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2022 ◽  
Vol 11 (1) ◽  
pp. e37811125132
Author(s):  
Dacyr Dante de Oliveira Gatto ◽  
Renato José Sassi

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.


2022 ◽  
pp. 340-353
Author(s):  
K. Bhargavi

Diabetes is one of the chronic diseases which keep increasing at an alarming rate, and the patients need to visit the clinic to routinely check their sugar levels and adjust their treatment plans. Artificial intelligence-enabled telemedicine is found to be a promising approach to monitor the health status of diabetic patients. Some of the promising artificial intelligence technologies for treating diabetic patients are a reactive machine, limited memory, theory of minds, and self-awareness. Each of these techniques is discussed with architecture, characteristics, algorithms, advantages, and applications. Performance analysis is carried out towards the performance metrics like accuracy, medical error rate, speed, and learning rate, and the performance achieved by self-awareness artificial intelligence technique is found to be better in delivering telemedicine-based care for diabetic patients with a very high level of precision and speed of operation.


2021 ◽  
Vol 40 (4) ◽  
pp. 728-731
Author(s):  
Z.O. Jagun ◽  
M.B. Olajide ◽  
B.A. Wokoma ◽  
E.N. Osegi

This paper presents the capability of an emerging swarm intelligence technique for power loss minimization known as the Artificial Bee Colony (ABC) used in the context of an Alternative Load Flow Analysis (LFA) technique (ABC-LFA) for the solution of a power systems network. Studies are performed considering the effect of an important parameter of the ABC, the “maxcycle” on the LFA process; experiments are conducted by applying the ABC-LFA to the Western System Coordinated Council (WSCC) 3-machine 9- bus power system and a section of the Nigerian 132-kV power transmission network Port-Harcourt Region (NPHC-132), and the results reported. The results indicate that increasing the value of the ABC “maxcycle” parameter has a pronounced effect on the results obtained by the ABC-LFA. The results also indicate the sensitivity of the ABC to low values of maxcycle parameter.


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