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Keyword: Evaluation Metrics
Development of Evaluation Metrics for Learners in Unplugged Activity
As well as physical computing, unplugged activity is important in ICT (Information and Communication Technology) education, that uses information and communications technology to support, enhance, and optimize the delivery of information, and software education. In order to achieve educational objectives in unplugged activity, evaluation metrics of learners are necessary. However, there is little work on…
Read More A Study on Development of Evaluation Metrics for Learners in Physical Computing
Physical computing is important for ICT (information and communication technology) Education and other informatics education such as software education since physical computing can provide learning-by-doing education for students. It is also a strong tool to increase students’ programming ability using various type of physical computing tools like a robot. In physical computing, it is necessary…
Read More Extending the Classifier Algorithms in Machine Learning to Improve the Performance in Spoken Language Understanding Systems Under Deficient Training Data
One of the open domain challenges for Spoken Dialogue System (SDS) is to maintain a natural conversation for rarely visited domain i.e. domain with fewer data. Spoken Language Understanding (SLU) is a component of SDS that converts user utterance into a semantic form that a computer can understand. If we scale SDS open domain challenge…
Read More Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review
In more recent times, researchers have turned their attention to link prediction and the role link inference can play in better understanding the evolutionary nature of social networking sites. The objective of this paper is to present an in-depth review, analysis, and discussion of the cutting-edge link prediction methods that can be applied to better…
Read More Solar Photovoltaic Power Output Forecasting using Deep Learning Models: A Case Study of Zagtouli PV Power Plant
Forecasting solar PV power output holds significant importance in the realm of energy management, particularly due to the intermittent nature of solar irradiation. Currently, most forecasting studies employ statistical methods. However, deep learning models have the potential for better forecasting. This study utilises Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU) and hybrid LSTM-GRU deep…
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