Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1941

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Browsing Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu by Publication Category "Kitap Bölümü - Uluslararası"

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    Foundations of Neuroscience-Based Learning
    (Springer International Publishing, 2022) Dorantes-Gonzalez, Dante Jorge
    Traditional learning and teaching approaches such as problem-based or project-based learning, among others, do not explicitly consider emotional-enhanced learning, which is a well-known driver of engagement leading to long-term memory retention. On the other hand, existing brain-based learning methods do not provide structured and scientifically-based strategies for the formation of the learner’s emotional experience and engagement. The Neuroscience-based Learning (NBL) technique is a novel neuroeducational approach that explains and applies the implicit neurophysiological mechanisms underlying vivid and highly-arousal emotional experiences leading to long-term memory retention. The NBL is devised from a cybernetics and system approach perspective. It starts from the basis of the neurophysiological learning scheme, describing the relationships among the environment and the learner’s internal mental processes ranging from perceptions, comparison with previous experiences and memories, immediate sensations, reactions, emotions, desires, intentions, higher-order cognitive functions, and controlled actions to the environment. The scheme relates memory systems, non-associative and associative learning mechanisms, implicit and explicit learning subsystems, signaling chemicals, and their neural subsystems, as well as identifying the amygdala as a key sensor triggering and modulating implicit learning. The NBL method exposes the triggers for vivid and highly arousal emotional learning: novelty, unpredictability, sense of low control, threat to the ego, avoidance (aversion-mediated learning), and reward (reward-based learning) and devises the principles of NBL toward more didactic applications. The foundations for implementing NBL in education and recommendations for learning during the online and pandemic situations were proposed. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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    Language Modeling for Turkish Text and Speech Processing
    (Springer, 2018) Arısoy, Ebru; Saraçlar, Murat
    This chapter presents an overview of language modeling followed by a discussion of the challenges in Turkish language modeling. Sub-lexical units are commonly used to reduce the high out-of-vocabulary (OOV) rates of morphologically rich languages. These units are either obtained by morphological analysis or by unsupervised statistical techniques. For Turkish, the morphological analysis yields word segmentations both at the lexical and surface forms which can be used as sub-lexical language modeling units. Discriminative language models, which outperform generative models for various tasks, allow for easy integration of morphological and syntactic features into language modeling. The chapter provides a review of both generative and discriminative approaches for Turkish language modeling.
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    Turkish Speech Recognition
    (2018) Arısoy, Ebru; Saraçlar, Murat
    Automatic speech recognition (ASR) is one of the most important applications of speech and language processing, as it forms the bridge between spoken and written language processing. This chapter presents an overview of the foundations of ASR, followed by a summary of Turkish language resources for ASR and a review of various Turkish ASR systems. Language resources include acoustic and text corpora as well as linguistic tools such as morphological parsers, morphological disambiguators, and dependency parsers, discussed in more detail in other chapters. Turkish ASR systems vary in the type and amount of data used for building the models. The focus of most of the research for Turkish ASR is the language modeling component covered in Chap. 4.