Pub Date : 2022-10-29DOI: 10.5121/csit.2022.121817
Ying Ma, Yu Sun
Technology has become increasingly vital in society. The COVID-19 pandemic demonstrated how useful technology was in keeping society running, especially education [15]. One major trend is the use of simulations as a tool for education. Business is one of the fields that could benefit massively from the implementation of new educational simulations. For this study, a survey was conducted to gauge their prior educational experience and interest in fields such as business and computer science. Additionally, the survey participants were questioned on their previous experiences with using interactive simulations. The study had fifty-one participants both complete the survey and give consent to have their data shared in this research paper. These participants were given an additional survey to either test a business simulation or watch a video of one and respond whether they learned from it. The results indicate that although most people would want to play a game that taught more about business, only roughly 45% of individuals expressed interest in the topic of business. Furthermore, the survey responses also indicated that a large majority of individuals would also prefer more interactive educational simulations for other topics. The reception to the business simulation was mostly positive, and participants indicated that it was effective at helping them learn business. Overall, it was concluded that there is not enough access to business simulations to meet the public’s interest, and either more should be created or existing ones should be made better known.
{"title":"An Interactive and Scenario-based Simulation Gaming System for Business Education using Game Engine and Machine Learning","authors":"Ying Ma, Yu Sun","doi":"10.5121/csit.2022.121817","DOIUrl":"https://doi.org/10.5121/csit.2022.121817","url":null,"abstract":"Technology has become increasingly vital in society. The COVID-19 pandemic demonstrated how useful technology was in keeping society running, especially education [15]. One major trend is the use of simulations as a tool for education. Business is one of the fields that could benefit massively from the implementation of new educational simulations. For this study, a survey was conducted to gauge their prior educational experience and interest in fields such as business and computer science. Additionally, the survey participants were questioned on their previous experiences with using interactive simulations. The study had fifty-one participants both complete the survey and give consent to have their data shared in this research paper. These participants were given an additional survey to either test a business simulation or watch a video of one and respond whether they learned from it. The results indicate that although most people would want to play a game that taught more about business, only roughly 45% of individuals expressed interest in the topic of business. Furthermore, the survey responses also indicated that a large majority of individuals would also prefer more interactive educational simulations for other topics. The reception to the business simulation was mostly positive, and participants indicated that it was effective at helping them learn business. Overall, it was concluded that there is not enough access to business simulations to meet the public’s interest, and either more should be created or existing ones should be made better known.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"100 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73419522","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 : 2022-05-28DOI: 10.5121/csit.2022.120912
Julia Colleoni Couto, Olimar Teixeira Borges, Duncan Dubugras Ruiz
When we work in a data lake, data integration is not easy, mainly because the data is usually stored in raw format. Manually performing data integration is a time-consuming task that requires the supervision of a specialist, which can make mistakes or not be able to see the optimal point for data integration among two or more datasets. This paper presents a model to perform heterogeneous in-memory data integration in a Hadoop-based data lake based on a top-k set similarity approach. Our main contribution is the process of ingesting, storing, processing, integrating, and visualizing the data integration points. The algorithm for data integration is based on the Overlap coefficient since it presented better results when compared with the set similarity metrics Jaccard, Sørensen-Dice, and the Tversky index. We tested our model applying it on eight bioinformatics-domain datasets. Our model presents better results when compared to an analysis of a specialist, and we expect our model can be reused for other domains of datasets.
{"title":"Automatized Bioinformatics Data Integration in a Hadoop-based Data Lake","authors":"Julia Colleoni Couto, Olimar Teixeira Borges, Duncan Dubugras Ruiz","doi":"10.5121/csit.2022.120912","DOIUrl":"https://doi.org/10.5121/csit.2022.120912","url":null,"abstract":"When we work in a data lake, data integration is not easy, mainly because the data is usually stored in raw format. Manually performing data integration is a time-consuming task that requires the supervision of a specialist, which can make mistakes or not be able to see the optimal point for data integration among two or more datasets. This paper presents a model to perform heterogeneous in-memory data integration in a Hadoop-based data lake based on a top-k set similarity approach. Our main contribution is the process of ingesting, storing, processing, integrating, and visualizing the data integration points. The algorithm for data integration is based on the Overlap coefficient since it presented better results when compared with the set similarity metrics Jaccard, Sørensen-Dice, and the Tversky index. We tested our model applying it on eight bioinformatics-domain datasets. Our model presents better results when compared to an analysis of a specialist, and we expect our model can be reused for other domains of datasets.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83659415","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 : 2022-05-28DOI: 10.5121/csit.2022.120906
Fadya Abbas
Dealing with extensive amounts of textual data requires an efficient deep learning model to be adapted. However, the following reasons; the highly ambiguous and complex nature of many prosodic phrasing also enough dataset suitable for system training is always limited, cause big challenges for training the NLP models. This proposed conceptual framework aims to provide an understanding and familiarity with the elements of modern deep learning networks for NLP use. In this design, the encoder uses Bidirectional Long Short-Term Memory deep network layers, to encode the test sequences into more context-sensitive representations. Moreover, the attention mechanism is mainly used to generate a context vector that is determined from distinct alignment scores at different word positions, hence, it can focus more on a small words' subset. Hence, the attention mechanism improved the model data efficiency, and the model performance is validated using an example of data sets that show promise for a real-life application.
{"title":"An Improved NLP for Syntactic and Semantic Matching using Bidirectional LSTM and Attention Mechanism","authors":"Fadya Abbas","doi":"10.5121/csit.2022.120906","DOIUrl":"https://doi.org/10.5121/csit.2022.120906","url":null,"abstract":"Dealing with extensive amounts of textual data requires an efficient deep learning model to be adapted. However, the following reasons; the highly ambiguous and complex nature of many prosodic phrasing also enough dataset suitable for system training is always limited, cause big challenges for training the NLP models. This proposed conceptual framework aims to provide an understanding and familiarity with the elements of modern deep learning networks for NLP use. In this design, the encoder uses Bidirectional Long Short-Term Memory deep network layers, to encode the test sequences into more context-sensitive representations. Moreover, the attention mechanism is mainly used to generate a context vector that is determined from distinct alignment scores at different word positions, hence, it can focus more on a small words' subset. Hence, the attention mechanism improved the model data efficiency, and the model performance is validated using an example of data sets that show promise for a real-life application.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75205465","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 : 2022-05-28DOI: 10.5121/csit.2022.120916
Michael Li, Yu Sun
In recent times with the pandemic, many people have been finding exercise as an outlet. However, this situation has made it difficult for people to connect with one another and share their progress with friends and family. This paper designs an application to utilize big data, a social media network, and exercise tracking [1][2]. The program aims to help people connect with others to support one another in their fitness journey. Through various experiments we demonstrated that the application was effective in connecting users with each other and overall improving their fitness experience. Additionally, people of all experience levels in fitness were generally satisfied with the performance of FitConnect, with those of higher experience being less satisfied than those with lesser experience. This application will facilitate getting into fitness through positive means for any person who wants to pursue a healthy lifestyle, whether in the walls of their house, a swimming pool, or a gym [3].
{"title":"FitConnect: An Intelligent Mobile Application to Automate the Exercise Tracking and Personalization using Big Data Analysis","authors":"Michael Li, Yu Sun","doi":"10.5121/csit.2022.120916","DOIUrl":"https://doi.org/10.5121/csit.2022.120916","url":null,"abstract":"In recent times with the pandemic, many people have been finding exercise as an outlet. However, this situation has made it difficult for people to connect with one another and share their progress with friends and family. This paper designs an application to utilize big data, a social media network, and exercise tracking [1][2]. The program aims to help people connect with others to support one another in their fitness journey. Through various experiments we demonstrated that the application was effective in connecting users with each other and overall improving their fitness experience. Additionally, people of all experience levels in fitness were generally satisfied with the performance of FitConnect, with those of higher experience being less satisfied than those with lesser experience. This application will facilitate getting into fitness through positive means for any person who wants to pursue a healthy lifestyle, whether in the walls of their house, a swimming pool, or a gym [3].","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89059925","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 : 2022-05-28DOI: 10.5121/csit.2022.120909
Álvaro Farias Pinheiro, Denis Silva da Silveira, Fernando Lima Neto
This work consists of applying supervised Machine Learning techniques to identify which types of active debts are appropriate for the collection method called protest, one of the means of collection used by the Attorney General of the State of Pernambuco. For research, the following techniques were applied, Neural Network (NN), Logistic Regression (LR), and Support Vector Machine (SVM). The NN model obtained more satisfactory results among the other classification techniques, achieving better values in the following metrics: Accuracy (AC), FMeasure (F1), Precision (PR), and Recall (RC) with indexes above 97% in the evaluation with these metrics. The results showed that the construction of an Artificial Intelligence/Machine Learning model to choose which debts can succeed in the collection process via protest could bring benefits to the government of Pernambuco increasing its efficiency and effectiveness.
{"title":"Use of Machine Learning for Active Public Debt Collection with Recommendation for the Method of Collection Via Protest","authors":"Álvaro Farias Pinheiro, Denis Silva da Silveira, Fernando Lima Neto","doi":"10.5121/csit.2022.120909","DOIUrl":"https://doi.org/10.5121/csit.2022.120909","url":null,"abstract":"This work consists of applying supervised Machine Learning techniques to identify which types of active debts are appropriate for the collection method called protest, one of the means of collection used by the Attorney General of the State of Pernambuco. For research, the following techniques were applied, Neural Network (NN), Logistic Regression (LR), and Support Vector Machine (SVM). The NN model obtained more satisfactory results among the other classification techniques, achieving better values in the following metrics: Accuracy (AC), FMeasure (F1), Precision (PR), and Recall (RC) with indexes above 97% in the evaluation with these metrics. The results showed that the construction of an Artificial Intelligence/Machine Learning model to choose which debts can succeed in the collection process via protest could bring benefits to the government of Pernambuco increasing its efficiency and effectiveness.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83903327","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 : 2022-05-28DOI: 10.5121/csit.2022.120923
R. Sabre, I. Wahyuni
The aim of multi-focus image fusion is to integrate images with different objects in focus so that obtained a single image with all objects in focus. In this paper, we present a novel multi-focus image fusion method based using Dempster-Shafer Theory and alpha stable distance. This method takes into consideration the information in the surrounding region of pixels. Indeed, at each pixel, the method exploits the local variability that is calculated from quadratic difference between the value of pixel I(x,y) and the value of all pixels that belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes in DempsterShafer Theory are considered: blurred part and focus part. We show that our method give the significant result.
{"title":"Dempster-Shafer and Alpha Stable Distance for Multi-Focus Image Fusion","authors":"R. Sabre, I. Wahyuni","doi":"10.5121/csit.2022.120923","DOIUrl":"https://doi.org/10.5121/csit.2022.120923","url":null,"abstract":"The aim of multi-focus image fusion is to integrate images with different objects in focus so that obtained a single image with all objects in focus. In this paper, we present a novel multi-focus image fusion method based using Dempster-Shafer Theory and alpha stable distance. This method takes into consideration the information in the surrounding region of pixels. Indeed, at each pixel, the method exploits the local variability that is calculated from quadratic difference between the value of pixel I(x,y) and the value of all pixels that belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes in DempsterShafer Theory are considered: blurred part and focus part. We show that our method give the significant result.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"266 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82791280","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 : 2022-05-28DOI: 10.5121/csit.2022.120911
Qian Chen, Yu Sun
Online media has become a mainstream of current society. With the rapid development of video data, how to acquire desired information from certain provided media is an urgent problem nowadays. The focus of this paper is to analyse a sufficient algorithm to address the issue of dynamic complex movie classification. This paper briefly demonstrates three major methods to acquire data and information from movies, including image classification, object detection, and audio classification. Its purpose is to allow the computer to analyse the content inside of each movie and understand video content. Movie classification has high research and application value. By implementing described methods, finding the most efficient methods to classify movies is the purpose of this paper. It is foreseeable that certain methods may have advantages over others when the clips are more special than others in some way, such as the audio has several significant peaks and the video has more content than others. This research aims to find a middle ground between accuracy and efficiency to optimize the outcome.
{"title":"An Multi-Dimensional Video Reverse Search Engine using Computer Vision and Machine Learning","authors":"Qian Chen, Yu Sun","doi":"10.5121/csit.2022.120911","DOIUrl":"https://doi.org/10.5121/csit.2022.120911","url":null,"abstract":"Online media has become a mainstream of current society. With the rapid development of video data, how to acquire desired information from certain provided media is an urgent problem nowadays. The focus of this paper is to analyse a sufficient algorithm to address the issue of dynamic complex movie classification. This paper briefly demonstrates three major methods to acquire data and information from movies, including image classification, object detection, and audio classification. Its purpose is to allow the computer to analyse the content inside of each movie and understand video content. Movie classification has high research and application value. By implementing described methods, finding the most efficient methods to classify movies is the purpose of this paper. It is foreseeable that certain methods may have advantages over others when the clips are more special than others in some way, such as the audio has several significant peaks and the video has more content than others. This research aims to find a middle ground between accuracy and efficiency to optimize the outcome.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84771500","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 : 2022-05-28DOI: 10.5121/csit.2022.120917
Leon He, Angwei Li
Sleep is a crucial part of a person’s daily routine [1]. However, oversleeping is often a hindrance to many people’s daily life. This paper develops an application to prevent people from oversleeping or falling back to sleep after snoozing the alarm. We applied our application to fellow students and conducted a qualitative evaluation of the approach. The results show that the application improves the chances of waking up to a significant degree.
{"title":"An Intelligent Alarm Clock System based on Big Data and Artificial Intelligence","authors":"Leon He, Angwei Li","doi":"10.5121/csit.2022.120917","DOIUrl":"https://doi.org/10.5121/csit.2022.120917","url":null,"abstract":"Sleep is a crucial part of a person’s daily routine [1]. However, oversleeping is often a hindrance to many people’s daily life. This paper develops an application to prevent people from oversleeping or falling back to sleep after snoozing the alarm. We applied our application to fellow students and conducted a qualitative evaluation of the approach. The results show that the application improves the chances of waking up to a significant degree.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87668598","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 : 2022-05-28DOI: 10.5121/csit.2022.120920
Sarvenaz Ghafourian, R. Sharifi, A. Baniasadi
The wide usage of computer vision has become popular in the recent years. One of the areas of computer vision that has been studied is facial emotion recognition, which plays a crucial role in the interpersonal communication. This paper tackles the problem of intraclass variances in the face images of emotion recognition datasets. We test the system on augmented datasets including CK+, EMOTIC, and KDEF dataset samples. After modifying our dataset, using SMOTETomek approach, we observe improvement over the default method.
{"title":"Facial Emotion Recognition in Imbalanced Datasets","authors":"Sarvenaz Ghafourian, R. Sharifi, A. Baniasadi","doi":"10.5121/csit.2022.120920","DOIUrl":"https://doi.org/10.5121/csit.2022.120920","url":null,"abstract":"The wide usage of computer vision has become popular in the recent years. One of the areas of computer vision that has been studied is facial emotion recognition, which plays a crucial role in the interpersonal communication. This paper tackles the problem of intraclass variances in the face images of emotion recognition datasets. We test the system on augmented datasets including CK+, EMOTIC, and KDEF dataset samples. After modifying our dataset, using SMOTETomek approach, we observe improvement over the default method.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89706288","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 : 2022-05-28DOI: 10.5121/csit.2022.120913
Ilnaz Nikseresht, I. Traoré, A. Baniasadi
The Activity and Event Network Model (AEN) is a new security knowledge graph that leverages large dynamic uncertain graph theory to capture and analyze stealthy and longterm attack patterns. Because the graph is expected to become extremely large over time, it can be very challenging for security analysts to navigate it and identify meaningful information. We present different visualization layers deployed to improve the graph model’s presentation. The main goal is to build an enhanced visualization system that can more simply and effectively overlay different visualization layers, namely edge/node type, node property, node age, node’s probability of being compromised, and the threat horizon layer. Therefore, with the help of the developed layers, the network security analysts can identify suspicious network security events and activities as soon as possible.
{"title":"Data Visualization of Graph-Based Threat Detection System","authors":"Ilnaz Nikseresht, I. Traoré, A. Baniasadi","doi":"10.5121/csit.2022.120913","DOIUrl":"https://doi.org/10.5121/csit.2022.120913","url":null,"abstract":"The Activity and Event Network Model (AEN) is a new security knowledge graph that leverages large dynamic uncertain graph theory to capture and analyze stealthy and longterm attack patterns. Because the graph is expected to become extremely large over time, it can be very challenging for security analysts to navigate it and identify meaningful information. We present different visualization layers deployed to improve the graph model’s presentation. The main goal is to build an enhanced visualization system that can more simply and effectively overlay different visualization layers, namely edge/node type, node property, node age, node’s probability of being compromised, and the threat horizon layer. Therefore, with the help of the developed layers, the network security analysts can identify suspicious network security events and activities as soon as possible.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78011473","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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