Recent years have seen the development of phishing detection and identification
approaches to defend against phishing attacks. Phishing detection solutions often report …

Objective: Scarcity of good quality electroencephalography (EEG) data is one of the
roadblocks for accurate seizure prediction. This work proposes a deep convolutional …

Inductive transfer or transfer learning refers to the problem of retaining and applying the
knowledge learned in one or more tasks to develop efficiently an effective hypothesis for a …

[41] Google, Inc.,“Google Ngram Viewer for Unintended Consequences,”[Online]. Available:
https://books. google. com/ngrams/graph? content=% 22unintended+ consequences …

We organized a data mining challenge in “unsupervised and transfer learning”(the UTL
challenge) followed by a workshop of the same name at the ICML 2011 conference in …

This article is about transfer learning as a new field in machine learning that is method of job
for model developed and is reused as the binger point for model on a second job. But …

This paper will look into the application of transfer learning in computer vision, where once
the training is completed or learned, we can use that learning to be applied for other new …

Abstract Detecting Social Desirable Responding (SDR) is important to ensure valid
personnel selection for any organization, yet current instruments for assessing SDR are …

One of the great challenges in modern biology is understanding the genome and its
translation into biological structures and function. In this context, the aim of this dissertation …

In this paper, it's looked upon how the two data set one of which is base data and the other is
target data and by varying the sizes of these two data sets and using the transfer learning in …