Omdena Chapter Page: Iraq

Omdena Iraq Chapter - Omdena Chapters
Completed Project(s)

Building Open Source NLP Libraries & Tools for the Arabic Language

The problem

1. Arabic is the 5th most spoken language in the world and the 1st language of the Arab world countries, making it extremely important worldwide.

2. Arabic is grammatically complex and has free order properties, which all pose significant challenges in Arabic NLP applications.

3. There are 3 types that characterize Arabic, including Classical Arabic, Modern Standard Arabic & Dialect Arabic.

4. Tools built by big tech and accessible to the majority of the world are limited to translating only a few of the most popular languages.

The Project Outcomes

The envisioned deliverables can be broken down into two main areas:

1. Build open-source Arabic NLP libraries for sentiment analysis, morphological modeling, dialect identification, and named entity recognition

2. Build 5:8 core functions to support Arabic NLP (lemmatization, stop words, tokenizing text, word embedding, part of speech tagging.. etc.) like NLTK but for Modern Standard Arabic.

Source Code: https://github.com/OmdenaAI/Arabic-Chapter

Demo: https://www.youtube.com/watch?v=PaWCX2IG7eo

Preventing Gang and Gun Violence via Social Media Analysis

The Project Goals

This 4-week project aims to help uncover how we can predict violence and gang actions from analyzing social tweets and communities. We will achieve this by :

1. Scrapping relevant data from different social media platforms; mainly tweets and Facebook.

2. Cleaning, processing, and labeling the data.

3. Implementing well-known algorithms and libraries such as Girvan Newmann algorithm,  NetworkX, to analyze and detect communities.

4. Training different machine learning models to classify violence from non-violent tweets.

5. Evaluating and visualizing the results.

You will also have the chance to write about your experience on the Omdena blog, putting your work in front of more than 19,000 of Omdena’s social media.

Source Code: https://github.com/OmdenaAI/omdena-iraq-gun-violence

Desertification Detection with Deep Learning and Satellite Data (completed)

 

https://github.com/OmdenaAI/iraq-chapter-desertification-detection
https://share.streamlit.io/mohammed-taie/iraq-desertification/app.py?radio=Home
https://omdena.com/blog/desertification-detection-with-machine-learning-and-satellite-data/

 

The Background 

According to Savory.globalDesertification is the persistent degradation of dryland ecosystems by variations in climate and human activities.”

In other words, it is making what used to be arable farming land into useless one. It is one of the greatest environmental challenges today and unfortunately mostly targets the world’s poorest population.

Desertification leads to so many other problems from affecting the agricultural sector leading to more hunger, to increasing the displacement of people who used to live on these lands yields and what used to be green fields, which in return have its own set of problems. 

Fortunately, though, most of this degradation can be reversed and treated by many methods that’s why many reports have been published addressing this important topic and demanding immediate actions. It is also why most of the countries suffer from it, due to obvious disregard by the authorities of these regions and countries.

The Problem

1. According to recent reports, the rate of desertification in Iraq has increased to 39% and 54% of the country’s agricultural land faces drought and land degradation.

2. According to a report by the Republic of Iraq Ministry of Agriculture, Iraq is losing 100 square kilometers annually from its arable lands as a consequence of desertification.

3. Iraq’s highly excessive dependence on water that comes outside of its borders, the mismanagement of water, inefficient farming habits and the already dry climate makes it more vulnerable to climate change.

4. Having more reliable sources to know where to focus the efforts could be the beginning of solving this huge challenge and providing immediate help to the most endangered regions.

The Project Goals

AI has proven to provide more and more accurate forecast results in recent years, allowing the formulation of solutions in a faster and agile way than before. 

Here we’ll work to harvest this technological advancement to help predict the most areas and regions that could fall victim to desertification in the upcoming years in Iraq.

That is why for 4 weeks our goal will be to produce a forecasting model to predict the status of different land covers in Iraq. This will include working on the following:

1. Collect free and publicly available Satellite data that covers Iraq over the years and in different seasons.

2. Using Supervised and Unsupervised learning algorithms to classify different land type covers.

3. Analyze the loss of green, degradation of lands in Iraq over the years (using NDVI, NDWI, and other indices), and build a forecast model based on that information.

4. Build a dashboard visualizing the areas affected and the future prediction using streamlit or other freely available tools.

The Learning Outcomes

1. Geospatial and Satellite data that will include learning the basics and advanced techniques to process satellite data.

2. Implement supervised and unsupervised machine learning and deep learning algorithms to classify different land types. 

3. Building a dashboard to visualize and present our results in an impactful way.

4. The project will include conducting multiple workshops on the above topics

 

The Tasks & Timeline

Week 1

Week 2

Week 3

Week 4

–Task1: Data collection

– Task 3/4: The models research phase

–Task1:Data collection

–Task2:Data pre-processing

–Task 3/4: The models research phase

–Task 3/4:

Testing & Choosing the best Models

–Task2:Data pre-processing

–Task 3/4: Implementing the models

–Task 5:

preparation for data visualization

–Task 3/4: Implementing the models

–Task 5:

Data visualization and Deployment 

Omdena Iraq Chapter
Iraq Chapter Lead(s)

Rasha Salim

She is a computer engineer with a passion to utilise technology for improving the human condition. Has a background in web and Android development and multi programming languages under her belt. She set off to enter the world of AI and data science in 2019 and never stopped since. Now as a machine learning engineer with experience working in computer vision, Geospatial data, task management, communication skills, and writing, her mission is to spread the word and make it possible for others to build a career out of AI4Good notion

Faris Baker

Growing up between Iraq & Kuwait, Farhad moved to the US and received his bachelor degree in computer science from University of SanFrancisco in California. Later, he travelled to the United Kingdom and did his postgraduate studies in Systems Integration from Napier University in Scotland. His professional career included working in multinational companies, Fujitsu & Logica, in software engineering. He also ran the Artificial Intelligence Lab at the Arab Open University where he was involved with and co-authored many research studies. Finally, Faris returned back to the UK and worked as a Senior Engineer at Vertical Aerospace. His aspirations to work in projects related to the united nations sustainable developments contributing his skills and experiences with continuous learning.