scholarly journals Intelligent Voice Instructor-assistant System for Collaborative and Interactive Classes

2021 ◽  
Author(s):  
Matthew Baker ◽  
Xiaohui Hu ◽  
Gennaro De Luca ◽  
Yinong Chen
Keyword(s):  
Short Term Memory ◽  
Memory Model ◽  
Discussion Boards ◽  
Short Term ◽  
The Past ◽  
Questions And Answers ◽  
Small Set ◽  
Long Short Term Memory ◽  
College Classes ◽  
The Voice

College classes are becoming increasingly large. A critical component in scaling class size is the collaboration and interactions among instructors, teaching assistants, and students. We develop a prototype of anIntelligent Voice Instructor-AssistantSystem (IVIAS)for supportinglargeClasses,in whichAmazon Web Services, Alexa Voice Services and self-developed services are used. It uses ascraping service for reading the questions and answers from the past and current course discussion boards, organizes the questions in JSON format and stored them in thedatabase, which can be accessed by AWS Alexa skills. When a voice question from a student comes, Alexais used fortranslating the voice sentence into texts. Then Siamesedeep LSTM (Long Short-Term Memory)model is introducedto calculate the similarity between the question asked and the questions in the database to find the best-matched answer. Questions with no match will be sent to the instructor, and instructor’s answer will be added into the database. Experiments show that the implemented model achieve promising results that can lead to a practical system. IVIAS starts with a small set of questions. It can grow through learning and improving when more and more questions are asked and answered.

scholarly journals Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different Variables

2021 ◽  
Vol 35 (4) ◽  
pp. 1167-1181
Author(s):  
Yun Bai ◽  
Nejc Bezak ◽  
Bo Zeng ◽  
Chuan Li ◽  
Klaudija Sapač ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Runoff Forecasting ◽  
Long Short Term Memory

Sequence Model based Cloudburst Prediction for the Indian State of Uttarakhand

2021 ◽  
Vol 14 (7) ◽  
pp. 1-9
Author(s):  
M. Sivagami ◽  
P. Radha ◽  
A. Balasundaram
Keyword(s):  
Predictive Power ◽  
Short Term Memory ◽  
Geographic Location ◽  
Short Term ◽  
The Past ◽  
Learning Techniques ◽  
Indian State ◽  
Long Short Term Memory ◽  
Life Challenge ◽  
Gated Recurrent Unit

Predicting the phenomenon of cloudburst has been a larger than life challenge to many weather and rain scientists. The very nature of cloudburst occurrence itself complicates the prediction of cloudburst. Since, cloudburst downpour occurs over a short span of time and is confined to very narrow geographic location, it is highly difficult for weather scientists to make any cloudburst predictions. In this work, the authors propose a cloudburst prediction model that leverages deep learning techniques to predict the occurrence of cloudburst in a location. The authors have collected the data pertaining to the cloudburst events that have occurred in the Indian State of Uttarakhand over the past decade and developed the model. Experiments were conducted using time series sequence models namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Predictive Power Score (PPS) has been used to extract the essential features that are fed as input to these sequence models. The performance of sequence models has been discussed in terms of loss function and accuracy and the results are promising for GRU based model in comparison with other sequence models.

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