Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00147
Miku Kawai, Jumpei Ono, Takashi Ogata
This paper introduces the generation of explanations for a stage-performing structural simulation system using the animation of kabuki dance that we have developed. After considering the explanation function in the context of narratives and kabuki knowledge, we present a mechanism in the aforementioned system that automatically determines the content and method of an explanation according to the degree of the user’s interest and knowledge. Through this trial, we consider effective explanation methods for narrative generation and, in this study, the use of kabuki-related knowledge.
{"title":"Kabuki Explanation System Based on User’s Knowledge and Interests","authors":"Miku Kawai, Jumpei Ono, Takashi Ogata","doi":"10.1109/CSCI54926.2021.00147","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00147","url":null,"abstract":"This paper introduces the generation of explanations for a stage-performing structural simulation system using the animation of kabuki dance that we have developed. After considering the explanation function in the context of narratives and kabuki knowledge, we present a mechanism in the aforementioned system that automatically determines the content and method of an explanation according to the degree of the user’s interest and knowledge. Through this trial, we consider effective explanation methods for narrative generation and, in this study, the use of kabuki-related knowledge.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134561180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00363
Marcel Danilescu, Laura Danilescu
Granting access to an organization's information resources is an issue that is the subject of numerous research works, with different approaches. This paper addresses access and action control policies based on the levels of trust given to them.The internal organization of an enterprise implies the existence of a hierarchy of departments, structured in a tree, in which data and information are circulated both ascending and descending. The staff of the organization is the one who carries out various processes, which consist of actions, workflows and/or information flows and events. In order to participate in these processes, a certain level of trust is assigned to the person. The association between the level of trust given to a person and the value of trust attributed to an object leads to the generation of policies implemented by computer applications that use access and actions control based on trust. The creation of these policies and their updating is done from a Policy Creation Point. A Policy Storage point is used to store all policies. The Document Status Point is the location where the document status matrix is located. Thus, The Document Storage Point is the space where documents are stored in electronic format. By creating them, a single point of access to policies is established for their creation and updating, a point where policies are stored, a point of storage of the workflow applied to documents and the active process, and a point of documents storage.Our paper presents, in addition to an original formal model, the use of trust gained by a member of an organization (trust calculated or attributed directly), and an example of its practical applicability in the information processes in the organization.This paper complements our previous work, which focuses on the aspects of using trust in controlling access and user interaction with information processes in the organization. This paper complements our previous work, which focuses on the aspects of using trust in controlling user access and interaction with information processes in the organization.
{"title":"Design of software applications using access and actions control policies based on trust","authors":"Marcel Danilescu, Laura Danilescu","doi":"10.1109/CSCI54926.2021.00363","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00363","url":null,"abstract":"Granting access to an organization's information resources is an issue that is the subject of numerous research works, with different approaches. This paper addresses access and action control policies based on the levels of trust given to them.The internal organization of an enterprise implies the existence of a hierarchy of departments, structured in a tree, in which data and information are circulated both ascending and descending. The staff of the organization is the one who carries out various processes, which consist of actions, workflows and/or information flows and events. In order to participate in these processes, a certain level of trust is assigned to the person. The association between the level of trust given to a person and the value of trust attributed to an object leads to the generation of policies implemented by computer applications that use access and actions control based on trust. The creation of these policies and their updating is done from a Policy Creation Point. A Policy Storage point is used to store all policies. The Document Status Point is the location where the document status matrix is located. Thus, The Document Storage Point is the space where documents are stored in electronic format. By creating them, a single point of access to policies is established for their creation and updating, a point where policies are stored, a point of storage of the workflow applied to documents and the active process, and a point of documents storage.Our paper presents, in addition to an original formal model, the use of trust gained by a member of an organization (trust calculated or attributed directly), and an example of its practical applicability in the information processes in the organization.This paper complements our previous work, which focuses on the aspects of using trust in controlling access and user interaction with information processes in the organization. This paper complements our previous work, which focuses on the aspects of using trust in controlling user access and interaction with information processes in the organization.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130314875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00096
Korn Sooksatra, P. Rivas
With the increasing presence of deep learning models, many applications have had significant improvements; however, they face a new vulnerability known as adversarial examples. Adversarial examples can mislead deep learning models to predict the wrong classes without human actors noticing. Recently, many works have tried to improve adversarial examples to make them stronger and more effective. However, although some researchers have invented mechanisms to defend deep learning models against adversarial examples, those mechanisms may negatively affect different measures of fairness, which are critical in practice. This work mathematically defines four fairness scores to show that training adversarially robust models can harm fairness scores. Furthermore, we empirically show that adversarial training, one of the most potent defensive mechanisms against adversarial examples, can harm them.
{"title":"Adversarial Training Negatively Affects Fairness","authors":"Korn Sooksatra, P. Rivas","doi":"10.1109/CSCI54926.2021.00096","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00096","url":null,"abstract":"With the increasing presence of deep learning models, many applications have had significant improvements; however, they face a new vulnerability known as adversarial examples. Adversarial examples can mislead deep learning models to predict the wrong classes without human actors noticing. Recently, many works have tried to improve adversarial examples to make them stronger and more effective. However, although some researchers have invented mechanisms to defend deep learning models against adversarial examples, those mechanisms may negatively affect different measures of fairness, which are critical in practice. This work mathematically defines four fairness scores to show that training adversarially robust models can harm fairness scores. Furthermore, we empirically show that adversarial training, one of the most potent defensive mechanisms against adversarial examples, can harm them.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130389019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00141
O. Kotevska, Folami T. Alamudun, Christopher Stanley
As the number of online services has increased, the amount of sensitive data being recorded is rising. Simultaneously, the decision-making process has improved by using the vast amounts of data, where machine learning has transformed entire industries. This paper addresses the development of optimal private deep neural networks and discusses the challenges associated with this task. We focus on differential privacy implementations and finding the optimal balance between accuracy and privacy, benefits and limitations of existing libraries, and challenges of applying private machine learning models in practical applications. Our analysis shows that learning rate, and privacy budget are the key factors that impact the results, and we discuss options for these settings.
{"title":"Optimal Balance of Privacy and Utility with Differential Privacy Deep Learning Frameworks","authors":"O. Kotevska, Folami T. Alamudun, Christopher Stanley","doi":"10.1109/CSCI54926.2021.00141","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00141","url":null,"abstract":"As the number of online services has increased, the amount of sensitive data being recorded is rising. Simultaneously, the decision-making process has improved by using the vast amounts of data, where machine learning has transformed entire industries. This paper addresses the development of optimal private deep neural networks and discusses the challenges associated with this task. We focus on differential privacy implementations and finding the optimal balance between accuracy and privacy, benefits and limitations of existing libraries, and challenges of applying private machine learning models in practical applications. Our analysis shows that learning rate, and privacy budget are the key factors that impact the results, and we discuss options for these settings.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114418535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00149
Chuluunsukh Anudari, YoungSu Yun, M. Gen
A supply chain network (SCN) model which considers facility and route disruptions simultaneously is proposed in this paper. Since most of conventional literature have focused either on facility disruption solely or on route disruption solely, the simultaneous consideration of facility and route disruptions can improve the flexibility of the implementation in the SCN model. The SCN model under the disruptions is represented as a mathematical formulation and a hybrid meta-heuristics (GA-VNS) approach which combines genetic algorithm (GA) with variable neighborhood search (VNS) is used for the mathematical formulation. In numerical experiment, two scaled SCN models are used for comparing the performance of the GA-VNS approach with those of some conventional meta-heuristics approaches. Experimental results prove that the GA-VNS approach is more robust than conventional meta-heuristics approaches, and the flexibility of the SCN model under the disruptions are also improved.
{"title":"Hybrid Meta-heuristics Approach for Solving Supply Chain Network Model under Disruption Risk","authors":"Chuluunsukh Anudari, YoungSu Yun, M. Gen","doi":"10.1109/CSCI54926.2021.00149","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00149","url":null,"abstract":"A supply chain network (SCN) model which considers facility and route disruptions simultaneously is proposed in this paper. Since most of conventional literature have focused either on facility disruption solely or on route disruption solely, the simultaneous consideration of facility and route disruptions can improve the flexibility of the implementation in the SCN model. The SCN model under the disruptions is represented as a mathematical formulation and a hybrid meta-heuristics (GA-VNS) approach which combines genetic algorithm (GA) with variable neighborhood search (VNS) is used for the mathematical formulation. In numerical experiment, two scaled SCN models are used for comparing the performance of the GA-VNS approach with those of some conventional meta-heuristics approaches. Experimental results prove that the GA-VNS approach is more robust than conventional meta-heuristics approaches, and the flexibility of the SCN model under the disruptions are also improved.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123162132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00095
Nour El-Din Ali Said, Yassin Samaha, Eman Azab, L. Shihata, M. Mashaly
In the manufacturing industries, the most challenging problems are mostly related to time efficiency and customer satisfaction. This is mainly translated to how efficient is the frequent task of scheduling jobs to alternative routes on a number of machines. Although scheduling has been studied for decades, there is a shortage to a generalized approach for the production scheduling that can adapt to changes occurring continuously at any production environment. This research work addresses the dynamic production scheduling problem and the optimization techniques that could be applied to the production schedule to increase its efficiency. An algorithm is proposed to apply the Q-learning optimization technique on a dynamic flexible job-shop scheduling problem of a real case study of a pharmaceutical factory with 18 machines and 22 products. Proposed algorithm is shown to be able to achieve an efficient schedule with short make-span in minimal time duration and without requiring any learning process from previous schedules, thus increasing the factory's overall efficiency. In addition, the proposed algorithm operates online as any change occurring in the production environment is signaled automatically to it where it responds be regenerating the most optimal updated production schedule.
{"title":"An Online Reinforcement Learning Approach for Solving the Dynamic Flexible Job-Shop Scheduling Problem for Multiple Products and Constraints","authors":"Nour El-Din Ali Said, Yassin Samaha, Eman Azab, L. Shihata, M. Mashaly","doi":"10.1109/CSCI54926.2021.00095","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00095","url":null,"abstract":"In the manufacturing industries, the most challenging problems are mostly related to time efficiency and customer satisfaction. This is mainly translated to how efficient is the frequent task of scheduling jobs to alternative routes on a number of machines. Although scheduling has been studied for decades, there is a shortage to a generalized approach for the production scheduling that can adapt to changes occurring continuously at any production environment. This research work addresses the dynamic production scheduling problem and the optimization techniques that could be applied to the production schedule to increase its efficiency. An algorithm is proposed to apply the Q-learning optimization technique on a dynamic flexible job-shop scheduling problem of a real case study of a pharmaceutical factory with 18 machines and 22 products. Proposed algorithm is shown to be able to achieve an efficient schedule with short make-span in minimal time duration and without requiring any learning process from previous schedules, thus increasing the factory's overall efficiency. In addition, the proposed algorithm operates online as any change occurring in the production environment is signaled automatically to it where it responds be regenerating the most optimal updated production schedule.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131759580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00249
Ying-Feng Hsu, Morito Matsuoka
In this paper, we present an early warning system for patients in the emergency department. Our proposed system includes data processing steps that transform raw clinical data streams into useful information that facilitates clinical decision making for the early warning. We tested the proposed approach in a medical monitoring system, which takes physiologic data and predicts in which clinical setting the data is most likely to be seen. To demonstrate the high utility of our approach, we conducted a set of experiments on the clinical data of 1,176 patients.
{"title":"An Early Warning System for Patients in Emergency Department based on Machine Learning","authors":"Ying-Feng Hsu, Morito Matsuoka","doi":"10.1109/CSCI54926.2021.00249","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00249","url":null,"abstract":"In this paper, we present an early warning system for patients in the emergency department. Our proposed system includes data processing steps that transform raw clinical data streams into useful information that facilitates clinical decision making for the early warning. We tested the proposed approach in a medical monitoring system, which takes physiologic data and predicts in which clinical setting the data is most likely to be seen. To demonstrate the high utility of our approach, we conducted a set of experiments on the clinical data of 1,176 patients.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133249324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00150
J. Bishop, M. Goode
This paper investigates the long-established ecological cognition framework and updates it to better account for the advances in computational science computational intelligence. To do this, two concepts are explored. The first, ‘serendipity engineering for seductive hypermedia,’ looks at how to design information systems to account for the pleasant occurrences that happen in offline environments studied by those in sales and marketing where beneficial outcomes often occur by chance encounters. The second, ‘user analysis using socialnomics’ looks at how a parametric user model based on the ecological framework can be used to understand users of information systems from the point of view of supporting a digital economy of users. A number of additional equations are developed using socialnomics that can be applied to digital transformation based on the parametric user model, including to calculate probability of seduction and probability of serendipity in an information system. The parametric model presented has great applicability for information and communications technology solution providers.
{"title":"Towards ‘serendipity engineering for seductive hypermedia’ and ‘user analysis using socialnomics’: The role of ecological cognition","authors":"J. Bishop, M. Goode","doi":"10.1109/CSCI54926.2021.00150","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00150","url":null,"abstract":"This paper investigates the long-established ecological cognition framework and updates it to better account for the advances in computational science computational intelligence. To do this, two concepts are explored. The first, ‘serendipity engineering for seductive hypermedia,’ looks at how to design information systems to account for the pleasant occurrences that happen in offline environments studied by those in sales and marketing where beneficial outcomes often occur by chance encounters. The second, ‘user analysis using socialnomics’ looks at how a parametric user model based on the ecological framework can be used to understand users of information systems from the point of view of supporting a digital economy of users. A number of additional equations are developed using socialnomics that can be applied to digital transformation based on the parametric user model, including to calculate probability of seduction and probability of serendipity in an information system. The parametric model presented has great applicability for information and communications technology solution providers.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"492 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132898806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00049
Khalid Amen, Mohamad Zohdy, M. Mahmoud
Phishing is a fraudulent process and a form of cybercrime where an attacker tries to obtain sensitive information for malicious use. A phisher uses social engineering and technical deception to fetch private information from the web user. Previous Machine Learning (ML) approaches have been used to detect whether URLs are valid, or invalid. The purpose of this work is to detect, or predict, the three stages of Phishing URLs starting with valid, not enough info and invalid URLs. We will investigate different potential models that are trained by Machine Learning algorithms and find out which of these models has better accuracy.
{"title":"Machine Learning for Multiple Stage Phishing URL Prediction","authors":"Khalid Amen, Mohamad Zohdy, M. Mahmoud","doi":"10.1109/CSCI54926.2021.00049","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00049","url":null,"abstract":"Phishing is a fraudulent process and a form of cybercrime where an attacker tries to obtain sensitive information for malicious use. A phisher uses social engineering and technical deception to fetch private information from the web user. Previous Machine Learning (ML) approaches have been used to detect whether URLs are valid, or invalid. The purpose of this work is to detect, or predict, the three stages of Phishing URLs starting with valid, not enough info and invalid URLs. We will investigate different potential models that are trained by Machine Learning algorithms and find out which of these models has better accuracy.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121102500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00259
Misagh Faezipour, M. Faezipour, Brianna Bauman
This paper proposes a systems engineering perspective to analyze the causes of COVID-19 health disparities impact and interventions to minimize the impact on minorities. The impact of the novel coronavirus has shown to be more intense on minorities. The percentage of COVID-19 case count and fatality rate for minorities is much higher than that of the general population, showing that they are more significantly affected than others. Many different factors influence this impact, ranging from economic to cultural. In this paper, these factors are shown to be connected through a causal model analyzing the effects of each factor, after which, potential interventions are suggested. Many factors are identified, such as high employment in the service industry or lower likelihood to have insurance. From this, a causal model is created showing the impact of each factor. Using this causal model, one can identify the high-impact factors causing a disparate impact as well as suggest possible interventions including making testing and treatment more accessible, reducing healthcare bias, and improving healthcare for immigrants.
{"title":"Development of a Causal Model to Study the Disparate Effects of COVID-19 on Minorities","authors":"Misagh Faezipour, M. Faezipour, Brianna Bauman","doi":"10.1109/CSCI54926.2021.00259","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00259","url":null,"abstract":"This paper proposes a systems engineering perspective to analyze the causes of COVID-19 health disparities impact and interventions to minimize the impact on minorities. The impact of the novel coronavirus has shown to be more intense on minorities. The percentage of COVID-19 case count and fatality rate for minorities is much higher than that of the general population, showing that they are more significantly affected than others. Many different factors influence this impact, ranging from economic to cultural. In this paper, these factors are shown to be connected through a causal model analyzing the effects of each factor, after which, potential interventions are suggested. Many factors are identified, such as high employment in the service industry or lower likelihood to have insurance. From this, a causal model is created showing the impact of each factor. Using this causal model, one can identify the high-impact factors causing a disparate impact as well as suggest possible interventions including making testing and treatment more accessible, reducing healthcare bias, and improving healthcare for immigrants.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116745047","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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