Rezaul Karim, Ph.D.'s repositories
Deep-Learning-for-Clustering-in-Bioinformatics
Deep Learning-based Clustering Approaches for Bioinformatics
DeepCOVIDExplainer
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images
DeepKneeOAExplainer_
Explainable Knee Osteoarthritis Diagnosis from Radiographs & MRIs
covid19-datasets
A list of high quality open datasets for COVID-19 data analysis
Genetic-CNN
CNN architecture exploration using Genetic Algorithm
ai-resources-ontology
AI4EU Ontology
bert-loves-chemistry
bert-loves-chemistry: a repository of HuggingFace models applied on chemical SMILES data for drug design, chemical modelling, etc.
CascadeTabNet
This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"
COVID19_BIBM_2020
BIBM_2020
Datasets-for-Hate-Speech-Detection
Datasets for Hate Speech Detection
Detection-of-Hate-Speech-in-Multimodal-Memes
Facebook Hatebook Memes Challenge
HateXplain
Can we use explanations to improve hate speech models? Paper accepted at AAAI 2021.
KG-Embedding-for-Fraud-Detection
This repository contains the code used in the experimental setup of the paper 'Inductive Graph Representation Learning for Fraud Detection
knowledge-graph-from-rdbms
A basic implementation of ontologies and knowledge graphs.
publications
My publications
Rice-crop-Insects-and-Weed-Detection-using-faster-R-CNN
As the increase in the world population the demand of the rice is also increases. In order to increase the growth of rice in the rice crop it is necessary to detect the weed and insects in the rice crop to minimize the growth of weed and insects so that the growth of the rice can be increased.Insect and Weed detection is the important factor to be analyzed. Unmanned Air Vehicle (UAV) is used for data acquisition of rice crop in different phases and states so that high quality of RGB images can be captured. In which we have taken 15 different types of rice crop insects species images and different phases of weed images to train the model. The proposed method facilitates the extraction of weed and insects into the rice crop field using deep learning concept faster region-based convolutional neural networks(Faster R-CNNs) it is implemented using Python3 with the help of Tensorflow API. The result shows that Faster R-CNN method is the state of arts method for detection and classification of weed and insects with good accuracy rate.
spark_project_template_generator
Used to generate Sample Spark Project Template
zindi_yield_prediction
A CNN LSTM based solution for yield prediction