Research Article
Intelligent Learning System Using Interactive Dialogflow and Webhooks
Chidi Ukamaka Betrand*,
Chinazo Juliet Onyema,
Mercy Eberechi Benson-Emenike,
Chinwe Gilean Onukwugha,
Uchenna Chinyere Onyemauche,
Douglas Allswell Kelechi
Issue:
Volume 12, Issue 4, August 2023
Pages:
54-62
Received:
21 November 2023
Accepted:
9 December 2023
Published:
26 December 2023
DOI:
10.11648/j.ijiis.20231204.11
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Abstract: Increase in the workload and number of information needed with additional study programs to be carried out can be too burdensome on the academic staff, Computer-based systems known as Intelligent Tutoring Systems (ITSs) give students individualized education and feedback. It has been demonstrated that Intelligent Tutoring Systems are beneficial in raising student performance generally. This system made an accurate assessment of the learner's knowledge and offer pertinent feedback using artificial intelligence techniques, machine learning and natural language processing. The system was able to adjust to the learner's preferred method of learning. Rapid Application Development (RAD) was used to create the system, allowing for modifications to be made to the system as it is being developed. JavaScript was the programming language utilized for the system. A user study with a group of math learners was to assess the system. The system's success in enhancing students' knowledge and abilities, as well as their pleasure with it, was evaluated in the research. The outcomes of this study aided in the creation of ITSs for math learning that are more efficient. The system created for this research will be a useful resource for students and has the potential to raise the standard of mathematics education.
Abstract: Increase in the workload and number of information needed with additional study programs to be carried out can be too burdensome on the academic staff, Computer-based systems known as Intelligent Tutoring Systems (ITSs) give students individualized education and feedback. It has been demonstrated that Intelligent Tutoring Systems are beneficial in ...
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Research Article
Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia
Hussen Ali Hassen*,
Yonatan Tibebu,
Dagemawi Negashe,
Mehret Ayana,
Fikru Fentaw Abera
Issue:
Volume 12, Issue 4, August 2023
Pages:
63-75
Received:
27 November 2023
Accepted:
19 December 2023
Published:
28 December 2023
DOI:
10.11648/j.ijiis.20231204.12
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Views:
Abstract: Runoff and sediment are important parameters to be understood and predict for managing land and water resource. So, understanding the dynamic process and prediction of the existing process by selecting suitable hydrological model is very essential. This study aims to test and evaluate the application of an artificial neural network (ANN) model for modeling runoff and sediment yield of Maybar watershed, Awash River basin. The ANN model was trained and cross validated using MATLAB, supported by the NN toolbox package. The main input for the ANN model was selected using correlation results from Statistical Packages for Social Science (SPSS). Present rainfall and previous one-day runoff up to four days of runoff were selected as inputs for runoff modeling, and present rainfall, present runoff, and previous one-day runoff were selected as inputs for sediment yield modeling. The proposed model was developed, trained, and cross validated by considering 7 years of data (2010–2016) for model training and 2 years of data for model testing (cross-validation), and their performance was evaluated using performance indicators (R2, RMSE, and NSE). Adding lag of runoff as input results increase the model efficiency during training. Of the five proposed ANN runoff models, model B (2 inputs, 3 hidden neurons, 1 output) performed better than the other proposed runoff models. Similarly, of the three proposed ANN sediment models, model III (3 inputs, 6 hidden neurons, 1 output) performed better than the other proposed sediment models. In general, the ANN model was applicable for predicting runoff and sediment in the Maybar watershed in daily time steps.
Abstract: Runoff and sediment are important parameters to be understood and predict for managing land and water resource. So, understanding the dynamic process and prediction of the existing process by selecting suitable hydrological model is very essential. This study aims to test and evaluate the application of an artificial neural network (ANN) model for ...
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