rezacsedu

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

AAAI-21-SDU-shared-task-1-AI

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"

CoAID

COVID19_BIBM_2020

BIBM_2020

Datasets-for-Hate-Speech-Detection

Datasets for Hate Speech Detection

DDI_BLKG

Drug-Drug Interaction Prediction on a Biomedical Literature Knowledge Graph

DeepHateLingo

Detection-of-Hate-Speech-in-Multimodal-Memes

Facebook Hatebook Memes Challenge

grobid

A machine learning software for extracting information from scholarly documents

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.

koan

A word2vec negative sampling implementation with correct CBOW update.

labml

🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

ML-API

Guide on creating an API for serving your ML model

PhD_thesis_v2

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.

san

Attention-based feature ranking for propositional data.

shapicant

Feature selection package based on SHAP and target permutation, for pandas and Spark

snomed_kge

spark_project_template_generator

Used to generate Sample Spark Project Template

zindi_yield_prediction

A CNN LSTM based solution for yield prediction