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5th International Conference on Information Technology for Education and Development, ITED 2022

作者： Iyanda, Abimbola Rhoda Ogungbe, Omolara Aminat Aderibigbe, Adeniyi Sunday Obafemi Awolowo University Department of Computer Science and Engineering Ile-Ife Nigeria Obafemi Awolowo University Department of Radiology Teaching Hospital Complex Ile-Ife Nigeria

ISBN: (纸本)9781509064229

In Nigeria, prose format is used to present and perform analysis on chest x-ray reports and this often results in delayed response from the clinicians. Therefore, with a view to developing a system for analyzing chest x-ray reports for diagnosing cardiomegaly, linear support vector machine algorithm was utilized to formulate an adaptable model with a train-test split of 70:30 for six hundred and fifty (650) de-identified patients' information. Attributes relevant to cardiomegaly from the collected dataset were extracted using Term frequency/inverse document frequency technique. This work provides an adequate requirement for diagnosis design with accuracy of 93.69%. Its implementation in software application has the potential to reduce delay in attending to patients and can also help the clinicians focus on the findings from chest x-ray reports. © 2022 IEEE.

关键词： Diagnosis

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The Impact of Online STEM teaching and Learning During COVID-19 on Underrepresented Students’ Self-Efficacy and Motivation

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Journal of College science teaching 2022年第6期51卷 6-15页

作者： Kahn, Sami Vertesi, Janet Adriaenssens, Sigrid Byeon, Julia Fixdal, Mona Godfrey, Kelly Lumbroso, Jérémie Wagoner, Kasey Council on Science and Technology United States Department of Sociology United States Department of Civil and Environmental Engineering United States McGraw Center for Teaching and Learning United States Department of Computer Science United States Department of Physics Princeton University Princeton NJ United States

Female students, students of color, first-generation students, and low-income students face considerable barriers in access to STEM education, leading to their underrepresentation in STEM fields. Ensuring that these students develop strong self-efficacy and motivation in STEM during the college years is key to addressing the “leaky” STEM pipeline. To determine whether the rapid shift to online teaching and learning during the COVID-19 pandemic exacerbated or mitigated inequities for college-level STEM students, we examined correlations between demographic and sociocultural factors and students’ self-assessments on indicators of self-efficacy and motivation. Our findings suggest that students from underrepresented groups were differentially negatively impacted by the shift to online teaching and learning, particularly with regard to access to study spaces, the internet, and peers. However, we found that the loss of traditional laboratories was not particularly impactful on any students’ motivation or self-efficacy, regardless of a course’s levels of dependence on such labs, as students were generally more impacted by concerns about family members’ health and loss of social and structural supports than academic experiences. We discuss these results in light of psychosocial theory and suggest pedagogical and structural changes that can support more equitable outcomes in online and in-person college-level STEM education. © 2022 National science teaching Association.

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Revolutionizing Healthcare: The Impact of Machine Learning and Artificial Intelligence

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E-Health Telecommunication Systems and Networks 2024年第4期13卷 87-91页

作者： Adebayo Augustine Adeniyi Oluwademiladeayo Samuel Adeniyi Department of Obstetrics and Gynaecology Afe Babalola University Ado Ekiti/Federal Teaching Hospital Ido Ekiti Nigeria Department of Computer Science Bowen University Iwo Nigeria

Machine Learning (ML) and Artificial Intelligence (AI) are transforming the healthcare landscape by enabling data-driven decision-making, enhancing diagnostic accuracy, and improving patient outcomes. This article reviews the current state of AI and ML in healthcare, explores their applications in diagnosis, treatment planning, and patient management, and discusses the challenges and future potential of these technologies in the medical field.

关键词： Artificial Intelligence Machine Learning Healthcare

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Analysis of Banking and Financial Sector Services Using Artificial Intelligence (AI) 1

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Isavnin, Alexey Kosorukova, Irina Madaminov, Inomjon Laxmi Lydia, E. Satapathy, Suresh Department of Business-Informatics and Mathematical Methods in Economics Naberezhnye Chelny Institute KFU Kazan Federal University Naberezhnye Chelny Russia Department of Corporate Finance and Corporate Governance Financial University under the Government of the Russian Federation Moscow Russia Department of Accounting and Auditing Urgench State University Urgench Uzbekistan Department of Computer Science and Engineering Vignan’s Institute of Information Technology Visakhapatnam530049 India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9789819748921

ISBN: (纸本)9789819748914

This research proposes the assessment of banking and financial services using AI to monitor how banks apply AI approaches and the feedback they receive from customers. In order to better track, anticipate, and react to customer behavior, the financial information generated by the growing utilization of digital pay and banking can be mined by banks and other financial institutions. Based on research, secondary sources are reviewed to ascertain the data used in banking and financial services. A methodical survey is designed to gather the main information clients have regarding AI applications. The study is that private banks and other financial institutions use a variety of artificial intelligence (AI) services to better serve their clients and ensure that they are happy with their financial services, as some clients are not happy with banking services. Additionally, the results indicated that clients would be more committed to banking and financial services representatives by providing creative preparation for the advancement of AI practices in the workplace. © The Author(s).

关键词： Decentralized finance

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Tagged barcodes for the topological analysis of gradient-like vector fields

arXiv

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arXiv 2024年

作者： Bannwart, Clemens Landi, Claudia Department of Physics Mathematics and Computer Science University of Modena and Reggio Emilia Italy Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Italy

Intending to introduce a method for the topological analysis of fields, we present a pipeline that takes as an input a weighted and based chain complex, produces a factored chain complex, and encodes it as a barcode of tagged intervals (briefly, a tagged barcode). We show how to apply this pipeline to the weighted and based chain complex of a gradient-like Morse-Smale vector field on a compact Riemannian manifold in both the smooth and discrete settings. Interestingly for computations, it turns out that there is an isometry between factored chain complexes endowed with the interleaving distance and their tagged barcodes endowed with the bottleneck distance. Concerning stability, we show that the map taking a generic enough gradient-like vector field to its barcode of tagged intervals is continuous. Finally, we prove that the tagged barcode of any such vector field can be approximated by the tagged barcode of a combinatorial version of it with arbitrary *** Codes 55N31, 37B35, 57R25 Copyright © 2024, The Authors. All rights reserved.

关键词： Pipelines

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Learning shadow variable representation for treatment effect estimation under collider bias 24

Learning shadow variable representation for treatment effect...

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Proceedings of the 41st International Conference on Machine Learning

作者： Baohong Li Haoxuan Li Ruoxuan Xiong Anpeng Wu Fei Wu Kun Kuang College of Computer Science and Technology Zhejiang University Hangzhou China Center for Data Science Peking University Beijing China Department of Quantitative Theory & Methods Emory University Atlanta

One of the significant challenges in treatment effect estimation is collider bias, a specific form of sample selection bias induced by the common causes of both the treatment and outcome. Identifying treatment effects under collider bias requires well-defined shadow variables in observational data, which are assumed to be related to the outcome and independent of the sample selection mechanism, conditional on the other observed variables. However, finding a valid shadow variable is not an easy task in real-world scenarios and requires domain-specific knowledge from experts. Therefore, in this paper, we propose a novel method that can automatically learn shadow-variable representations from observational data without prior knowledge. To ensure the learned representations satisfy the assumptions of the shadow variable, we introduce a tester to perform hypothesis testing in the representation learning process. We iteratively generate representations and test whether they satisfy the shadow-variable assumptions until they pass the test. With the help of the learned shadow-variable representations, we propose a novel treatment effect estimator to address collider bias. Experiments show that the proposed methods outperform existing treatment effect estimation methods under collider bias and prove their potential application value.

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Artificial Intelligence facing Multidimensional Poverty in Elderly 3

Artificial Intelligence facing Multidimensional Poverty in E...

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3rd Italian Workshop on Artificial Intelligence for an Ageing Society, AIxAS 2022

作者： Bandini, Stefania Borodi, Valeria Marina Chieregato, David Cremaschi, Marco D’Antico, Federica Messina, Vincenzina Terraneo, Marco Terzera, Laura Gasparini, Francesca Department of Computer Science Systems and Communications University of Milano - Bicocca Italy Department of Sociology and Social Research University of Milano - Bicocca Italy Department of Statistics and Quantitative Methods University of Milano - Bicocca Italy

Poverty is a multidimensional concept that, besides the economic status and financial resources, should consider the lack of access to resources enabling a minimum standard of living and participation in society. In particular, elderly people are likely to require help with some or everyday activities and the total costs of this help can be very high and absorb a significant amount of their income, especially when they are alone and not in good health. This work proposes a strategy based on Bayesian Network to identify the risk of poverty in elderly people, relying on multidimensional indicators learned from heterogeneous sources of information, including the difficulty of accessing services, social exclusion and health status. Data cleaning and integration that include socio-demographic indicators are proposed here, and an overall framework of analysis that can be exportable to several other categories of the population at risk of poverty is presented. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Bayesian networks

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Project-Based Learning with historic buildings: immersion in real training environments for the Degree in Technical Architecture

Project-Based Learning with historic buildings: immersion in...

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2021 International Conference of Innovation, Learning and Cooperation, CINAIC 2021

作者： Valentín-Gamazo, David Villanueva Durango, Nelson Tuesta García-Herrero, Francisco Blanco, Ma Isabel Mansilla de las Moras, Ma Cruz Rey Technical Teaching Department European University Miguel de Cervantes Valladolid Spain Department of Computer Architecture and Automatics Computer Science Faculty Complutense University of Madrid Madrid Spain Humanities Department European University Miguel de Cervantes Valladolid Spain

University education in the knowledge branches of Engineering and Architecture, has usually been based on the system of assignments and projects as a practical dynamic of training and assessment in many subjects. With the EHEA arrival, the search for new complementary systems more focused on learning than on teaching or, in other words, on the student than on the teacher, boosted the commitment to the methodology known as Project Based Learning (PBL), oriented to the development of technical skills in contexts similar to those produced in professional environments. In general, these pedagogical scenarios are built on the basis of theoretical models inspired by real cases to a greater or lesser extent. This paper analyzes the method implemented since the 2017-2018 academic year to innovate in the compulsory subject of Rehabilitation, Restoration and Pathology of the Technical Architecture Degree at the European University Miguel de Cervantes, when the PBL applied in case studies of existing historic buildings;providing students with heritage constructions to visit, recognize, measure, diagnose, draw, rehabilitate, restore, etc., and allowing their immersion in situations that introduce them to their future work practice reality. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Students

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Creating an Appropriate computer science and Computational Thinking Graduate Curriculum for K-12 Teachers: Context and Initial Results 12

Creating an Appropriate Computer Science and Computational T...

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12th IEEE Integrated STEM Education Conference, ISEC 2022

作者： Herbert-Berger, Katherine G. Hagiwara, Sumi Rogacki, Elizabeth M. Marlowe, Thomas J. Department of Computer Science Montclair State University Herbertk United States Department of Teaching and Learning Acting Associate Dean for Academic Affairs College of Education and Human Services Montclair State University Herbertk United States Department of Science Mount St Dominic Academy CaldwellNJ United States Department of Mathematics and Computer Science Seton Hall University United States

ISBN: (纸本)9781665484299

K-12 computer science education has challenges related to content and to teacher expertise and comfort. This is further made difficult with inconsistent standards and teacher preparation from state-to-state. We describe a K-12 computer science teaching certificate program, located at Montclair State University, aimed at providing current teachers in northern New Jersey with enhanced understanding of computer science concepts, capabilities, and skills, plus scaffolding of equitable and inclusive teacher practices for applied CS pedagogy. We discuss a brief history of the field, our curriculum and approach and then our first graduating cohort's experiences and challenges. Finally, we discuss our future work. © 2022 IEEE.

关键词： Curricula

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Design of Composite Factor Variance and Component Analysis System Based on Data Analysis Algorithm

Design of Composite Factor Variance and Component Analysis S...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Yue Wang Yiqian Zhang Division of Public Teaching Jinan Vocational College Jinan China Department of Computer Science Jinan Vocational College Jinan China

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

It is very difficult to analyze and mine massive and high-dimensional data. Although the current hardware conditions have greatly improved, they still cannot meet the needs of large-scale, high-dimensional data. In medical research, scientific experiments, and biological gene sequencing, a combination of analysis of variance and principal component analysis was used to study the above issues. On this basis, this article started from two main data analysis methods and studied the relevant theories and algorithms of binary analysis of variance and principal component analysis, in order to achieve the analysis of sample data. The experiment proved that the data analysis based on the system in this article can combine the factor load matrix of the main factor variables to evaluate the contribution of each factor in the investment portfolio to the overall risk. Finally, the optimal stock portfolio was 601989, 601601, 600028, 600104, 601857, and 601988.

关键词： High-dimensional data Sequential analysis Data analysis Standardization Big Data Systems support Portfolios

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