Vinasetan Ratheil HOUNDJI


E-Agriculture

Sorghum Yield Prediction using Drones and Machine Learning

Reference paper: Sorghum Yield Prediction using Machine Learning, J. G. N. Zannou, V. R. Houndji. International Conference on Bio-engineering for Smart Technologies (BioSMART'2019), Paris, France.

   

Agriculture is one of our greatest assets. Estimation of a future agricultural production is an important challenge for farmers. In this work, we propose a system based on artificial intelligence to estimate farm yields. We use drones and Artificial Intelligence (in particular Machine Learning algorithms) to estimate an agricultural yield production. For the first experiments, we focus on the Sorghum. Sorghum is the fifth largest cereal with regards to volume of production, after maize, rice, wheat and barley. It is the main cereal for many low-income populations living in the semi-arid tropics of Africa and Asia. We propose and experiment an approach with Machine Learning to estimate the agricultural yield of a farmland of Sorghum before the harvest. These algorithms allow us 1) to detect the different ears of Sorghum on an image and 2) to estimate their weight. On our dataset, we obtain an average accuracy of 74,5% for the detection of sorghum.



Academic performance prediction

Academic performance prediction

Reference paper: AmonAI : a students academic performances prediction system, I. Houndayi, V. R. Houndji, P-J Zohou, E. C. Ezin. Accepted at the 11th EAI International Conference on e‐Infrastructure and e‐Services for Developing Countries.

   

To ensure a great training of the students, it is important for them to receive adequate support to improve and succeed in their studies. Unfortunately several conditions, the highest number of students mainly, make more difficult to monitor students. To reduce the failure rate of students we iniate this project. It will then dive into the application of machine learning techniques on the available data to make the prediction of academic performances of the students in the LMD system. We work on AmonAI, a system that allows to anticipate their results in order to reduce their failure as much as possible by taking appropriate decision. The system makes the prediction of the students academic performances through classification and regression in each teaching unit of a semester and provide visualizations based on these predictions.



Surgical operation in Virtual Reality

Surgical VR: Surgical operation in Virtual Reality

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In the health sector, practice is very important for surgeons who must master the surgical procedures as well as the techniques and tools used for the success of each surgical operation. In this work, we realized an accessible tool that can help them to carry out sessions of practical work in an ideal virtual environment, by putting at their disposal a medical simulation application in virtual reality. We propose in this work an implementation of a virtual reality simulation module of laparoscopic appendectomy. The prototype is totally functional.



Natural Language Processing

Coming soon...

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Coming soon...