Titlebook: Shallow Learning vs. Deep Learning; A Practical Guide fo Ömer Faruk Ertuğrul,Josep M Guerrero,Musa Yilmaz Book 2024 The Editor(s) (if appli

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Shallow Learning vs. Deep Learning in Social Applications,ment analysis, opinion mining, and social network analysis. The effectiveness of different methods will be contrasted, and the chapter will end with some observations, suggested unresolved open problems, and possible future research directions. This completes the whole storytelling on shallow and de

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Shallow Learning vs. Deep Learning in Image Processing,eatures that are defined from the input data for the model and have one or two layered models. Deep learning (DL) eliminates some of the data pre-processing that is typically involved with shallow learning. These algorithms can ingest and process unstructured data, like text and images, and it autom

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FECT

Shallow Learning vs. Deep Learning in Anomaly Detection Applications,s. Anomalies, deviations from normal patterns in data, pose significant challenges across various domains, necessitating effective detection mechanisms. Shallow learning methods, characterized by their simplicity and interpretability, have historically been employed for anomaly detection. However, r

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Advanced Techniques and Application Areas in Remote Sensing Images: Integration of Deep Learning ans study aims to examine various advanced techniques and various application areas of these techniques within the framework of research focusing on remote sensing images. Advances in image analysis and processing techniques stand out as important issues that allow remote sensing images to be used mor

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Shallow Learning vs Deep Learning in Smart Grid Applications, systems. Here, SGs that depend on SL with structured data, on the one hand, and DL methods for managing unstructured datasets and complex data representations, on the other hand, are examined by comparing their applications in the literature. In practice, SL and DL applications in key SG domains, s

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