Title: Exploring the Intersection of Generative AI and Cognitive Science: Insights and Implications
<br>Cover Date: 2024-01-01
<br>Cover Display Date: 2024
<br>DOI: 10.1109/ICNGN63705.2024.10871702
<br>Description: The rapid advancements in Generative Artificial Intelligence (AI) have revolutionized domains such as natural language processing, computer vision, and creative content generation. Simultaneously, Cognitive Science seeks to understand the mechanisms of human cognition, including memory, decision-making, and creativity. This paper explores the intersection of these fields, investigating how Generative AI models can simulate cognitive processes and how Cognitive Science insights can inform AI development. Methodologies include experiments with Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and GPT-3 to assess simulations of memory, creativity, and decision-making. Empirical findings demonstrate how VAEs enable memory reconstruction, GANs simulate decision-making processes, and Transformer-based models like GPT-3 exhibit creative capabilities. This study provides valuable insights into advancing AI research while deepening the theoretical understanding of human cognition.
<br>Citations: 0
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: A Comparative Study of Machine Learning Approaches for Predicting Close-Price Cryptocurrency
<br>Cover Date: 2022-01-01
<br>Cover Display Date: 2022
<br>DOI: 10.1109/ICTKE55848.2022.9983453
<br>Description: This study aimed to evaluate the effectiveness of several algorithms for predicting the close-price of various cryptocurrencies. Three algorithms employed in this comparative study were Support Vector Regression (SVR), Random Forest (RF), and Long Short-Term Memory (LSTM), while the three cryptocurrency datasets examined were Bitcoin, Ethereum, and Litecoin. Furthermore, in the stage of the data preparation, we compared two popular data normalization methods: min-max and z-score. After examining the close-price prediction results of each approach using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), it was revealed that the predictive model generated by the LSTM algorithm together with z-score normalization yielded the most effective results for each cryptocurrency dataset.
<br>Citations: 3
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Spatial and temporal reasoning in multimedia information retrieval and composition with XDD
<br>Cover Date: 2004-12-01
<br>Cover Display Date: 2004
<br>DOI: 10.1109/MMSE.2004.76
<br>Description: This research first proposes a general framework for retrieval and composition of spatial and temporal multimedia. It is based on an Equivalent Transformation theory and an XDD theory. It employs the MPEG-7 and SMIL standards which provide the tools to annotate the spatial and temporal relations inside multimedia and the tools to compose the multimedia, respectively. SMIL is selected to be one of the representation languages for the multimedia documents and presentations. Yet this has no enough efficiency to handle higher-level spatial and temporal relations, whereas the MPEG-7 provides tools to annotate those kinds of relations. These tools are used to decompose multimedia contents into segments and synchronize them together. Nevertheless, they should have more expressiveness power for retrieval and composition. As a result, the XDD theory is employed to enhance the language in terms of expressibility and computability. Moreover, by making use of XDD, constraints and axiom specifications are easily defined. This paper also defines a modeling language called SMILD that be able to represent spatial and temporal multimedia. © 2004 IEEE.
<br>Citations: 1
<br>Aggregation Type: Conference Proceeding
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