abdul-rasool Goto Github PK

abnormal-contracts-detection

A Feature-Based Robust Method for Abnormal Contracts Detection in Ethereum Blockchain

acr-sa-main

ACR-SA: Attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis.

autism-detection

Exploring Autism Detection and Classification through Eye-Tracking Scanpath and AI-based Models: Challenges and Insights

dna_rs_coding

Error correction scheme for storing information on DNA using Reed Solomon codes

dnadigitaldatastorage

This project purpose to research procedure of digital storage through DNA.

eds

EDS: An Effective DNA-Based File Storage System for Practical Archiving and Retrieval of Medical MRI Data

hong-kong-stock-market-dataset

improved-machine-learning-based-predictive-models-for-breast-cancer-diagnosis

Breast cancer diagnoses with four different machine learning classifiers (SVM, LR, KNN, and EC) by utilizing data exploratory techniques (DET) at Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD).

lstm-based-model-for-stock-market-prediction

LSTM based Model for Real-time Stock Market Prediction on Unexpected Incidents

mfos

MFOS proposes a computational evolutionary approach based on a synergistic Moth-Flame Optimizer by levy flight and opposition-based learning mutation Strategies to

python-data-science-

Python Data Science Handbook: full text in Jupyter Notebooks

sbsps-challenge-1087-twitter-sentimental-analysis-on-covid-19

stock-prediction

Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping.

twitter-sentiment-analysis

Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.

twitter-sentimental-analysis

I have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Linear Classifier, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and ROC(Receiver Operating Characteristic) and AUC(Area Under Curve) and finally shown how they are classifying the tweet in positive and negative.