Teles, G., Rodrigues, J. J., Rabê, R. A., & Kozlov, S. A. (2020). Artificial neural network and Bayesian network models for credit risk prediction. Journal of Artificial Intelligence and Systems, 2, 118-132.
Lakhani, M., Dhotre, B., & Giri, S. (2019). Prediction of credit risks in lending bank loans using machine learning. SAARJ Journal on Banking & Insurance Research, 8(1), 55-61.
Sun, T., & Vasarhelyi, M. A. (2018). Predicting credit card delinquencies: An application of deep neural networks. Intelligent Systems in Accounting, Finance and Management, 25(4), 174-189.
Fitzpatrick, Trevor & Mues, Christophe, 2016. “An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market,” European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
Huang, X., Liu, X., & Ren, Y. (2018). Enterprise credit risk evaluation based on neural network algorithm. Cognitive Systems Research, 52, 317-324.
Chi, G., Uddin, M. S., Abedin, M. Z., & Yuan, K. (2019). Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches. International Journal on Artificial Intelligence Tools, 28(05), 1950017.
Barboza, Flavio & Kimura, Herbert & Altman, Edward. (2017). Machine Learning Models and Bankruptcy Prediction. Expert Systems with Applications. 83. 10.1016/j.eswa.2017.04.006.
https://www.sciencedirect.com/science/article/abs/pii/S0957417418301179(Predicting mortgage default using convolutional neural networks)
Graves, J. T., Acquisti, A., & Christin, N. (2018). Should Credit Card Issuers Reissue Cards in
Response to a Data Breach? Uncertainty and Transparency in Metrics for Data Security
Policymaking. ACM Transactions on Internet Technology (TOIT), 18(4), 1-19.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud
detection: realistic modeling and a novel learning strategy. IEEE transactions on neural
networks and learning systems, 29(8), 3784-3797.
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S., & Zeineddine, H. (2019). An
experimental study with imbalanced classification approaches for credit card fraud
detection. IEEE Access, 7, 93010-93022.
Kalid, S. N., Ng, K. H., Tong, G. K., & Khor, K. C. (2020). A Multiple Classifiers System for
Anomaly Detection in Credit Card Data with Unbalanced and Overlapped Classes. IEEE
Access, 8, 28210-28221.
Taha, A. A., & Malebary, S. J. (2020). An Intelligent Approach to Credit Card Fraud Detection
Using an Optimized Light Gradient Boosting Machine. IEEE Access, 8, 25579-25587.
Can, B., Yavuz, A. G., Karsligil, E. M., & Guvensan, M. A. (2020). A Closer Look into the
Characteristics of Fraudulent Card Transactions. IEEE Access, 8, 166095-166109.
Kundu, A., Panigrahi, S., Sural, S., & Majumdar, A. K. (2009). Blast-ssaha hybridization for
credit card fraud detection. IEEE transactions on dependable and Secure Computing,
6(4), 309-315.
Al-Khater, W. A., Al-Maadeed, S., Ahmed, A. A., Sadiq, A. S., & Khan, M. K. (2020).
Comprehensive Review of Cybercrime Detection Techniques. IEEE Access, 8, 137293-
137311.
Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud
detection using AdaBoost and majority voting. IEEE access, 6, 14277-14284.
>American history homework help
UncategorizedWhile this is an informal assignment, you should write in complete sentences and pay attention to grammar and style.
You will find the topic, assigned readings, and preliminary instructions for this week’s discussion topic in the class announcement titled “Week 12-Discussion.”
The Announcement also contains the editable Google Doc file with your group’s answers.
You will be graded both on your group discussion document (5 pts) and on your reflection (5 pts). The maximum points possible is 10.
Scoring Rubric for Discussions (10 points total) and the total will be entered under your discussion-reflection assignment.
Using Health Information Technology as a Source of Evidence-Based Practice in Nursing
Nursing HomeworksUsing Health Information Technology as a Source of Evidence-Based Practice in Nursing
Before the digital revolution, health information technology supplied very limited support for evidence-based practice. If nurses wanted to be informed about cutting-edge research, their best bet was to either subscribe to leading journals or make periodic trips to the library. With the establishment of research databases, however, nurses became empowered to learn about and facilitate interdisciplinary and translational research. Databases are just one example of how health information technology supports evidence-based practice.
ORDER A CUSTOM-WRITTEN PAPER NOW
To prepare:
Twelve-hour shifts are problematic for patient and nurse safety, and yet hospitals continue to keep the 12-hour shift schedule. In 2004, the Institute of Medicine (Board on Health Care Services & Institute of Medicine, 2004) published a report that referred to studies as early as 1988 that discussed the negative effects of rotating shifts on intervention accuracy. Workers with 12-hour shifts realized more fatigue than workers on 8-hour shifts. In another study done in Turkey by Ilhan, Durukan, Aras, Turkcuoglu, and Aygun (2006), factors relating to increased risk for injury were age of 24 or less, less than 4 years of nursing experience, working in the surgical intensive care units, and working for more than 8 hours.
Question:
Post a description of your practice concern. Outline how you used health information technology to locate evidence-based practices that address this concern. Cite and include insights from the resources. Analyze how health information technology supports evidence-based practice.
Explain how nursing practice has changed over time and how this evolution has changed the scope of practice and the approach to treating the individual.
UncategorizedDetails:
The field of nursing has changed over time. In a 750-1,000 word paper, discuss nursing practice today by addressing the following:
Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.c
Select a nursing issue or current trend of interest to you. Research and gather as much information as you can on that issue.
UncategorizedCurrent Nursing Trends and Issues Paper
Select a nursing issue or current trend of interest to you. Research and gather as much information as you can on that issue. Write a paper of no more than 4 pages on that issue making sure to address the following:
1. Introduction and background of the nursing trend or issue
2. Describe the trend/issue in detail – What is the cause of the trend/issue, who is involved, who is affected?
3. What are the overall healthcare and nursing specific impact of this nursing trend or issue?
4. What interventions or recommendation is available to manage this nursing trend or issue?
5. Conclusion
6. Must be in APA format, no more than 4 pages (less cover page and references), with at least 3 current peer-reviewed articles cited.
RUBRIC: Current Nursing Trends and IssuesPaper
(4%) Introduction and background of the nursing trend or issue.
(6%) Describe the issue in detail – What is the cause of the trend/issue, who is involved, who is affected?
(6%) What are the overall healthcare and nursing-specific impact of this nursing trend or issue?
(6%) What interventions or recommendation are available to manage this nursing trend or issue?
(4%) Conclusion.
(4%) Must be in APA format, no more than 4 pages (less cover page and references), with at least 3 current peer-reviewed articles cited.
Evaluating a nursing leadership problem and the dynamics
UncategorizedREQUIRED RESOURCES TO DO THIS JOB ATTACHED AT THE END !!!
Create 1–2 page outlines of your response plan for three intervention scenarios.
Nurse leaders need to quickly identify a strategy for evaluating a nursing leadership problem and the dynamics related to the problem, in order to orchestrate intervention efforts and put together a plan of action that leads to stakeholder cooperation.
By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and assessment criteria:
Preparation
Use the Capella library and the Internet to research change theory, leadership, and communication strategies. Use the Suggested Resources to research leadership and communication concepts and change theory.
Rationale for this assessment:
Nurse leaders solve problems or resolve conflict on a daily basis. Understanding how change theory can be applied to a situation and examining various types of interventions in advance can relieve pressure on the nurse leader and improve the workplace environment and outcomes. Rehearsing potential interventions provides a mental toolkit on which to rely during stressful times.
Your management training workshop continues:
Deliverables: Submit three Response Plans to complete this assessment.
>Law homework help
UncategorizedInstructions
Did read the leadership theories for this area? If not, click on image below to read.
*****************************
Case Study
Learning Outcomes: Demonstrate general understanding of:
Scenario:
Assessing the Situation:
Resolution:
As a Chief,
EVALUATE BASIC RESEARCH STRATEGIES APPLICABLE TO HEALTHCARE SETTINGS FOR INFORMING RESEARCH PROPOSALS.
UncategorizedHealthcare administrators, managers, and executives are responsible for planning, directing, and coordinating health services at various levels for the populations they serve. Interpreting research is integral to the role of a healthcare professional, especially when conducting a needs assessment for program planning.
In this course, you will choose a clinical area of interest related to healthcare administration and create an annotated bibliography. For your final assessment, you will compose an integrated review. In this review, you will discuss the criteria necessary for inclusion or exclusion in the research study, critique the quality of each study, and present a synthesis of the results.
This integrated review will address the following course outcomes:
1. Critique ethical issues in healthcare research for their influence on compliance with rules and regulations
2. Evaluate basic research strategies applicable to healthcare settings for informing research proposals
3. Assess the appropriateness of utilizing secondary databases in healthcare research as an alternative to conducting original research
4. Justify the selection of specific data analysis methodology in published healthcare research for informing healthcare research methodology
5. Select healthcare administration issues to research in validating the need for program evaluation
Prompt
Using the six peer-reviewed literature articles from your annotated bibliography, compose an integrated review that focuses on a clinical issue of interest. Ensure that the topic of this integrated review is viewed from the perspective of a healthcare professional who is looking to validate the need for program evaluation at your hospital, even if your annotated bibliography was not this focused.
Specifically, your integrated review should focus on the following critical elements:
I. Abstract
Craft a well-drafted abstract. Be sure to adhere to the guidelines from the latest edition of the American Psychological Association’s style guide. Consider the appropriate length for your audience.
II. Introduction
a) State the purpose, aims, or objectives of the integrated review. What do you wish to achieve through the drafting of this review? Be explicit in
your answer.
b) Introduce the topic of interest. Why is this topic the focus of the review?
c) What is the research question you are going to focus on? If you were to prepare a research proposal, what would your hypothesis be? Why?
d) What variables are of interest to you? How will these variables help you throughout this integrated review? Be sure to label the types of
variables each of these are.
e) Discuss the background and significance of the problem to healthcare administration.
ANALYSIS OF READING SOURCE
UncategorizedFrom the ‘Resources’ section, read Parts II and III–Chapters 10 – 20 of “What is a Disaster” by Perry & Quarantelli (eds.).
Assignment: Provide an approximate 1500-word document analyzing important concepts in the readings. Ensure your apply the discussion tenets from the contributors to you work including the work of Barton, Boin, Buckle, Smith, Stallings, Perry, and Quarantelli. Assume that you are writing for an uninformed reader that knows nothing about the topic and has not read what you read. Provide an introduction that gives the background of the resource that you are reviewing, so the reader will understand what they’re reading and why. Include the following topics in the discussion:
– Discuss the relationship between disaster and stress including the difference between individual and collective stress, and why is it important?
– Discuss Stallings theses; analyze and discuss how various contributors add and detract from his work? Which criticisms are valid and which not?
– Provide an analysis of the discussions throughout the readings (Barton, Boin, Buckle, Smith, Stallings, Perry, and Quarantelli).
– Discuss how Stallings work inform the future research that is still required? What would be the value of that research?
DO NOT list out the topics or questions and answer them. They are not meant to be all-inclusive, and your reader will not understand the context. Rather, give an overview of the author’s entire body of work, using the topics as guidelines. Ensure that you meet or exceed the 1500-word target, and that your paper meets APA presentation requirements.
Accounting homework help
UncategorizedACCT 3300





Excel Budgeting Project
Open the Excel spreadsheet and click on the Problem tab at the bottom to view the instructions for the project. Click on the Worksheet tab and begin entering the formulas provided below.
Scroll down the Worksheet page until you come to the Answer Section. The Sales Budget section is listed first. In the shaded cells, enter the appropriate formula for each cell shown below. After typing in the formula, click Enter on your keyboard. Be sure you have each formula in the exact cell (row and column) as shown below.
You will notice that the amounts and formulas for August and September will appear when you complete the July entries. These additional formulas have been pre-programmed into the spreadsheet to save time. Also notice that the cell references in the formulas (for example, B10 is cell reference for the cell in column B, row 10) are to the data section at the top of the worksheet tab.
Formulas for the Unit Purchases budget will be entered next. See below.
Next, enter the formulas for Cash Budget. Do not be concerned when the August and September amounts do not display immediately. The will show when you complete this section. Note there is one formula for Column C in this section.
When these formulas are completed, move down to the Income Statement section and enter the following formulas.
Then complete the Balance Sheet formulas.
Once you have completed all the formulas, you have completed Items 1 and 2 of the problem. Save your worksheet as instructed in Item 2 and use the check figures to verify your amounts. Continue to Items 3 and 4 and when finished with those sections, save your file again as instructed in Item 4 and submit both files using the Assignment link at the top of the Blackboard section describing the assignment.
Information Systems homework help
UncategorizedTeles, G., Rodrigues, J. J., Rabê, R. A., & Kozlov, S. A. (2020). Artificial neural network and Bayesian network models for credit risk prediction. Journal of Artificial Intelligence and Systems, 2, 118-132.
Lakhani, M., Dhotre, B., & Giri, S. (2019). Prediction of credit risks in lending bank loans using machine learning. SAARJ Journal on Banking & Insurance Research, 8(1), 55-61.
Sun, T., & Vasarhelyi, M. A. (2018). Predicting credit card delinquencies: An application of deep neural networks. Intelligent Systems in Accounting, Finance and Management, 25(4), 174-189.
Fitzpatrick, Trevor & Mues, Christophe, 2016. “An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market,” European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
Huang, X., Liu, X., & Ren, Y. (2018). Enterprise credit risk evaluation based on neural network algorithm. Cognitive Systems Research, 52, 317-324.
Chi, G., Uddin, M. S., Abedin, M. Z., & Yuan, K. (2019). Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches. International Journal on Artificial Intelligence Tools, 28(05), 1950017.
Barboza, Flavio & Kimura, Herbert & Altman, Edward. (2017). Machine Learning Models and Bankruptcy Prediction. Expert Systems with Applications. 83. 10.1016/j.eswa.2017.04.006.
https://www.sciencedirect.com/science/article/abs/pii/S0957417418301179(Predicting mortgage default using convolutional neural networks)
Graves, J. T., Acquisti, A., & Christin, N. (2018). Should Credit Card Issuers Reissue Cards in
Response to a Data Breach? Uncertainty and Transparency in Metrics for Data Security
Policymaking. ACM Transactions on Internet Technology (TOIT), 18(4), 1-19.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud
detection: realistic modeling and a novel learning strategy. IEEE transactions on neural
networks and learning systems, 29(8), 3784-3797.
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S., & Zeineddine, H. (2019). An
experimental study with imbalanced classification approaches for credit card fraud
detection. IEEE Access, 7, 93010-93022.
Kalid, S. N., Ng, K. H., Tong, G. K., & Khor, K. C. (2020). A Multiple Classifiers System for
Anomaly Detection in Credit Card Data with Unbalanced and Overlapped Classes. IEEE
Access, 8, 28210-28221.
Taha, A. A., & Malebary, S. J. (2020). An Intelligent Approach to Credit Card Fraud Detection
Using an Optimized Light Gradient Boosting Machine. IEEE Access, 8, 25579-25587.
Can, B., Yavuz, A. G., Karsligil, E. M., & Guvensan, M. A. (2020). A Closer Look into the
Characteristics of Fraudulent Card Transactions. IEEE Access, 8, 166095-166109.
Kundu, A., Panigrahi, S., Sural, S., & Majumdar, A. K. (2009). Blast-ssaha hybridization for
credit card fraud detection. IEEE transactions on dependable and Secure Computing,
6(4), 309-315.
Al-Khater, W. A., Al-Maadeed, S., Ahmed, A. A., Sadiq, A. S., & Khan, M. K. (2020).
Comprehensive Review of Cybercrime Detection Techniques. IEEE Access, 8, 137293-
137311.
Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud
detection using AdaBoost and majority voting. IEEE access, 6, 14277-14284.