… to detect various attacks based on deep learning model in ad-hoc networks. This plug and …
into deep learning detection model to detect. This paper we use DNN deep learning model to …

… simulated data using deep learning techniques to detect … Deep Learning in network security,
specifically through the use of a Deep Autoencoder approach to improve IDSs in Mobile Ad…

… Ad-hoc video search (AVS) is an important yet challenging … a fully deep learning method for
query representation learning. … With W2VV++, we establish a new baseline for ad-hoc video …

… Abstract—Vehicular Ad hoc Networks (VANETs) provide ef… trust model with respect to
Machine/Deep Learning (ML/DL) is … Neural Network that combines deep learning with probabilistic …

… Current predefined architectures for deep learning are computationally very heavy and use
… an ad hoc architecture for the classification of multispectral images using deep learning …

… Deep Learning is a branch of machine learning, this subfield is … In this section we will present
some of the deep learning … In this section, we compare machine learning and deep learning …

… We want to augment todays ADAS with deep learning systems that will learn the behavior of
drivers over time [13]. By adding the computer vision and AR features to the human machine …

… In this article, we invoke deep learning (DL) to assist routing … , deep neural networks (DNNs)
[19] are capable of learning a … a bespoke deep learning (DL) technique for learning routing …

… The method of the deep learning has severely helped in the … However, deep learning
revolutionized evaluation of network … intrusion detection system using the deep learning (DL) …

… Mobile ad hoc networks (MANETs) are infrastructure-less, … as flying ad-hoc networks (FANETs),
vehicular ad-hoc networks (… We use the approach of deep learning exactly deep neural …