Cognitive Deep Learning

Chowdhary, Chiranji Lal; Darwish, Ashraf; Hassanien, Aboul Ella

doi:10.4018/978-1-7998-7705-9.ch094

Research Anthology on Artificial Intelligence Applications in Security 2020

DOI: 10.4018/978-1-7998-7705-9.ch094

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Cognitive Deep Learning

Chiranji Lal Chowdhary

Ashraf Darwish

Aboul Ella Hassanien

Abstract: Deep learning states the scientific algorithms that are accustomed to come through a particular assignment. Such tidy issues could also be meteorology or brain diagnosing wherever records are obtainable as text. In such a state of affairs, cognitive computing can help medical practitioners to diagnose patterns that they might not observe, and they will extend the flexibility to diagnose the brain with efficiency. Deep learning is additionally able to introduce new APIs.

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Cited by 2 publications

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To What Extent Can Multidisciplinary Artificial Intelligence Applications Enhance Higher Education?

Ndhlovu

Goosen

2022

Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence

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In order to provide readers with an overview of, and summarize, the content of this chapter, the purpose is stated as answering the primary research question: To what extent can multidisciplinary artificial intelligence in education (AIED) applications enhance higher education teaching and learning at an open and distance e-learning (ODeL) institution in South Africa? It is important to note that this is done against the background of multidisciplinary applications of deep learning-based artificial emotional intelligence.

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To What Extent Can Multidisciplinary Artificial Intelligence Applications Enhance Higher Education?

Ndhlovu

Goosen

2022

Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence

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Simple Linear Iterative Clustering (SLIC) and Graph Theory-Based Image Segmentation

Chowdhary

2021

Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security

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With the extensive application of deep acquisition devices, it has become more feasible to access deep data. The accuracy of image segmentation can be improved by depth data as an additional feature. The current research interests in simple linear iterative clustering (SLIC) are because it is a simple and efficient superpixel segmentation method, and it is initially applied for optical images. This mainly comprises three operation steps (i.e., initialization, local k-means clustering, and postprocessing). A scheme to develop the image over-segmentation task is introduced in this chapter. It considers the pixels of an image with simple linear iterative clustering and graph theory-based algorithm. In this regard, the main contribution is to provide a method for extracting superpixels with greater adherence to the edges of the regions. The experimental tests will consider biomedical grayscales. The robustness and effectiveness will be verified by quantitative and qualitative results.

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Cognitive Deep Learning

Cited by 2 publications

References 33 publications

To What Extent Can Multidisciplinary Artificial Intelligence Applications Enhance Higher Education?

To What Extent Can Multidisciplinary Artificial Intelligence Applications Enhance Higher Education?

Simple Linear Iterative Clustering (SLIC) and Graph Theory-Based Image Segmentation

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