Data Science Tools Application for Business Processes Modelling in Aviation

2021 ◽  
pp. 617-631
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

Data Science Tools Application for Business Processes Modelling in Aviation

2019 ◽  
pp. 176-190
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

Forced Sexual Victimization Among Youth in Custody: Do Risk Factors Vary by Gender and Perpetrator?

2019 ◽  
Vol 100 (2) ◽  
pp. 151-172
Author(s):  
Eileen M. Ahlin
Keyword(s):  
Risk Factors ◽  
Gender Differences ◽  
Logistic Regression ◽  
National Survey ◽  
Regression Models ◽  
Sexual Victimization ◽  
Multivariate Logistic Regression ◽  
Logistic Regression Models ◽  
Factors Associated ◽  
Using Data

There is relatively little literature examining risk factors associated with sexual victimization among youth in custody. The current study explored whether risk of forced sexual victimization among youth in custody differs by gender or perpetrator. Using data from a sample of 8,659 youth who participated in the National Survey of Youth in Custody, multivariate logistic regression models were employed to investigate gender differences in risk factors associated with overall forced sexual victimization and staff-on-inmate and inmate-on-inmate forced sexual victimization. Findings suggest that gender differences are more pronounced when perpetrator type is considered.

scholarly journals Computational Design Map for Heterogeneous Experimental Studies

2021 ◽  
Author(s):  
Chhaya Kulkarni ◽  
Nuzhat Maisha ◽  
Leasha J Schaub ◽  
Jacob Glaser ◽  
Erin Lavik ◽  
...  
Keyword(s):  
Data Mining ◽  
Blood Loss ◽  
Decision Trees ◽  
Data Science ◽  
Computational Design ◽  
Science Methods ◽  
Frequent Patterns ◽  
Multiple Data ◽  
Mining Methods ◽  
Using Data

This paper focuses on the discovery of a computational design map of disparate heterogeneous outcomes from bioinformatics experiments in pig (porcine) studies to help identify key variables impacting the experiment outcomes. Specifically we aim to connect discoveries from disparate laboratory experimentation in the area of trauma, blood loss and blood clotting using data science methods in a collaborative ensemble setting. Trauma related grave injuries cause exsanguination and death, constituting up to 50% of deaths especially in the armed forces. Restricting blood loss in such scenarios usually requires the presence of first responders, which is not feasible in certain cases. Moreover, a traumatic event may lead to a cytokine storm, reflected in the cytokine variables. Hemostatic nanoparticles have been developed to tackle these kinds of situations of trauma and blood loss. This paper highlights a collaborative effort of using data science methods in evaluating the outcomes from a lab study to further understand the efficacy of the nanoparticles. An intravenous administration of hemostatic nanoparticles was executed in pigs that had to undergo hemorrhagic shock and blood loss and other immune response variables, cytokine response variables are measured. Thus, through various hemostatic nanoparticles used in the intervention, multiple data outcomes are produced and it becomes critical to understand which nanoparticles are critical and what variables are key to study further variations in the lab. We propose a collaborative data mining framework which combines the results from multiple data mining methods to discover impactful features. We used frequent patterns observed in the data from these experiments. We further validate the connections between these frequent rules by comparing the results with decision trees and feature ranking. Both the frequent patterns and the decision trees help us identify the critical variables that stand out in the lab studies and need further validation and follow up in future studies. The outcomes from the data mining methods help produce a computational design map of the experimental results. Our preliminary results from such a computational design map provided insights in determining which features can help in designing the most effective hemostatic nanoparticles.

Intelligent Big Data Analytics

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Decision ◽  
Technology System ◽  
System Service ◽  
Managerial Perspective

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores intelligent big data analytics from a managerial perspective. More specifically, it first looks at the age of trinity and argues that intelligent big data analytics is at the center of the age of trinity. This chapter then proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service, and management for improving business decision making. Then it examines intelligent big data analytics for management taking into account four managerial functions: planning, organizing, leading, and controlling. The proposed approach in this chapter might facilitate the research and development of intelligent big data analytics, big data analytics, business intelligence, artificial intelligence, and data science.

Security in Mission Critical Communication Systems

2017 ◽  
pp. 270-291 ◽  
Author(s):  
Karen Medhat ◽  
Rabie A. Ramadan ◽  
Ihab Talkhan
Keyword(s):  
Wireless Communications ◽  
Decision Tree ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Binary Tree ◽  
Communication Systems ◽  
Clustering Algorithm ◽  
Critical Systems ◽  
Mission Critical

This chapter introduces two different algorithms to detect intrusions in mission critical communication systems to guarantee their security. The first algorithm is a classification algorithm which applies the concept of supervised learning. The second algorithm is a clustering algorithm which applies the concept of unsupervised learning. The algorithms detect intrusions using a set of detection rules that are structured in the form of decision trees. The algorithms are described in details and their results on well-known dataset are introduced. An enhancement for the J48algorithm is also introduced, where the decision tree for the algorithm is changed to a binary tree. The change enhances the complexity to reach a decision. The chapter includes a brief introduction about the security in Mission critical systems and the reason behind securing such systems. It introduces different methodologies that were introduced to detect intrusions in wireless communications.

Managing GIS and Spatial Data to Support Effective Decision Making Throughout the Pipeline Lifecycle

2006 ◽  
Author(s):  
Robert van Wyngaarden ◽  
Mel VanderWal
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Spatial Data ◽  
Business Processes ◽  
Cost Savings ◽  
Regulatory Compliance ◽  
Business Decision ◽  
Geographic Data ◽  
Spatial Data Warehouse ◽  
The Individual

Many pipeline industry managers and senior officials intuitively understand that location is important to most aspects related to pipelines throughout the life-cycle — from project concept, through construction and operations and finally to decommissioning. However, many organizations are not taking full advantage of location as being a vital component to support business decision-making across the entire range of activities undertaken by pipeline companies. A Geographic Information System (GIS) is a tool that takes advantage of geography. GIS is ideally suited for the storage, display, and output of geographic data, and moreover, the analysis and modeling of geographic data. While GIS has been around as a technology for over 30 years it is only in the last several years that it has started to be extensively used within the pipeline industry. Most managers have heard about GIS. Many organizations have already started to implement GIS and CAD-based solutions through individual projects and with a technical focus of automating work flows or business processes such as generating alignment sheets, regulatory compliance, integrity management, and land management to name a few. Given that many of these applications tend to be stand-alone or isolated developments, pipeline companies need to look at the complete spatial environment of all potential tools and applications, and support this with a vision of a common spatial data warehouse in a holistic sense. Any company that embraces a continuous gathering of spatial data throughout the pipeline life-cyle will have a significant knowledge base whose value will increase over time. A spatial data warehouse of truly integrated environmental, engineering and socioeconomic factors related to a pipeline during the entire lifecycle will have a total value that transcends the value of the individual factors. The Return on Investment (ROI) of a properly developed GIS framework and spatial data warehouse looking at all operational demands and support applications will certainly be many times over the original expenditure as measured in cost savings as well as better decision making. This paper will present insights and approaches into how to properly and effectively leverage the spatial data asset and in deploying GIS throughout the enterprise. These include addressing all of the elements that are key in implementing GIS — hardware, software, data, people and methods — as well as considering some of the ROI and value-based measures for GIS success.

Organizational Commitment and Involvement

2013 ◽  
Vol 43 (2_suppl) ◽  
pp. 61S-83S ◽  
Author(s):  
Lili Wang ◽  
Robert F. Ashcraft
Keyword(s):  
Logistic Regression ◽  
Organizational Commitment ◽  
Regression Models ◽  
Community Organizations ◽  
Professional Associations ◽  
Charitable Donations ◽  
Logistic Regression Models ◽  
Using Data ◽  
Tax Deduction ◽  
Practical Implications

Charitable gifts provide vital support for the operation of many associations. Using data collected from members of six professional associations, this study examines the factors that influence charitable donations to this particular type of organization. The results of logistic regression models suggest that the decision to give to associations is not driven by the inducement of tax deduction but by members’ commitment to associations, their level of engagement in these organizations, and whether they were solicited for a charitable gift. In addition, retired non-U.S. members who have supported other community organizations are more likely to donate to associations as are members working for government and those holding higher job positions. The results suggest that soliciting donations significantly increases the propensity to give, particularly among members with low educational attainment. Practical implications of the empirical findings are discussed.

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