Pinned Repositories
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.
BO-DNA
BO-DNA: Biologically optimized encoding model for a highly-reliable DNA data storage
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-Effective-DNA-Storage-System
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
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.
abdul-rasool's Repositories
abdul-rasool/Abnormal-Contracts-Detection
A Feature-Based Robust Method for Abnormal Contracts Detection in Ethereum Blockchain
abdul-rasool/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).
abdul-rasool/Hong-Kong-Stock-Market-Dataset
abdul-rasool/EDS-Effective-DNA-Storage-System
EDS: An Effective DNA-Based File Storage System for Practical Archiving and Retrieval of Medical MRI Data
abdul-rasool/MFOS
MFOS proposes a computational evolutionary approach based on a synergistic Moth-Flame Optimizer by levy flight and opposition-based learning mutation Strategies to
abdul-rasool/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.
abdul-rasool/ACR-SA-Main
ACR-SA: Attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis.
abdul-rasool/BO-DNA
BO-DNA: Biologically optimized encoding model for a highly-reliable DNA data storage
abdul-rasool/dna_rs_coding
Error correction scheme for storing information on DNA using Reed Solomon codes
abdul-rasool/DNADigitalDataStorage
This project purpose to research procedure of digital storage through DNA.
abdul-rasool/LSTM-based-Model-for-Stock-Market-Prediction
LSTM based Model for Real-time Stock Market Prediction on Unexpected Incidents
abdul-rasool/Python-Data-Science-
Python Data Science Handbook: full text in Jupyter Notebooks
abdul-rasool/SBSPS-Challenge-1087-TWITTER-SENTIMENTAL-ANALYSIS-ON-COVID-19
abdul-rasool/twitter-sentiment-analysis
Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.
abdul-rasool/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.