Search Results for author: H M Dipu Kabir

Found 7 papers, 5 papers with code

Reduction of Class Activation Uncertainty with Background Information

2 code implementations5 May 2023 H M Dipu Kabir

We also investigate class activation mappings (CAMs) of the trained model and observed the tendency towards looking at a bigger picture in a few class classification problems with the proposed model training methodology.

Fine-Grained Image Classification Satellite Image Classification

CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis

1 code implementation20 Sep 2022 Sadia Khanam, Mohammad Reza Chalak Qazani, Subrota Kumar Mondal, H M Dipu Kabir, Abadhan S. Sabyasachi, Houshyar Asadi, Keshav Kumar, Farzin Tabarsinezhad, Shady Mohamed, Abbas Khorsavi, Saeid Nahavandi

In this research, the PyTorch pre-trained models (VGG19\_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers.

COVID-19 Diagnosis Image Classification +1

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

Optimal Uncertainty-guided Neural Network Training

no code implementations30 Dec 2019 H M Dipu Kabir, Abbas Khosravi, Abdollah Kavousi-Fard, Saeid Nahavandi, Dipti Srinivasan

Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain.

Prediction Intervals Uncertainty Quantification

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