Titlebook: Development and Analysis of Deep Learning Architectures; Witold Pedrycz,Shyi-Ming Chen Book 2020 Springer Nature Switzerland AG 2020 Compu

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Deep Learning for Soft Sensor Design, the process industry. Many Soft Sensors are designed by using data-driven approaches and exploiting historical databases. Machine learning is widely used for this aim. Here, the potentialities of deep learning in solving some challenges raising in industrial applications are introduced. More specif

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Case Study: Deep Convolutional Networks in Healthcare,complexity of the big data. Therefore, the methodologies utilized for data analysis have been evolved due to the increase in importance of extracting information from big data. Healthcare systems are important systems dealing with big data analysis. Deep learning is the most applied data analysis me

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Deep Domain Adaptation for Regression,ecent years. Domain adaptation or transfer learning algorithms alleviate this challenge by transferring relevant knowledge from a source domain to induce a model for a related target domain, where labeled data are scarce. Further, deep learning algorithms are instrumental in learning informative fea

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,Baby Cry Detection: Deep Learning and Classical Approaches, signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network (CNN) architectures for baby cry detection, and compare thei

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Securing Industrial Control Systems from False Data Injection Attacks with Convolutional Neural Netrms of attacks are constantly emerging to exploit vulnerabilities in system compromising the security parameters such as Confidentiality, Integrity and Availability (CIA). Injection attacks also termed as False Data Injection Attacks (FDIA) are the complex attacks on the ICS. FDIA affects the data i