5 results
Search Results
Now showing 1 - 5 of 5
Article Citation - WoS: 3Citation - Scopus: 4Attention Mediates the Effect of Emotional Arousal on Learning Outcomes in Multimedia Learning: an Eye-Tracking Study(Routledge Journals, Taylor & Francis Ltd, 2023) Aksaray, Sevgi Genc; Ozcelik, ErolRecent findings from psychological studies have shown that emotional arousal improves human memory. However, more evidence is necessary if these results are generalisable to multimedia learning environments. Considering these needs, the study has the goal to examine the effect of emotional arousal on multimedia learning. Fifty-seven participants were presented with instructional materials with either high- or low-arousing words and pictures in an experimental study. The eye movements of participants were recorded while they studied the instructional materials to examine the online processes during learning. The results suggest that emotional arousal enhanced recall and transfer scores. The eye-tracking results demonstrate that emotional arousal attracted attention. The results of the mediation analysis suggest that fixation time on emotional pictures as an indicator of attention mediated the relationship between emotional arousal and learning outcomes. The findings show the importance of the guidance of attention by emotional multimedia elements for learning.Article Citation - WoS: 11Citation - Scopus: 20Reinforcement Learning Using Fully Connected, Attention, and Transformer Models in Knapsack Problem Solving(Wiley, 2022) Yildiz, Beytullah; Yıldız, Beytullah; Yıldız, BeytullahKnapsack is a combinatorial optimization problem that involves a variety of resource allocation challenges. It is defined as non-deterministic polynomial time (NP) hard and has a wide range of applications. Knapsack problem (KP) has been studied in applied mathematics and computer science for decades. Many algorithms that can be classified as exact or approximate solutions have been proposed. Under the category of exact solutions, algorithms such as branch-and-bound and dynamic programming and the approaches obtained by combining these algorithms can be classified. Due to the fact that exact solutions require a long processing time, many approximate methods have been introduced for knapsack solution. In this research, deep Q-learning using models containing fully connected layers, attention, and transformer as function estimators were used to provide the solution for KP. We observed that deep Q-networks, which continued their training by observing the reward signals provided by the knapsack environment we developed, optimized the total reward gained over time. The results showed that our approaches give near-optimum solutions and work about 40 times faster than an exact algorithm using dynamic programming.Article Critical Analysis and Alternative Explanations for Effects of Apnea on the Timing of Motor Representations(Brill Academic Publishers, 2015) Alkan,N.This commentary is designed to provide an analysis of issues pertinent to the investigation of the effects of the temporary cessation of breathing (apnea), particularly during water immersion or diving, and its effects on time estimation in general and the timing of motor representation in particular. In addition, this analysis provides alternative explanations of certain unexpected findings reported by Di Rienzo et al. (2014) pertaining to apnea and interval timing. The perspective and guidance that this commentary provides on the relationship between apnea and time estimation is especially relevant considering the scarcity of experimental and clinical studies examining these variables. © 2015 by Koninklijke Brill NV, Leiden, The Netherlands.Article Citation - WoS: 29Citation - Scopus: 43Text Classification Using Improved Bidirectional Transformer(Wiley, 2022) Tezgider, Murat; Yıldız, Beytullah; Yildiz, Beytullah; Aydin, Galip; Yıldız, BeytullahText data have an important place in our daily life. A huge amount of text data is generated everyday. As a result, automation becomes necessary to handle these large text data. Recently, we are witnessing important developments with the adaptation of new approaches in text processing. Attention mechanisms and transformers are emerging as methods with significant potential for text processing. In this study, we introduced a bidirectional transformer (BiTransformer) constructed using two transformer encoder blocks that utilize bidirectional position encoding to take into account the forward and backward position information of text data. We also created models to evaluate the contribution of attention mechanisms to the classification process. Four models, including long short term memory, attention, transformer, and BiTransformer, were used to conduct experiments on a large Turkish text dataset consisting of 30 categories. The effect of using pretrained embedding on models was also investigated. Experimental results show that the classification models using transformer and attention give promising results compared with classical deep learning models. We observed that the BiTransformer we proposed showed superior performance in text classification.Article Citation - WoS: 9Citation - Scopus: 9A Hybrid Deep Learning Methodology for Wind Power Forecasting Based on Attention(Taylor & Francis inc, 2024) Akbal, Yildirim; Unlu, Kamil DemirberkWind energy, as a sustainable energy source, poses challenges in terms of storage. Therefore, careful planning is crucial to utilize it efficiently. Deep learning algorithms are gaining popularity for analyzing complex time series data. However, as the "no free lunch" theorem suggests, the trade-off is: they need a lot of data to achieve the benefits. This even brings up a severe challenge for time series analysis, as the availability of historical data is often limited. This study aims to address this issue by proposing a novel shallow deep learning approach for wind power forecasting. The proposed model utilizes a fusion of transformers, convolutional and recurrent neural networks to efficiently handle several time series simultaneously. The empirical evidence demonstrates that the suggested innovative method exhibits exceptional forecasting performance, as indicated by a coefficient of determination (R2) of 0.99. When the forecasting horizon reaches 48, the model's performance declines significantly. However, when dealing with long ranges, utilizing the mean as a metric rather than individual point estimates would yield superior results. Even when forecasting up to 96 hrs in advance, obtaining an R2 value of 0.50 is considered a noteworthy accomplishment in the context of average forecasting.
