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AI for Good-Innovate for Impact



                      Use case – 42: AI 4 Health – Live Primary Health Care African

                      National Sign Languages Translation tool















                      Country: Zimbabwe
                      Organization: Purple Signs Global

                      Contact person: Dominic Tinashe Tapfuma,

                                                      tapfumadominic@ gmail .com

                                                      +263 773 744 246, +263 292 200 040


                      42�1� Use case summary table


                       Domain             Artificial Intelligence in Healthcare
                       The problem to be  1.   Limited access to critical health information and services.
                       addressed          2.   Difficulty in effective communication between deaf individuals and
                                            service providers.
                                          3.   Marginalization of deaf and hard of hearing individuals, hindering
                                            their ability to participate fully in society and the economy.

                       Key aspects of the   AI-powered Live Sign Language Translation
                       solution           Multimodal Communication Capabilities


                       Technology         AI-powered Translation, Text to Speech, Speech to Sign Language,
                       keywords           Facial Animation, AI Dubbing, Healthcare Accessibility, RMNCAH
                                          (Reproductive, Maternal, Newborn, Child, and Adolescent Health &
                                          Nutrition).

                       Data availability   Private data available
                       Metadata (type of   1.  Creating visuals with video
                       data)              2.  Can convert any text to visual.
                                          3.  Computer vision to computer generated visuals
                       Model Training and  Using AutoML, which automatically prepares a dataset for model
                       fine-tuning        training, performs a set of trials using open-source libraries such as
                                          scikit-learn and XGBoost, and creates a Python notebook with the
                                          source code for each trial run so you can review, reproduce, and modify
                                          the code.
                                          Using hyperparameter tuning for fine tuning the model . Making use of
                                          HyperOpt, scikit-learn, MLflow libraries.









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