scholarly journals Efficacious Discriminant Analysis (Classifier) Measures for End Users

2016 ◽  
Vol 2016 ◽  
pp. 1-17
Author(s):  
E. Earl Eiland ◽  
Lorie M. Liebrock
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Discriminant Analysis ◽  
End Users ◽  
Summary Statistics ◽  
Performance Information ◽  
End User ◽  
Classification Prediction ◽  
Test Outcomes

Many problem domains utilize discriminant analysis, for example, classification, prediction, and diagnoses, by applying artificial intelligence and machine learning. However, the results are rarely perfect and errors can cause significant losses. Hence, end users are best served when they have performance information relevant to their need. Starting with the most basic questions, this study considers eight summary statistics often seen in the literature and evaluates their end user efficacy. Results lead to proposed criteria necessary for end user efficacious summary statistics. Testing the same eight summary statistics shows that none satisfy all of the criteria. Hence, two criteria-compliant summary statistics are introduced. To show how end users can benefit, measure utility is demonstrated on two problems. A key finding of this study is that researchers can make their test outcomes more relevant to end users with minor changes in their analyses and presentation.

scholarly journals Efficacious End User Measures—Part 1: Relative Class Size and End User Problem Domains

2013 ◽  
Vol 2013 ◽  
pp. 1-22 ◽  
Author(s):  
E. Earl Eiland ◽  
Lorie M. Liebrock
Keyword(s):  
Class Size ◽  
Joint Probability ◽  
End Users ◽  
Performance Information ◽  
End User ◽  
Loss Of Life ◽  
Relative Class ◽  
Probability Table ◽  
Information Coefficient ◽  
Classification Prediction

Biological and medical endeavors are beginning to realize the benefits of artificial intelligence and machine learning. However, classification, prediction, and diagnostic (CPD) errors can cause significant losses, even loss of life. Hence, end users are best served when they have performance information relevant to their needs, this paper’s focus. Relative class size (rCS) is commonly recognized as a confounding factor in CPD evaluation. Unfortunately, rCS-invariant measures are not easily mapped to end user conditions. We determine a cause of rCS invariance, joint probability table (JPT) normalization. JPT normalization means that more end user efficacious measures can be used without sacrificing invariance. An important revelation is that without data normalization, the Matthews correlation coefficient (MCC) and information coefficient (IC) are not relative class size invariants; this is a potential source of confusion, as we found not all reports using MCC or IC normalize their data. We derive MCC rCS-invariant expression. JPT normalization can be extended to allow JPT rCS to be set to any desired value (JPT tuning). This makes sensitivity analysis feasible, a benefit to both applied researchers and practitioners (end users). We apply our findings to two published CPD studies to illustrate how end users benefit.

scholarly journals Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems

2021 ◽  
Vol 26 (1) ◽  
pp. 22-30
Author(s):  
Oksana Ņikiforova ◽  
Vitaly Zabiniako ◽  
Jurijs Kornienko ◽  
Madara Gasparoviča-Asīte ◽  
Amanda Siliņa
Keyword(s):  
Machine Learning ◽  
User Experience ◽  
End Users ◽  
End User ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Usability Problems ◽  
Improvement Potential ◽  
Usability Analysis ◽  
Transformation Methods

Abstract Improving IS (Information System) end-user experience is one of the most important tasks in the analysis of end-users behaviour, evaluation and identification of its improvement potential. However, the application of Machine Learning methods for the UX (User Experience) usability and effic iency improvement is not widely researched. In the context of the usability analysis, the information about behaviour of end-users could be used as an input, while in the output data the focus should be made on non-trivial or difficult attention-grabbing events and scenarios. The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define dependency between these data and desired output, which can help to apply Machine Learning / graph algorithms to user activity records.

scholarly journals Supporting End-User Understanding of Classification Errors: Visualization and Usability Issues

2019 ◽  
Vol 7 ◽  
pp. 29
Author(s):  
Emma M.A.L. Beauxis-Aussalet ◽  
Joost Van Doorn ◽  
Lynda Hardman
Keyword(s):  
Machine Learning ◽  
Roc Curves ◽  
End Users ◽  
False Positives ◽  
Visual Features ◽  
End User ◽  
False Negatives ◽  
Informed Decisions ◽  
Classification Errors ◽  
Confusion Matrices

Classifiers are applied in many domains where classification errors have significant implications. However, end-users may not always understand the errors and their impact, as error visualizations are typically designed for experts and for improving classifiers. We discuss the specific needs of classifiers' end-users, and a simplified visualization designed to address them. We evaluate this design with users from three levels of expertise, and compare it with ROC curves and confusion matrices. We identify key difficulties with understanding the classification errors, and how visualizations addressed or aggravated them. The main issues concerned confusions of the actual and predicted classes (e.g., confusion of False Positives and False Negatives). The machine learning terminology, complexity of ROC curves, and symmetry of confusion matrices aggravated the confusions. The end-user-oriented visualization reduced the difficulties by using several visual features to clarify the actual and predicted classes, and more tangible metrics and representation. Our results contribute to supporting end-users' understanding of classification errors, and informed decisions when choosing or tuning classifiers.

scholarly journals Post Construction Quality Evaluation – A Manufacturer’s Use of the End User to Minimize Risk

2013 ◽  
Vol 5 (1) ◽  
pp. 20
Author(s):  
Dhaval Gajjar ◽  
Kenneth Sullivan ◽  
Dean Kashiwagi
Keyword(s):  
Research Group ◽  
Quality Evaluation ◽  
Retention Rate ◽  
Customer Retention ◽  
End Users ◽  
Performance Information ◽  
End User ◽  
Construction Quality ◽  
Satisfaction Rating

A roofing manufacturer is motivated to increase accountability, minimize risk and differentiate themselves from other manufacturers to increase their sales. In order to achieve this, the manufacturer approached the research group to implement a warranty program that measures the performance information of their systems and applicators. The manufacturer submits a list of warranted jobs to the researchers, researchers perform a satisfaction check by calling the end users and report back to the manufacturer. Concepts utilized by the manufacturer include the use of warranty to ensure performance decreases risk, transparency is the best way to mitigate risk and risk can be mitigated before it happens. The research revealed that warranty program minimizes the risk for manufacturer and clients and helps differentiates the manufacturer by identifying end users that are not satisfied, applicators that are low performing, jobs that are leaking, customer retention rate and having a running log of satisfaction rating for every warranted job.

scholarly journals Dimension-Based Interactions with Virtual Assistants: A Co-Design Project with Design Fictions

2021 ◽  
Vol 5 (12) ◽  
pp. 78
Author(s):  
Hebitz C. H. Lau ◽  
Jeffrey C. F. Ho
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Design Process ◽  
End Users ◽  
Daily Lives ◽  
Design Project ◽  
And Function ◽  
Future Technologies ◽  
Design Fiction ◽  
Unrealistic Expectations

This study presents a co-design project that invites participants with little or no background in artificial intelligence (AI) and machine learning (ML) to design their ideal virtual assistants (VAs) for everyday (/daily) use. VAs are differently designed and function when integrated into people’s daily lives (e.g., voice-controlled VAs are designed to blend in based on their natural qualities). To further understand users’ ideas of their ideal VA designs, participants were invited to generate designs of personal VAs. However, end users may have unrealistic expectations of future technologies. Therefore, design fiction was adopted as a method of guiding the participants’ image of the future and carefully managing their realistic, as well as unrealistic, expectations of future technologies. The result suggests the need for a human–AI relationship based on controls with various dimensions (e.g., vocalness degree and autonomy level) instead of specific features. The design insights are discussed in detail. Additionally, the co-design process offers insights into how users can participate in AI/ML designs.

Current Trends in Deep Learning Frameworks With Opportunities and Future Prospectus

2020 ◽  
pp. 63-77
Author(s):  
Chitra A. Dhawale ◽  
Krtika Dhawale
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
End Users ◽  
The Other ◽  
Qualitative Review ◽  
Current Trends ◽  
Real Time Applications ◽  
Learning Frameworks

Artificial Intelligence (AI) is going through its golden era by playing an important role in various real-time applications. Most AI applications are using Machine learning and it represents the most promising path to strong AI. On the other hand, Deep Learning (DL), which is itself a kind of Machine Learning (ML), is becoming more and more popular and successful at different use cases, and is at the peak of developments. Hence, DL is becoming a leader in this domain. To foster the growth of the DL community to a greater extent, many open source frameworks are available which implemented DL algorithms. Each framework is based on an algorithm with specific applications. This chapter provides a brief qualitative review of the most popular and comprehensive DL frameworks, and informs end users of trends in DL Frameworks. This helps them make an informed decision to choose the best DL framework that suits their needs, resources, and applications so they choose a proper career.

scholarly journals Cybersecurity Education to Create Awareness in Artificial Intelligence Applications for Developers and End Users

2020 ◽  
Vol 25 (2) ◽  
pp. 85-92
Author(s):  
Maurice Dawson ◽  
Annamaria Szakonyi
Keyword(s):  
Artificial Intelligence ◽  
End Users ◽  
End User ◽  
New Era ◽  
Societal Benefits

AbstractThis new era brings new promises of technology that will bring economic and societal benefits. Artificial Intelligence is to be the disruptor for work and even military technological applications. However, developers and end-users will play keys roles in how this technology is developed and ultimately used. Among these two groups, there are cybersecurity concerns that need to be considered. In this paper, the researchers address the process of secure development and testing. Also, for the end-user appropriate methods, procedures, and recommendations are defined that can mitigate the overall use of this technology within an enterprise.

scholarly journals Demystifying Artificial Intelligence for End-Users: Findings from a Participatory Machine Learning Show

2021 ◽  
pp. 257-270
Author(s):  
Katharina Weitz ◽  
Ruben Schlagowski ◽  
Elisabeth André
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
End Users

scholarly journals Towards Machine Learning Models as a Key Mean to Train and Optimize Multi-view Web Services Proxy Security Layer

2018 ◽  
Vol 6 (4) ◽  
pp. 65
Author(s):  
Anass Misbah ◽  
Ahmed Ettalbi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Web Services ◽  
End Users ◽  
Human Being ◽  
Learning Models ◽  
Cloud Infrastructure ◽  
Machine Learning Models

<p class="0abstractCxSpFirst">Muti-view Web services have brought many advantages regarding the early abstraction of end users needs and constraints. Thus, security has been positively impacted by this paradigm, particularly, within Web services applications area, and then Multi-view Web services.</p><p class="0abstractCxSpMiddle">In our previous work, we introduce the concept of Multi-view Web services to Internet of Things architecture within a Cloud infrastructure by proposing a Proxy Security Layer which consists of Multi-view Web services allowing the identification and categorizing of all interacting IoT objects and applications so as to increase the level of security and improve the control of transactions.</p><p class="0abstractCxSpLast">Besides, Artificial Intelligence and especially Machine Learning are growing fast and are making it possible to simulate human being intelligence in many domains; consequently, it is more and more possible to process automatically a large amount of data in order to make decision, bring new insights or even detect new threats / opportunities that we were not able to detect before by simple human means.</p>In this work, we are bringing together the power of the Machine Learning models and The Multi-view Web services Proxy Security Layer so as to verify permanently the consistency of the access rules, detect the suspicious intrusions, update the policy and also optimize the Multi-view Web services for a better performance of the whole Internet of Things architecture.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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