Goal: Develop, implement, and deploy a Question Answering (QA) system by training a Large Language Model (LLM) using a car manual. The system aims to provide drivers with real-time answers regarding car functionalities while they are driving.

Goal: Develop a cloud platform based on the Azure platform. The platform presents an efficient space for automatic training, validation, testing, and monitoring of an ML model that is able to learn user preferences. The platform provides, Data Engineering, Model development, Continuous Development/ Integration, model monitoring

   Task: Development of an artificial intelligence solution employing YOLOv5 and YOLOv8 algorithms to evaluate the quality of African pears, achieving mean average precision scores of 88.2% and 89.9%,       respectively, using a dataset from three regions in Cameroon. The deployed YOLOv8 solution, a pioneering intelligent system, operates on a web application for inspecting African plum quality.
   Role: Project Founder/Leader
  Tools management: Google Colab, Jira, git, Bitbucket, Confluence, MLOPS, Roboflow
  Libraries: Pytorch, Scikitlearn
  IDE: Visual Studio Code
  Implemented stages: Data acquisition, Data process, ML development, deployment on Roboflow
  Demo: https://drive.google.com/file/d/1At5CnaDXv4K1u3hSaorHaMJJSodk6GJj/view
  Deployed model: https://demo.roboflow.com/plums-0ucd7/3?publishable_key=rf_Jow5TwJ4d7VFcm2Eg5As8MrnR4F2
  Publications
    Indaba 2023: https://openreview.net/group?id=DeepLearningIndaba.com/2023/Conference#tab-active-submissions
        - https://openreview.net/forum?id=uBPApDJPfn
       - https://drive.google.com/file/d/19rQQzdQmNKfFjxGm1IxUbEy13oA0sqq5/view

  To do: Intelligent Quality Assessment of African Plums and Deployment on Resource-Constrained Devices

The aim of the project is to implement an AI model which predicts if a Covid-19 patient arriving in the hospital will end in a critical state and therefore will need intensive care. The model relies on a
Decision Tree, 3D Convolutional Neural Network, and Hierarchical Probabilistic Logic Programs.

Build a supervised Machine Learning for classifying genomic data that represent portions of genomic sequences of individuals belonging to a certain population. A Convolutional Neural Network is used to determine if the genomic sequences bear traces of a process of natural selection. Training performed on simulated data shows that the proposed model can accurately predict neutral and selection processes on portions of genomes taken from real populations with almost 90% accuracy.

Task: The aim of the project is to implement different neural network architectures on an FPGA
platform based on Xilinx Ultrascale for real-time image processing applications. Performance, both in
terms of execution time and efficiency (accuracy) are compared with those running on a GPU-
based platform. The main objective is to use the FPGA platform as an accelerator for the inference
phase in Deep Neural Networks.
Role: Machine Learning Engineer
Tools management: Trello, git, Bitbucket, google meet
Libraries: Tensorflow, Keras Vitis AI
IDE: Visual studio code
Programming Language: Python
Machine learning models: Deep Neural Networks

Task: Build a computer vision system embedded in a mobile phone for classifying medicinal plant leaves.
Tools management: Trello, git, Bitbucket+Libraries: Tensorflow, Tensorboard, Keras, Matplotlib, Numpy, Google Colab
Role: Machine Learning Engineer and project manager
IDE: Visual studio code, TexMaker
Machine learning Models: Convolutional neural networks, AlexNet, VGGNet, RestNet
Publication: The work is published in the Journal of Computer Science
   https://thescipub.com/pdf/jcssp.2023.1387.1397.pdf

Task: Build a computer vision that classifies dates (good and lost skin) and tomatoes fruit
Role: Machine Learning Engineer
Tools management: Trello, git, Bitbucket
Libraries: Tensorflow, Tensorboard, Keras, PyTorch, CNTK
IDE: Visual Studio Code
Machine learning Models: Convolutional neural network, Faster RCNN