Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …

The availability of abundant labeled data in recent years led the researchers to introduce a
methodology called transfer learning, which utilizes existing data in situations where there …

The real estate market creates one of the significant business domains for investors, but a
wise investment in real estate is more important for low-income people who have just one …

In recent years, smart healthcare IoT devices have become ubiquitous, but they work in
isolated networks due to their policy. Having these devices connected in a network enables …

This paper proposes a model to predict the levels (eg, Bachelor, Master, etc.) of
postsecondary degree awards that have been ambiguously expressed in the student …

Medical Imaging is one of the growing fields in the world of computer vision. In this study, we
aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges in …

Making decisions in smart cities is challenging due to the high direct/indirect dimensional
factors and parameters. This chapter focuses on one of the smart cities' important branches …

An undeniable part of a smart city is its use of smart agents. These agents can vary a lot in
sizes, shapes, and functionalities. Embodied artificial intelligence is the field of study that …

DRDr II is a hybrid of machine learning and deep learning worlds. It builds on the successes
of its antecedent, namely, DRDr, that was trained to detect, locate, and create segmentation …

In Speaker, identifying or recognizing human voices is a challenging task. Recently, the
automatic speaker recognition technique has been developed by using deep learning …