scholarly journals Integration of Smart Class Control System Using Amazon Echo Dot with Artificial Neural Networks

2019 ◽  
Vol 5 ◽  
Author(s):  
Teddy Januar ◽  
Abd. Rabi ◽  
Dwi Arman Prasetya
Keyword(s):  
Neural Network ◽  
Electronic Devices ◽  
Raspberry Pi ◽  
Network Technology ◽  
Sensors And Actuators ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Neural Network Technology ◽  
Resource System ◽  
Number Of Cells

Development of a class resource system that is integrated with the system that is the application-based system. One system that can be used is the Smart Class. Smart Class is a system that offers control of electronic equipment in the classroom using voice command control with a device that is Smart Speaker called Amazon echo dot which is used to facilitate the use of electronic devices in classrooms using Raspberry Pi Microcontroller technology by embedding smart class artificial neural network technology. With maximum performance at 1500ms to 2000ms on all conditions both sensors and actuators by iterating simultaneously 500 times with two hidden layers and the number of cells of each hidden layer is 9 and 5.

scholarly journals Problem-Solving Skills Among Precollege Students in Clinical Immunology and Microbiology: Classifying Strategies with a Rubric and Artificial Neural Network Technology

2001 ◽  
Vol 2 (1) ◽  
pp. 25-33
Author(s):  
SUSAN KANOWITH-KLEIN ◽  
MEL STAVE ◽  
RON STEVENS ◽  
ADRIAN M. CASILLAS
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High School Students ◽  
Problem Solving ◽  
Network Technology ◽  
Problem Solving Skills ◽  
Problem Solving Strategies ◽  
Artificial Neural ◽  
Neural Network Technology

Educators emphasize the importance of problem solving that enables students to apply current knowledge and understanding in new ways to previously unencountered situations. Yet few methods are available to visualize and then assess such skills in a rapid and efficient way. Using a software system that can generate a picture (i.e., map) of students’ strategies in solving problems, we investigated methods to classify problem-solving strategies of high school students who were studying infectious and noninfectious diseases. Using maps that indicated items students accessed to solve a software simulation as well as the sequence in which items were accessed, we developed a rubric to score the quality of the student performances and also applied artificial neural network technology to cluster student performances into groups of related strategies. Furthermore, we established that a relationship existed between the rubric and neural network results, suggesting that the quality of a problem-solving strategy could be predicted from the cluster of performances in which it was assigned by the network. Using artificial neural networks to assess students’ problem-solving strategies has the potential to permit the investigation of the problem-solving performances of hundreds of students at a time and provide teachers with a valuable intervention tool capable of identifying content areas in which students have specific misunderstandings, gaps in learning, or misconceptions.

scholarly journals Prediction of dust storms in construction projects using intelligent artificial neural network technology

2019 ◽  
Vol 7 (4) ◽  
pp. 1659
Author(s):  
Salah Kh. Zamim ◽  
Noora Saad Faraj ◽  
Ibrahim A. Aidan ◽  
Faiq M. S. Al-Zwainy ◽  
Mohammed A. AbdulQader ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Construction Projects ◽  
Dust Storms ◽  
Network Technology ◽  
Artificial Neural ◽  
Neural Network Technology
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