Newsletter

1. DHS-To-Database-dhs2CSVTables-simplified
   DHS AI | Database conversion | Open Source | Contribute | Python Sep 29, 2024
   We are excited to announce the release of our new project: DHS-To-Database-dhs2CSVTables-simplified! This open-source tool is designed to simplify the conversion of raw Demographic and Health Surveys (DHS) data into a format suitable for database storage in CSVTables format.
   
   What is DHS-To-Database-dhs2CSVTables-simplified?
   Our project serves as a convenient wrapper around the existing DHS-To-Database tool developed by Harry Gibson. While the original tool is powerful, we recognized the need for a more accessible and user-friendly way to handle DHS data conversions. This new wrapper allows users to perform these conversions effortlessly without delving deep into the original library internals.
   
   Key Features

   • User-Friendly Wrapper: Provides an easy-to-use interface for converting raw DHS data.
   • Supports Python 3.8 and Above: Ensure compatibility with modern Python environments.
   • Simplified Usage: Designed for seamless interaction with raw DHS data.
   • CSV to SQLite Conversion (New in v0.2.0): Now supports converting relational CSV tables to a SQLite database using the csvs-to-sqlite command-line tool. This allows seamless migration of DHS data into a more structured relational database format.

   
   For detailed documentation, examples, and to get started with the tool, please visit our GitHub repository: DHS-To-Database-dhs2CSVTables-simplified.
   
   Contributing
   We welcome contributions from the community! If you have suggestions or improvements, please feel free to fork the repository and submit a pull request. Your contributions can help enhance this tool for everyone.
   
   Acknowledgments
   A special thanks to Harry Gibson for his foundational work on the DHS-To-Database tool. Our project builds on his incredible contributions to the open-source community.


2. NIGATWA1087
   WebApp Framework Feb 02, 2024
   We are thrilled to announce the official launch of NIGATWA1087, a powerful web application boilerplate framework designed to accelerate your development journey.
   
   Effortless Development with a Solid Foundation
   
   NIGATWA1087 empowers you to:

   • Hit the ground running: Skip repetitive setup tasks with a comprehensive boilerplate structure, ready to customize
   • Build with confidence: Leverage the time-tested foundation used in our own projects like kofiyatechapps
   • Build a robust dashboard like FLOWER: Leverage NIGATWA1087's foundation to create complex and data-rich dashboards like FLOWER
   • Focus on what matters: Streamline user management with built-in user authentication, registration, and secure password recovery
   • Empower your users: Equip administrators with tools to support and manage your webapp effectively
   • Handle the unexpected: Ensure a seamless user experience with error handling for incorrect URLs
   • Deploy where you need: Choose between AWS cloud deployment with Amazon RDS database or local development environment with either SQLite or PostgreSQL database


3. CIAO BAYESIAN
   DHS AI | Algorithm Oct 27, 2023
   Building on the success of KILIMA TULIP AI, we are excited to introduce our latest initiative. This project, rooted in Explainable AI (XAI), focuses on child survival risk analysis using Bayesian statistics. By employing transparent and interpretable Bayesian methods, we aim to provide not just insights but a clear understanding of the factors influencing child survival. Our mission is to harness the power of data science, XAI, and social change to drive informed decisions and combat child mortality.
   
   For those eager to explore the CIAO BAYESIAN results and insights, you can access them conveniently through our user-friendly FLOWER Dashboard.
   
   Stay tuned as we embark on this journey at the intersection of data-driven discovery and transparent AI solutions, working to save young lives and create a better future for all.


4. FLOWER
   DHS AI | Dashboard Sep 01, 2023
   Welcome to the FLOWER Dashboard, your gateway to exploring child survival risk insights powered by the KILIMA TULIP AI model. With the FLOWER Dashboard, you can customize your analysis by selecting a specific country, feature group, and risk factor.
   
   Our AI-driven predictions, based on the KILIMA TULIP AI model, provide valuable insights into child survival risk. These insights empower you to allocate resources effectively, develop targeted interventions, and contribute to research efforts.
   
   Get started today by visiting the FLOWER Dashboard and making data-driven decisions to improve child well-being.


5. KILIMA TULIP AI
   DHS AI | Deep Learning | Africa Jul 30, 2023
   Welcome to KILIMA TULIP AI, a project dedicated to predicting under-five (U5) child survival risk in Africa using Deep Learning AI techniques. As part of our mission to improve child health, this project builds upon the success of our previous DEEP MINTILO AI project, where we achieved a remarkable above 90% accuracy in predicting child survival risk for under-five children in Ethiopia.
   
   With KILIMA TULIP AI, we are excited to share that we have achieved an even more remarkable performance of 95% accuracy in predicting child survival outcomes across the five African countries we are focusing on, namely Ethiopia, Ghana, Uganda, South Africa, and Zimbabwe. By leveraging the DHS survey datasets from these regions, we aim to further enhance child survival rates through the power of artificial intelligence.
   
   Our team at kofiyatech is thrilled to present this groundbreaking technology. Join us in our mission to make a positive impact on the well-being of under-five children.


6. DEEP MINTILO AI
   DHS AI | Deep Learning | Ethiopia Jul 14, 2023
   Welcome to DEEP MINTILO AI, a project dedicated to predicting under-five (U5) child survival risk in Ethiopia using Deep Learning AI techniques. This project is an extension of our MINTILO AI initiative and aims to leverage the power of neural networks to improve child survival rates.
   
   With DEEP MINTILO AI, we have achieved remarkable performance gains, surpassing 90% accuracy in predicting child survival outcomes. By harnessing the potential of deep learning, we can now uncover crucial risk factors and patterns associated with child survival more effectively than ever before.
   
   Our team at kofiyatech is excited to offer you a demo of this cutting-edge technology. Reach out to us to learn more about the implementation and the potential impact on child well-being. Together, we can make a difference.


7. MINTILO AI
   DHS AI | Algorithm | Contribute | Open Source | Python Apr 06, 2023
   An open source project dedicated to utilizing Python and machine learning techniques to provide insights for policy makers in eradicating child mortality. By analyzing data from DHS surveys, MINTILO AI identifies risk factors associated with child survival, leveraging artificial intelligence to contribute to the collective efforts in improving child survival rates.
   
   This project relies on the DHS AI WebApp platform as its data source. To access and contribute to MINTILO AI, visit the GitHub repository: GitHub - MINTILO AI.
   
   For more information and to contribute, please feel free to reach out to us. Let us know if you would like to contribute and be part of this impactful initiative.


8. WATOTO SURVIVAL
   DHS AI | Algorithm | Contribute | Open Source | R Apr 15, 2023
   An open source project that utilizes classical survival analysis methods in R, including Kaplan-Meier and Cox regression techniques. Its primary objective is to enable researchers and graduate students to collaborate and identify risk factors related to child survival in children under-five years old. By leveraging statistical techniques and the rich dataset from DHS surveys, WATOTO SURVIVAL aims to provide valuable insights for policymakers and researchers in their efforts to address this critical issue.
   
   This project relies on the DHS AI WebApp platform as its input source. You can access the project on GitHub by visiting the following link: GitHub - WATOTO SURVIVAL.
   
   For more information and to contribute, please feel free to contact us. We welcome your participation and contributions to make a meaningful impact in this crucial area of research.


9. DHS AI WebApp
   DHS AI | WebApp | AI Initiative for Public Health and Tropical Medicine Feb 17, 2023
   The DHS AI WebApp is a user-friendly, SQLite-based database repository specifically designed for DHS survey data. It serves as a powerful resource for Data Science projects, providing researchers, data analysts, and graduate students with easy-to-use querying capabilities tailored to their specific research subjects.
   
   Our project initiative aims to promote the use of data science and machine learning techniques with DHS survey data. It was inspired by a study published by Bitew, et al (2020) that highlighted the untapped potential of utilizing these methods.
   
   We are committed to supporting researchers interested in machine learning and data science. Through our project, we provide valuable resources, guidance, and assistance to help the researchers explore the vast opportunities offered by DHS survey data in their research endeavors.
   
   Additionally, we strive to encourage the adoption of a consistent and unified framework for working with DHS survey data when developing machine learning algorithms. By advocating for standardized practices, we aim to improve the reproducibility and comparability of results across different studies and research projects. Our goal is to establish a solid foundation that enables researchers to effectively harness the power of DHS survey data in their machine learning endeavors.