Search Results for author: Javad Hassannataj Joloudari

Found 13 papers, 0 papers with code

Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction

no code implementations27 Jan 2023 Soroush Hashemifar, Abdolreza Marefat, Javad Hassannataj Joloudari, Hamid Hassanpour

To the best of our knowledge, FRA is the first method that shifts its focus towards manipulating the face embeddings generated by any face representation learning algorithm to create new embeddings representing the same identity and facial emotion but with an altered posture.

Data Augmentation Face Recognition +1

BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets

no code implementations4 Nov 2022 Javad Hassannataj Joloudari, Sadiq Hussain, Mohammad Ali Nematollahi, Rouhollah Bagheri, Fatemeh Fazl, Roohallah Alizadehsani, Reza Lashgari, Ashis Talukder

The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.

Sentiment Analysis

The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms

no code implementations23 Mar 2022 Javad Hassannataj Joloudari, Sanaz Mojrian, Hamid Saadatfar, Issa Nodehi, Fatemeh Fazl, Sahar Khanjani Shirkharkolaie, Roohallah Alizadehsani, H M Dipu Kabir, Ru-San Tan, U Rajendra Acharya

In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication(M2M), Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network.

Cloud Computing Q-Learning +2

Cannot find the paper you are looking for? You can Submit a new open access paper.