• Journal of Information Science Theory and Practice
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• v.1 no.3
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• pp.12-32
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• 2013

The paper introduces the research area of interactive information retrieval (IIR) from a historical point of view. Further, the focus here is on evaluation, because much research in IR deals with IR evaluation methodology due to the core research interest in IR performance, system interaction and satisfaction with retrieved information. In order to position IIR evaluation, the Cranfield model and the series of tests that led to the Cranfield model are outlined. Three iconic user-oriented studies and projects that all have contributed to how IIR is perceived and understood today are presented: The MEDLARS test, the Book House fiction retrieval system, and the OKAPI project. On this basis the call for alternative IIR evaluation approaches motivated by the three revolutions (the cognitive, the relevance, and the interactive revolutions) put forward by Robertson & Hancock-Beaulieu (1992) is presented. As a response to this call the 'IIR evaluation model' by Borlund (e.g., 2003a) is introduced. The objective of the IIR evaluation model is to facilitate IIR evaluation as close as possible to actual information searching and IR processes, though still in a relatively controlled evaluation environment, in which the test instrument of a simulated work task situation plays a central part.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.611-616
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• 2023

ECG(electrocardiogram) is a test used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the cause of all heart diseases can be found. Because the ECG signal obtained using the ECG-KIT includes noise in the ECG signal, noise must be removed from the ECG signal to apply to the deep learning. In this paper, the noise of the ECG signal was removed using the digital IIR Butterworth low-pass filter. When the performance evaluation of the three activation functions, sigmoid(), ReLU(), and tanh() functions, was compared using the deep learning model of LSTM, it was confirmed that the activation function with the smallest error was the tanh() function. Also, When the performance evaluation and elapsed time were compared for LSTM and GRU models, it was confirmed that the GRU model was superior to the LSTM model.