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Artificial Intelligence and Social Work
eBook/eTreatise | Cambridge University Press | 2018

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Imprint
New York : Cambridge University Press, 2018.
©2018.
Descript
1 online resource (270 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series
Contents
Cover -- Half-title -- Series information -- Title page -- Copyright information -- Contents -- Contributors -- Part I -- 1 Merging Social Work Science and Computer Science for Social Good -- Motivations for Our Research -- What Is Social Work -- What Is AI -- The Unlikely Partnership -- New Science for Both Sides -- The Structure of the Book -- 2 The Causes and Consequences of Youth Homelessness -- The Extent of Youth Homelessness -- Causes of Youth Homelessness -- Thrown Out of Home/Run Away from Home -- Aging Out of Foster Care -- Sexual- and Gender-Minority Youth -- Travelers -- Consequences of Youth Homelessness -- Experiences of Violence -- Mental Health -- Substance Use/Abuse -- Contraception and Pregnancy -- The Importance of Social Networks -- HIV Prevention for Homeless Youth -- Why We Need Artificial Intelligence -- References -- 3 Using Social Networks to Raise HIV Awareness among Homeless Youth -- Introduction -- Related Work -- HEALER's Design -- Network Construction Application -- DIME Solver -- HEALER Design -- DIME Problem Statement -- Uncertain Network -- Influence Model -- Problem Statement -- The Value of Information -- Computational Hardness -- HEAL: DIME Problem Solver -- POMDP Model -- HEAL -- DOSIM: A New Algorithm for the DIME Problem -- Experimental Results -- Conclusion -- References -- 4 Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application -- Introduction -- Pilot Study Pipeline -- Results from the Field -- Challenges Uncovered -- Conclusion, Limitations, and Lessons Learned -- Acknowledgments -- References -- 5 Influence Maximization with Unknown Network Structure -- Introduction -- Exploratory Influence Maximization -- Related Work -- The ARISEN Algorithm -- Initial weights -- Refining the weights -- Experiments -- References -- Part II.
6 Maximizing the Spread of Sexual Health Information in a Multimodal Communication Network of Young Black Women -- Introduction -- Methods -- YBW Quantitative Network Survey - Data Collection -- Mathematically Modeling Information Diffusion in the YBW Network -- Algorithms for Solving the Maximum Influence Problem -- Results and Discussion -- Conclusions and Future Work -- 7 Minimizing Violence in Homeless Youth -- Introduction -- Data Collection -- Dependent Variable -- Model -- Voter Model -- Uncertain Voter Model -- Greedy Minimization -- Uncertainty in Time -- Experiments -- Discussion -- Multiple Waves -- Utilizing Other Variables -- Implications from Social Work Perspective -- References -- 8 Artificial Intelligence for Improving Access to Sexual Health Necessities for Youth Experiencing Homelessness -- Introduction -- Homelessness and HIV Risk -- Our Innovation: "Smart Kiosks" -- Problem Definition -- Weighted K-Center Problem -- Datasets -- Area Division -- Model Parameters -- Points V -- Existing Centers V'' -- Distances d(v, r) -- Weights W(v) -- Proposed Approach -- Approximation Guarantees of the Greedy Approach -- Population Estimation -- Results -- Discussion and Conclusions -- References -- 9 Know-Stress: Predictive Modeling of Stress among Diabetes Patients under Varying Conditions -- Introduction -- Data Description -- A Brief Background of the Data Set Used -- Description of Variables Presented in the Dataset -- Aim of Our Analysis -- More Detailed Description of the Aims of the Project -- Predicted Variable Creation -- Determining Optimal Cutoff -- Feature Selection and Various Data Sets -- Experimental Setup -- Models of Classification -- Decision Tree -- Logistic Regression -- Feature Selection -- Metrics of Evaluation -- Experimental Results and Discussion -- Discussion on the Results -- Optimal Cutoff Range -- Model Stability.
Feature Selection -- Conclusion -- Future Work -- References -- 10 A Multidisciplinary Study on the Relationship between Foster Care Attributes and Posttraumatic Stress Disorder Symptoms on Foster Youth -- Introduction -- Literature Review -- PTSD Symptoms among Youth with Homelessness and Foster Care Histories -- Machine Learning within the Social Sciences -- Data Augmentation -- Data Samples and Procedures -- Data: Selected Variables and Outcomes for PTSD Prediction -- Methods -- Data Preprocessing for Models -- Modeling Techniques and Evaluations -- Evaluation Techniques -- Model 0 (baseline): Logistic Regression -- Model 1: Neural Networks -- Model 2: Decision Tree -- Model 3: Bayesian Networks -- Discussion and Conclusions -- References -- 11 Artificial Intelligence to Predict Intimate Partner Violence Perpetration -- Introduction -- Problem Definition -- Data Set Description -- Data Analysis -- Theoretical Quantitative Analysis -- Statistical Analysis -- P-value -- LASSO -- Support Vector Machines (SVM) -- Random Forest -- Final Rankings -- Methodology -- Features -- Baseline -- Learning Algorithms -- Neural Networks -- Logistic Regression -- Deep Support Vector Machine -- Results -- Conclusion -- References -- 12 SHIHbot: Sexual Health Information on HIV/AIDS, chatbot -- Motivating Social Problem -- Overview -- Data Gathering -- NPCEditor -- Deployment -- Messenger as the User Interface -- Intermediate Web Service as the Proxy -- NPCEditor as the Backend -- Evaluation Metrics -- Linguistic-Driven -- Online-Driven -- Social-Driven -- Alpha Test -- Data -- Observations -- Future Work and Directions -- Support for Additional Platforms -- Obtain Additional Information -- Add Additional Functionality -- Analyze User Data to Improve SHIHbot -- References -- 13 Ethics and Artificial Intelligence in Public Health Social Work -- Introduction.
Case Study: Adapting TND Network for Homeless Youth -- Beneficence Problems -- Moral Duties and Beneficence Problems -- A Framework for Resolving Conflicts -- Operationalizing the Framework -- Notes -- References -- Glossary -- Index.
Summary
An introductory guide with real-life examples on using AI to help homeless youth, diabetes patients, and other social welfare interventions.
Note
Description based on publisher supplied metadata and other sources.
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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ISBN
9781108645225 (electronic bk.)
9781108425995
MARC
MiAaPQ
20210429153515.0
m o d |
cr cnu||||||||
210429s2018 xx o ||||0 eng dnam3i
(MiAaPQ)EBC5569639
(Au-PeEL)EBL5569639
(CaPaEBR)ebr11629298
(OCoLC)1060524972
MiAaPQ eng rda pn MiAaPQ MiAaPQ
https://ebookcentral.proquest.com/lib/duqlaw-ebooks/detail.action?docID=5569639 Available via eBookCentral to DU Community *Access restrictions may apply*
ebrary file 02-26-2016 PDA
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