drstef / machine-learning-and-digital-signal-processing-for-genome-classification

Supervised classification of various species DNA sequences using FFT and Machine Learning.

Machine Learning - Deep Learning Projects

Advanced projects

This section contains Research and Development projects in Machine Learning and Deep Learning that require original developments. They call on our expertise in Digital Signal Processing, Optimization, Calculus, Linear Algebra.

• Deep Learning and Digital Signal Processing for Environmental Sound Classification

Automatic environmental sound classification (ESC) based on ESC-50 dataset (and ESC-10 subset) built by Karol Piczak and described in the following article:
"ESC: Dataset for Environmental Sound Classification." by Karol J. Piczak. 2015. In Proceedings of the 23rd ACM international conference on Multimedia (MM '15). Association for Computing Machinery, New York, NY, USA, 1015–1018. https://doi.org/10.1145/2733373.2806390"

Multi-feature Convolutional Neural Networks (CNN) achieves accuracy close to 99%, with custom pre-processing and a fusion mel-spectrograms + complex wavelet transforms.
The last confusion "sea waves" "rain" is solved by developping an original transform of the complex CWT. This Transform, aT-CWT replaces the phase of the CWT for stationary, pseudo-stationary sounds with a Gaussian characteristics.
With the aT-CWT transform, the multi-feature CNN model achieves 100% accuracy.

• Machine Learning and Digital Signal Processing for Genome Classification

In this project we develop effective methods for classifying mitochondrial genomes (DNA sequences) based on Digital Signal Processing, Machine Learning, Deep Learning. This is on-going research and results will be published on a regular basis. As a starting point we analyzed the following paper:
"ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels" by Gurjit S. Randhawa , Kathleen A. Hill and Lila Kari. https://doi.org/10.1186/s12864-019-5571-y

Their alignement free DNA sequence classification approach: ML-DSP is very effective. By introducing a simple alignment technique and short FFTs: ML-FFT + SoftAlign, we outperform ML-DSP with difficult datasets: Fungi, Insects.

• Deep Learning and Digital Signal Processing: Voice Activity Detection

• Machine Learning and Digital Signal Processing: Sound Source Localization

Standard projects

This section is a portfolio of Machine Learning projects with Python and various visualization and analysis tools. Most of these projects were carried out within the framework of IBM certifications. They are presented with Jupyter Notebooks.
Some projects have been improved by incorporating more in-depth data analysis, better graphs, advanced ML techniques.

• SpaceX Falcon 9 First Stage Landing Prediction

In this project, we predict if the Falcon 9 first stage will land successfully. Project includes: SpaceX data collection, Data Wrangling, Webscraping, EDA with SQL Queries & Data visualization, SpaceX Launch Records Dashboard, Launch Sites Locations Analysis with Folium, Machine Learning classification with optimization of hyperparameters and selection of best model: KNN, Decision Tree, SVM, Logistic Regression.

• Machine Learning with Python

A widerange of small projects with various ML techniques, prediction, supervised and unsupervised classification: Linear Regression, Polynomial Regression, Non-Linear Regression, Recommandation Systems, KNN, Customer Segmentation with K-Means, Hierarchical Clustering, Density-Based Clustering, Logistic Regression.

• House sales in King County USA

The project consists of finding the best model for predicting home prices in King County, USA in Washington State, based on a dataset of homes sold between May 2014 and May 2015. Prediction accuracy was improved by implementing a spline regression model.
One Jupyter Notebook includes interactive Folium maps (interactive maps will not display on Github).

• Loan status prediction using Classification Algorithms

Loan Status Prediction using Supervised Classification Algorithms: KNN, Decision Tree, SVM, Logistic Regression.

Data Analysis - SQL, MySQL

• Insights in Boston real estate - Statistical analysis

Old dataset on housing prices derived from the U.S. Census Service to present insights based on our experience in Statistics. Median value of houses bounded by the Charles river, of owner-occupied units built before 1940, relationship between Nitric oxide concentrations and the proportion of non-retail business acres per town, impact of weighted distance to the five Boston employment centres on the median value of owner-occupied homes.

• Data Analysis with Python

Dataset: car dataset including various makes, specifications and prices.
After cleaning the dataset, running statistics, identifying the most relevant variables, we develop several models that will predict the price of a car using a set of features/variables.

• Data Visualization with Python

Word Cloud	Folium with markers	Choropleth

• Databases and SQL for Data Science

• Stock extraction & vizualisation - yFinance, Webscraping

Digital Signal Processing

• Microphone Array - Beamforming

• 6 microphones circular array - Generalized Sidelobe Canceller (GSC) - Validation

• Binaural Beamforming - Generalized Sidelobe Canceller (GSC) - Validation

• A few words about complex wavelet transforms

Modeling and Scientific Computing

• Integral equation - Boundary Elements Method (BEM)

• Electroacoustics - Simulations

• Discrete optimization: Genetic Algorithms - Bin-packing

• Matplotlib - 3D surfaces

"Figure 8" toroid	Gyroid	Truncated cuboctahedron	Helicoid-Catenoid

• Linear Algebra problems

🔭 I’m currently working on advanced projects in ML & DL
👯 I’m looking to collaborate on Digital Signal Processing, Machine Learning, Deep Learning
📫 How to reach me: [email protected]