Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor

<p>In the software version release management process, there is a need, on the part of human specialists, to classify the criticality of each software version However, the subjectivity of this classification may be present according to the experience acquired by specialists over the years. In order to reduce subjectivity in the process, an Artificial Intelligence technique called the Expert System (ES) can be applied to represent the knowledge of human specialists and use it in problem solving. <a>Thus, the aim of this paper was to reduce the subjectivity in the criticality classification of the software version with the support of the Expert System. </a>To this end, a questionnaire was developed with the objective of obtaining the criticality opinions classified as High, Medium and Low in each specialist's software version to assist in the preparation of the ES production rules. ES generated 17 production rules with a 100% confidence level applied to a production database. The results of the classification carried out by the ES corresponded to the classification carried out by the specialists in the production base, that is, the ES was able to represent their knowledge. Then, another questionnaire was applied to the specialists in order to verify the perception of satisfaction regarding the use of the ES with a result obtained of 4.8, considered satisfactory. It was concluded, then, that the ES supported the reduction of subjectivity in the classification of the criticality of software version.</p>

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Subjectivity reducing in software version criticality classification with the support of an expert system

Research Society and Development ◽

10.33448/rsd-v11i1.25132 ◽

2022 ◽

Vol 11 (1) ◽

pp. e37811125132

Author(s):

Dacyr Dante de Oliveira Gatto ◽

Renato José Sassi

Keyword(s):

Artificial Intelligence ◽

Expert System ◽

Problem Solving ◽

Confidence Level ◽

Management Process ◽

Production Rules ◽

Artificial Intelligence Technique ◽

Release Management ◽

Intelligence Technique

In the software version release management process, there is a need, on the part of human specialists, to classify the criticality of each software version. However, the subjectivity of this classification may be present according to the experience acquired by specialists over the years. To reduce subjectivity in the process, an Artificial Intelligence technique called Expert System (ES) can be applied to represent the knowledge of human specialists and use it in problem solving. Thus, the aim of this paper was to reduce the subjectivity in the criticality classification of the software version with the support of the Expert System. To this end, a questionnaire was developed with the objective of obtaining the criticality opinions classified as High, Medium and Low in each specialist's software version to assist in the preparation of the ES production rules. ES generated 17 production rules with a 100% confidence level applied to a production database. The results of the classification carried out by the ES corresponded to the classification carried out by the specialists in the production base, that is, the ES was able to represent their knowledge. Then, another questionnaire was applied to the specialists to verify the perception of satisfaction regarding the use of the ES with a result obtained of 4.8, considered satisfactory. It was concluded, then, that the ES supported the reduction of subjectivity in the classification of the criticality of software version.

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Subjectivity Reducing In Software Version Criticity Classification With The Support Of An Expert System

10.36227/techrxiv.13158095.v1 ◽

2020 ◽

Author(s):

Dacyr Gatto ◽

Renato José Sassi

Keyword(s):

Artificial Intelligence ◽

Expert System ◽

Problem Solving ◽

Confidence Level ◽

Management Process ◽

Production Rules ◽

Artificial Intelligence Technique ◽

Release Management ◽

Intelligence Technique

<p>In the software version release management process, there is a need, on the part of human specialists, to classify the criticality of each software version However, the subjectivity of this classification may be present according to the experience acquired by specialists over the years. In order to reduce subjectivity in the process, an Artificial Intelligence technique called the Expert System (ES) can be applied to represent the knowledge of human specialists and use it in problem solving. <a>Thus, the aim of this paper was to reduce the subjectivity in the criticality classification of the software version with the support of the Expert System. </a>To this end, a questionnaire was developed with the objective of obtaining the criticality opinions classified as High, Medium and Low in each specialist's software version to assist in the preparation of the ES production rules. ES generated 17 production rules with a 100% confidence level applied to a production database. The results of the classification carried out by the ES corresponded to the classification carried out by the specialists in the production base, that is, the ES was able to represent their knowledge. Then, another questionnaire was applied to the specialists in order to verify the perception of satisfaction regarding the use of the ES with a result obtained of 4.8, considered satisfactory. It was concluded, then, that the ES supported the reduction of subjectivity in the classification of the criticality of software version.</p>

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Artificial Intelligence Technique for Pilot Approach Decision Aid Logic (PADAL) System

10.21236/ada389405 ◽

2001 ◽

Cited By ~ 1

Author(s):

Robert Richards ◽

Jason Chrenka ◽

Marvin Thordsen

Keyword(s):

Artificial Intelligence ◽

Decision Aid ◽

Artificial Intelligence Technique ◽

Intelligence Technique

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Towards a Customized¬ Awareness Campaign to Educate the Public about COVID-19: Development and Validation of a Messaging Campaign to Promote Precautionary Behavior Using Geospatial Intelligence in Saudi Arabia (Preprint)

10.2196/preprints.26856 ◽

2020 ◽

Author(s):

Mayda Alrige ◽

Hind Bitar Bitar ◽

Maram Meccawi ◽

Balakrishnan Mullachery

Keyword(s):

Artificial Intelligence ◽

Saudi Arabia ◽

Content Validity ◽

Awareness Campaign ◽

Artificial Intelligence Technique ◽

The Public ◽

High Infection ◽

Local Map ◽

The City ◽

Intelligence Technique

BACKGROUND Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease, such as Covid-19. In Saudi Arabia, many attempts have been made toward raising the public awareness about Covid-19 infection-level and its precautionary health measures that have to be taken. Although this is useful, most of the health information delivered through the national dashboard and the awareness campaign are very generic and not necessarily make the impact we like to see on individuals’ behavior. OBJECTIVE The objective of this study is to build and validate a customized awareness campaign to promote precautionary health behavior during the COVID-19 pandemic. The customization is realized by utilizing a geospatial artificial intelligence technique called Space-Time Cube (STC) technique. METHODS This research has been conducted in two sequential phases. In the first phase, an initial library of thirty-two messages was developed and validated to promote precautionary messages during the COVID-19 pandemic. This phase was guided by the Fogg Behavior Model (FBM) for behavior change. In phase 2, we applied STC as a Geospatial Artificial Intelligence technique to create a local map for one city representing three different profiles for the city districts. The model was built using COVID-19 clinical data. RESULTS Thirty-two messages were developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The enumerated content validity of the messages was established through the utilization of Content Validity Index (CVI). Thirty-two messages were found to have acceptable content validity (I-CVI=.87). The geospatial intelligence technique that we used showed three profiles for the districts of Jeddah city: one for high infection, another for moderate infection, and the third for low infection. Combining the results from the first and second phases, a customized awareness campaign was created. This awareness campaign would be used to educate the public regarding the precautionary health behaviors that should be taken, and hence help in reducing the number of positive cases in the city of Jeddah. CONCLUSIONS This research delineates the two main phases to developing a health awareness messaging campaign. The messaging campaign, grounded in FBM, was customized by utilizing Geospatial Artificial Intelligence to create a local map with three district profiles: high-infection, moderate-infection, and low-infection. Locals of each district will be targeted by the campaign based on the level of infection in their district as well as other shared characteristics. Customizing health messages is very prominent in health communication research. This research provides a legitimate approach to customize health messages during the pandemic of COVID-19.

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