since more data is stored in HBase, it is necessary to extract useful information from HBase. In this paper, we are devoted to construction of RDF with HBase. We put forward formal definitions of RDF and HBase and propose our strategy for generating RDF with HBase. We develop a prototype system to create RDF, and test results demonstrate the feasibility of our method.
{"title":"Research on Construction of RDF with HBase","authors":"Hui Hu","doi":"10.5121/csit.2023.130213","DOIUrl":"https://doi.org/10.5121/csit.2023.130213","url":null,"abstract":"Resource Description Framework (RDF) is designed as a standard metadata model for data interchange on the Internet. Because of machine comprehensibility, it has been successfully used in many areas, such as the intelligent processing of numerous data. While the generation of RDF with relational database (RDB) receives much attention, little effort has been put into the automatic construction of RDF with HBase due to its flexible data structure. Since more data is stored in HBase, it is necessary to extract useful information from HBase. In this paper, we are devoted to construction of RDF with HBase. We put forward formal definitions of RDF and HBase and propose our strategy for generating RDF with HBase. We develop a prototype system to create RDF, and test results demonstrate the feasibility of our method.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128331766","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 : 2023-01-28DOI: 10.5121/csit.2023.130207
Nicole Ma, Yu Sun
Instances of hate speech on popular social media platforms such as Twitter are becoming increasingly common and intense. However, there still exists a lack of comprehensive deeplearning models to combat Twitter hate speech. In this project, a comprehensive detection and reporting platform, entitled “TweetWatch,” was created to solve this issue. A binary classification CNN (Convolutional Neural Network) and a multi-class CNN were created to detect hate speech from real-time Twitter data and classify tweets with hate speech into five categories. The binary classification model has an AUC score of 98.95% and an F1 score of 97.88%. The multi-class classification model has an AUC score of 89.46%. All metrics reached over a targeted 5% increase from previous models in multiple papers, validating the proposed solution. Additionally, the only real-time choropleth map for hate speech in the United States was successfully created.
{"title":"A Novel System for Regional Twitter Hate Speech Analysis and Detection using Deep Learning Models and Web Scraping","authors":"Nicole Ma, Yu Sun","doi":"10.5121/csit.2023.130207","DOIUrl":"https://doi.org/10.5121/csit.2023.130207","url":null,"abstract":"Instances of hate speech on popular social media platforms such as Twitter are becoming increasingly common and intense. However, there still exists a lack of comprehensive deeplearning models to combat Twitter hate speech. In this project, a comprehensive detection and reporting platform, entitled “TweetWatch,” was created to solve this issue. A binary classification CNN (Convolutional Neural Network) and a multi-class CNN were created to detect hate speech from real-time Twitter data and classify tweets with hate speech into five categories. The binary classification model has an AUC score of 98.95% and an F1 score of 97.88%. The multi-class classification model has an AUC score of 89.46%. All metrics reached over a targeted 5% increase from previous models in multiple papers, validating the proposed solution. Additionally, the only real-time choropleth map for hate speech in the United States was successfully created.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130691926","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 : 2023-01-28DOI: 10.5121/csit.2023.130211
Lina Lumburovska, V. Dimitrova, Aleksandra Popovska Mitrovikj, Ss. Cyril
The latest research shows the benefits, the impact, and the usage of Blockchain and decentralized systems with a high confidence. Its popularity becomes even higher with the electronic voting systems based on the technology itself. In this paper we propose a new implementation of an electronic voting system based on Blockchain using ECDSA with blind signatures. Additionally, the system is compared with other electronic voting systems based on Blockchain technology. Mainly these types of systems hardly ever fulfill the scalability. Nevertheless, our system has an advantage in comparison with the other systems. Since the idea of the Blockchain technology is to show the flexibility and equal privileges to all nodes, this implementation with Angular and Spring Boot shows that, so everyone can track the chain. To sum up, this implementation can have a good usage in smaller departments, because of the performances and all mathematical operations.
{"title":"Implementation of a New E-voting System based on Blockchain using ECDSA with Blind Signatures","authors":"Lina Lumburovska, V. Dimitrova, Aleksandra Popovska Mitrovikj, Ss. Cyril","doi":"10.5121/csit.2023.130211","DOIUrl":"https://doi.org/10.5121/csit.2023.130211","url":null,"abstract":"The latest research shows the benefits, the impact, and the usage of Blockchain and decentralized systems with a high confidence. Its popularity becomes even higher with the electronic voting systems based on the technology itself. In this paper we propose a new implementation of an electronic voting system based on Blockchain using ECDSA with blind signatures. Additionally, the system is compared with other electronic voting systems based on Blockchain technology. Mainly these types of systems hardly ever fulfill the scalability. Nevertheless, our system has an advantage in comparison with the other systems. Since the idea of the Blockchain technology is to show the flexibility and equal privileges to all nodes, this implementation with Angular and Spring Boot shows that, so everyone can track the chain. To sum up, this implementation can have a good usage in smaller departments, because of the performances and all mathematical operations.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114126723","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}
Anxiety is a chronic illness especially during the Covid and post-pandemic era. It’s important to diagnose anxiety in its early stages. Traditional Machine learning (ML) methods have been developmental intense procedures to detect mental health issues, but Automated machine learning (AutoML) is a method whereby the novice user can build a model to detect a phenomenon such as Generalized Anxiety Disorder (GAD) fairly easily. In this study we evaluate a popular AutoML technique with recent chat engine (Discord) conversation dataset using anxiety hashtags. This multi-symptom AutoML Random Forest predictive model is at least 75+% accurate with the most prevalent symptom, namely restlessness. This could be a very useful first step in diagnosing GAD by medical professionals and their less skilled hospital’s IT area using pre diagnostic textual conversations. But it lacks high quality in predicting GAD in most symptoms as found by a low 50% precision on most symptoms (except 5). The AutoML technology is quicker for IT professionals and gives a decent performance, but it can be improved upon by more sophisticated ANN methods like Convolution neural networks that plug AutoML’s symptom’s deficiencies with at least 80+% precision and 0.4+% in F1 score, namely in detecting poorly predicted symptoms of concentration and irritability.
{"title":"Comparative Study of Anxiety Symptom’s Predictions From Discord Chat Messages using Automl","authors":"Anishka Duvvuri, Navya Kovvuri, Sneka Kumar, Rebecca Victor, Tanush Kaushik","doi":"10.5121/csit.2023.130202","DOIUrl":"https://doi.org/10.5121/csit.2023.130202","url":null,"abstract":"Anxiety is a chronic illness especially during the Covid and post-pandemic era. It’s important to diagnose anxiety in its early stages. Traditional Machine learning (ML) methods have been developmental intense procedures to detect mental health issues, but Automated machine learning (AutoML) is a method whereby the novice user can build a model to detect a phenomenon such as Generalized Anxiety Disorder (GAD) fairly easily. In this study we evaluate a popular AutoML technique with recent chat engine (Discord) conversation dataset using anxiety hashtags. This multi-symptom AutoML Random Forest predictive model is at least 75+% accurate with the most prevalent symptom, namely restlessness. This could be a very useful first step in diagnosing GAD by medical professionals and their less skilled hospital’s IT area using pre diagnostic textual conversations. But it lacks high quality in predicting GAD in most symptoms as found by a low 50% precision on most symptoms (except 5). The AutoML technology is quicker for IT professionals and gives a decent performance, but it can be improved upon by more sophisticated ANN methods like Convolution neural networks that plug AutoML’s symptom’s deficiencies with at least 80+% precision and 0.4+% in F1 score, namely in detecting poorly predicted symptoms of concentration and irritability.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123928798","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 : 2023-01-28DOI: 10.5121/csit.2023.130203
Chunhei Zhu, Yujia Zhang
The issue of finding a clean and simple player movement implementation that the general public will find intuitive and easy to use has been tackled over the years in various ways. With FPS (first-person shooter) games, the need fora simple and fun style of movement is monumentally crucial, as that will be a core aspect of the gameplay [4]. To address this issue, an FPS game was created with the ability to maintain momentum while crouching with intention of providing a smoother and more intuitive gaming experience for players. This movement implementation was tested by having participants play the game for a sufficient amount of time, then asking the participants to rate the experience of movement in the game and the overall enjoyment of playing the game. The results indicate that the implemented movement would be well-received by the general public, as the vast majority of the participants viewedthe new form of movement as a welcome feature based on the optional feedback and the quantitative ratings. However, the other aspects of the gameplay were not as polished and therefore lowered the overall enjoyment of the game for the participants, particularly the shooting in the game that does not yet have proper audio or visual cues tolet the player know that the weapon has been fired.
{"title":"A First-Person Shooter Game Designed to Educate and Aid the Player Movement Implementation","authors":"Chunhei Zhu, Yujia Zhang","doi":"10.5121/csit.2023.130203","DOIUrl":"https://doi.org/10.5121/csit.2023.130203","url":null,"abstract":"The issue of finding a clean and simple player movement implementation that the general public will find intuitive and easy to use has been tackled over the years in various ways. With FPS (first-person shooter) games, the need fora simple and fun style of movement is monumentally crucial, as that will be a core aspect of the gameplay [4]. To address this issue, an FPS game was created with the ability to maintain momentum while crouching with intention of providing a smoother and more intuitive gaming experience for players. This movement implementation was tested by having participants play the game for a sufficient amount of time, then asking the participants to rate the experience of movement in the game and the overall enjoyment of playing the game. The results indicate that the implemented movement would be well-received by the general public, as the vast majority of the participants viewedthe new form of movement as a welcome feature based on the optional feedback and the quantitative ratings. However, the other aspects of the gameplay were not as polished and therefore lowered the overall enjoyment of the game for the participants, particularly the shooting in the game that does not yet have proper audio or visual cues tolet the player know that the weapon has been fired.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121373078","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 : 2023-01-28DOI: 10.5121/csit.2023.130209
Yulin Zhang, Yu Sun
Over the past few decades, the problem of distraction and its accompanying side effects has taken its root deeply in all parts of our daily life and extended its ever-increasing influences among young generations [2]. In addition to its alarming prevalence, another characteristic of distraction that raises most concerns is how easily we can get distracted from our tasks at hand while using the electronic devices as a means of solving problems [3]. This paper attempts to address this society-wide problem thoroughly and universally through a technical approach of detecting, analyzing, and blocking the websites intelligently. Our design highlights the applications of machine learning and natural language processing, and is implemented purely in Python, Javascript, and several other web development languages. After retrieving the web content from the target websites through the web scraping process, summarizing the data to a number of short paragraphs via the use of NLP, we were able to perform data analysis on the result and finally block the websites accordingly [4]. With the help of this extension, students and those who wish to improve their concentration in work will be able to put more focus on the tasks at hand and thus boost their work efficiency under any working conditions.
{"title":"A NLP-learning Powered Customizable Approach Towards Auto-blocking Distracting Websites","authors":"Yulin Zhang, Yu Sun","doi":"10.5121/csit.2023.130209","DOIUrl":"https://doi.org/10.5121/csit.2023.130209","url":null,"abstract":"Over the past few decades, the problem of distraction and its accompanying side effects has taken its root deeply in all parts of our daily life and extended its ever-increasing influences among young generations [2]. In addition to its alarming prevalence, another characteristic of distraction that raises most concerns is how easily we can get distracted from our tasks at hand while using the electronic devices as a means of solving problems [3]. This paper attempts to address this society-wide problem thoroughly and universally through a technical approach of detecting, analyzing, and blocking the websites intelligently. Our design highlights the applications of machine learning and natural language processing, and is implemented purely in Python, Javascript, and several other web development languages. After retrieving the web content from the target websites through the web scraping process, summarizing the data to a number of short paragraphs via the use of NLP, we were able to perform data analysis on the result and finally block the websites accordingly [4]. With the help of this extension, students and those who wish to improve their concentration in work will be able to put more focus on the tasks at hand and thus boost their work efficiency under any working conditions.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"01 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127332067","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 : 2023-01-28DOI: 10.5121/csit.2023.130206
Bill Xu, Yu Sun
Recent years have witnessed the dramatic popularity of cryptocurrencies, in which millions invest to join the cryptocurrency community or make financial gains [1]. Investors employ many ways to analyze a cryptocurrency, from a purely technical approach to a more utility-centred approach [2]. However, few technologies exist to help investors find cryptocurrencies with bright prospects through social metrics, an equally if not more important viewpoint to consider due to the importance of communities in the space. This paper proposes an application to evaluate cryptocurrencies based on social metrics by establishing scores and models with machine learning and other tools [3]. We verified the need for our application through surveys, applied it to test investment strategies, andconducted a qualitative evaluation of the approach. The results show that our tool benefits investors by providing them with a different lens to view cryptocurrencies and helps them make more thorough decisions.
{"title":"A Cryptocurrency Analysis Tool based on Social Metrics","authors":"Bill Xu, Yu Sun","doi":"10.5121/csit.2023.130206","DOIUrl":"https://doi.org/10.5121/csit.2023.130206","url":null,"abstract":"Recent years have witnessed the dramatic popularity of cryptocurrencies, in which millions invest to join the cryptocurrency community or make financial gains [1]. Investors employ many ways to analyze a cryptocurrency, from a purely technical approach to a more utility-centred approach [2]. However, few technologies exist to help investors find cryptocurrencies with bright prospects through social metrics, an equally if not more important viewpoint to consider due to the importance of communities in the space. This paper proposes an application to evaluate cryptocurrencies based on social metrics by establishing scores and models with machine learning and other tools [3]. We verified the need for our application through surveys, applied it to test investment strategies, andconducted a qualitative evaluation of the approach. The results show that our tool benefits investors by providing them with a different lens to view cryptocurrencies and helps them make more thorough decisions.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133781717","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 : 2023-01-28DOI: 10.5121/csit.2023.130214
Joerg H. Hardy
Autonomous robots will need to form relationships with humans that are built on reliability and (social) trust. The source of reliability and trust in human relationships is (human) ethical competence, which includes the capability of moral decision-making. As autonomous robots cannot act with the ethical competence of human agents, a kind of human-like ethical competence has to be implemented into autonomous robots (AI-systems of various kinds) by way of ethical algorithms. In this paper I suggest a model of the general logical form of (human) meta-ethical arguments that can be used as a pattern for the programming of ethical algorithms for autonomous robots.
{"title":"Ethical Algorithms in Human-Robot-Interaction. A Proposal","authors":"Joerg H. Hardy","doi":"10.5121/csit.2023.130214","DOIUrl":"https://doi.org/10.5121/csit.2023.130214","url":null,"abstract":"Autonomous robots will need to form relationships with humans that are built on reliability and (social) trust. The source of reliability and trust in human relationships is (human) ethical competence, which includes the capability of moral decision-making. As autonomous robots cannot act with the ethical competence of human agents, a kind of human-like ethical competence has to be implemented into autonomous robots (AI-systems of various kinds) by way of ethical algorithms. In this paper I suggest a model of the general logical form of (human) meta-ethical arguments that can be used as a pattern for the programming of ethical algorithms for autonomous robots.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128665401","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 : 2023-01-28DOI: 10.5121/csit.2023.130201
Randy Klepetko, R. Krishnan
Convolutional Neural Networks (CNN) continue to revolutionize image recognition technology and are being used in non-image related fields such as cybersecurity. They are known to work as feature extractors, identifying patterns within large data sets, but when dealing with nonnatural data, what these features represent is not understood. Several class activation map (CAM) visualization tools are available that assist with understanding the CNN decisions when used with images, but they are not intuitively comprehended when dealing with nonnatural security data. Understanding what the extracted features represent should enable the data analyst and model architect tailor a model to maximize the extracted features while minimizing the computational parameters. In this paper we offer a new tool Model integrated Class Activation Maps, (MiCAM) which allows the analyst the ability to visually compare extracted feature intensities at the individual layer detail. We explore using this new tool to analyse several datasets. First the MNIST handwriting data set to gain a baseline understanding. We then analyse two security data sets: computers process metrics from cloud based application servers that are infected with malware and the CIC-IDS-2017 IP data traffic set and identify how re-ordering nonnatural security related data affects feature extraction performance and identify how reordering the data affect feature extraction performance.
{"title":"Micam: Visualizing Feature Extraction of Nonnatural Data","authors":"Randy Klepetko, R. Krishnan","doi":"10.5121/csit.2023.130201","DOIUrl":"https://doi.org/10.5121/csit.2023.130201","url":null,"abstract":"Convolutional Neural Networks (CNN) continue to revolutionize image recognition technology and are being used in non-image related fields such as cybersecurity. They are known to work as feature extractors, identifying patterns within large data sets, but when dealing with nonnatural data, what these features represent is not understood. Several class activation map (CAM) visualization tools are available that assist with understanding the CNN decisions when used with images, but they are not intuitively comprehended when dealing with nonnatural security data. Understanding what the extracted features represent should enable the data analyst and model architect tailor a model to maximize the extracted features while minimizing the computational parameters. In this paper we offer a new tool Model integrated Class Activation Maps, (MiCAM) which allows the analyst the ability to visually compare extracted feature intensities at the individual layer detail. We explore using this new tool to analyse several datasets. First the MNIST handwriting data set to gain a baseline understanding. We then analyse two security data sets: computers process metrics from cloud based application servers that are infected with malware and the CIC-IDS-2017 IP data traffic set and identify how re-ordering nonnatural security related data affects feature extraction performance and identify how reordering the data affect feature extraction performance.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127160173","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 : 2023-01-28DOI: 10.5121/csit.2023.130208
Ke Zhang
We aim to tackle the issue of improving the global situation regarding climate change by creating a mobile application named Climerry, which educates its users on recent news related to climate on the home screen. Climerry also features a second tab that allows users to view opportunities to improve the climate change situation in the vicinity by typing in a ZIP code or city name. Some examples of opportunities include beach cleanups and tree-planting sessions. By informing and encouraging the general public to become more involved in the effort to preserve our planet, the negative effects of climate change may be much less significant in the future. To prove the effectiveness of this application in encouraging the general public to take action against climate change, one experiment was performed to gauge how much knowledge regarding climate change the participants had gained by using the application. Another experiment tested the reliability of the news API used in the application by testing the accuracy of information in each of the selected articles in the featured news section of the application. The result of the experiments indicated that the application is useful when it comes to providing accurate news and educating its users on the topic of climate change.
{"title":"A Smart Mobile Application Designed to Educate and Aid the Public in Combating Climate Change","authors":"Ke Zhang","doi":"10.5121/csit.2023.130208","DOIUrl":"https://doi.org/10.5121/csit.2023.130208","url":null,"abstract":"We aim to tackle the issue of improving the global situation regarding climate change by creating a mobile application named Climerry, which educates its users on recent news related to climate on the home screen. Climerry also features a second tab that allows users to view opportunities to improve the climate change situation in the vicinity by typing in a ZIP code or city name. Some examples of opportunities include beach cleanups and tree-planting sessions. By informing and encouraging the general public to become more involved in the effort to preserve our planet, the negative effects of climate change may be much less significant in the future. To prove the effectiveness of this application in encouraging the general public to take action against climate change, one experiment was performed to gauge how much knowledge regarding climate change the participants had gained by using the application. Another experiment tested the reliability of the news API used in the application by testing the accuracy of information in each of the selected articles in the featured news section of the application. The result of the experiments indicated that the application is useful when it comes to providing accurate news and educating its users on the topic of climate change.","PeriodicalId":132577,"journal":{"name":"Machine Learning and Soft Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131115331","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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