Affective Neuroscience and Computing

Date

2010-2013

Author

Gökçay, Didem

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

96
views

0
downloads

This course aims to examine the current state and future prospects of affect in computing and cognition. The objective is to provide knowledge in several aspects of affect: sensation, representation, expression and then show applications of this knowledge in human-computer-interaction. By the end of this course, students should have learned how emotions/affect can be quantified through behavioral/physiology measures and what are the current state-of-art applications in this newly emerging field.

URI

https://ocw.metu.edu.tr/course/view.php?id=230
https://hdl.handle.net/11511/36949

Collections

Graduate School of Informatics, Course Material

Suggestions

OpenMETU
Core

Fostering Creative Cognition In Design Education: A Comparative Analysis Of Algorithmic And Heuristic Educational Methods In Basic Design Education Çiğdem, Zeynep; Ürey, Uysal (2021-6-8) This research aims to examine the impact of algorithmic and heuristic educational methods in basic design education in an attempt to see their effects on the development of the creative cognition of students. The impact of educational methods in the development of creativity and creative problem-solving ability in design education has been addressed by a limited number of studies. The impact of algorithmic and heuristic educational methods in basic design education, on the other hand, has not been studied a...
Geospatial object recognition using deep networks for satellite images Barut, Onur; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2018) Deep learning paradigm has been drawing significant interest during the last decade due to the recent developments in novel machine learning algorithms and improvements in computational hardware. Satellite image analysis is also an important scientific area with many objectives, such as disaster and crisis management, forest cover, road mapping, city planning, even military purposes. Spatial correlations of land cover or geospatial objects between different images lead to widely utilization of convolutional...
Geospatial Object Detection Using Deep Networks Barut, Onur; Alatan, Abdullah Aydın (2019-01-01) In the last decade, deep learning has been drawing a huge interest due to the developments in the computational hardware and novel machine learning techniques. This progress also significantly effects satellite image analysis for various objectives, such as disaster and crisis management, forest cover, road mapping, city planning and even military purposes. For all these applications, detection of geospatial objects has crucial importance and some recent object detection techniques are still unexplored to b...
English Language Preservice Teachers\u2019 Stages of Concern for Web 2.0 Technology Integration Kayaduman, Halil; Delialioğlu, Ömer (2021-06-01) This study explores the English Language preservice teachers (PST)’ stages of concern (SoC) for Web 2.0 technology integration in the Learning Technology by Design (LBD) based course. It aims to identify and better understand PSTs’ concerns. Understanding the concerns has advantages of describing and addressing the needs of PSTs prior to actual in-service experiences. SoC model, a component of the Concerns-Based Adoption Model, was used as the theoretical framework. An embedded mixed methods design was impl...
Neural network based online estimation of maneuvering steady states and control limits Gürsoy, Gönenç; Yavrucuk, İlkay; Department of Aerospace Engineering (2010) This thesis concerns the design and development of neural network based predictive algorithms to predict approaching aircraft limits. Therefore, approximate dynamics of flight envelope parameters such as angle of attack and load factor are constructed using neural network augmented dynamic models. Then, constructed models are used to predict steady state responses. By inverting the models and solving for critical controls at the known envelope limits, critical control inputs are calculated as well. The perf...

Citation Formats

D. Gökçay, “Affective Neuroscience and Computing,” 00, 2010, Accessed: 00, 2020. [Online]. Available: https://ocw.metu.edu.tr/course/view.php?id=230.