Recent advances on effective and efficient deep learning-based solutions

Abstract

This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions.

Gravitational Deep Convoluted Stacked Kernel Extreme Learning Based Classification for Face Recognition

“…Also, face recognition development stages and related algorithms were provided. Recent advancements in efficient and significant deep learning-based solutions for face recognition were presented in [10].…”

“…DM=|0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 | (9) Then, with the above diagonal matrix DM (9), the Deep Convoluted temporal relationship employing the Tikhonov Regularization function (i.e., pooling) between representations across different years is mathematically formulated as given below. (10) From the above equation ( 10), the result of the Deep Convoluted representations DC i j is used to reduce the false positive rate (FPR) whereas the second part of the equation is designed with the purpose of face recognition across adjacent years of age to become similar for a subject and vice versa. Finally, Max Pooling Aggregation (MPA) is employed to aggregate representations across different years or ages as given below.…”