sedjokas
Dr. in Sciences. My research interest includes Data Sciences, Machine, Deep Learning, Multi-Agent systems, Applied Mathematics.
University of KinshasaRD Congo
Pinned Repositories
EBM-TBMR
MODEL 2 : A mathematical model that allows to simulate the spread of TBS and TBMR in DR Congo
Enhanced-Transfer-Model
Xception-Based Model Enhanced by Transfer Learning for Precise Diagnosis of COVID-19 from X-ray Thorax Images
Evacuation_Hospital_Building
GAML source code for Integration of Evacuee Categorization in an Agent-Based Simulation for Enhancing Building Evacuation in Case of Fire Breakout
ForDrBisuta
IMAGE-PROCESSING-CODE
This repository content my source code of SIFT descriptor in C and C++ with OpenCV. A short source code of David Lowe is integrated in our code. Enjoy !
myApply
Mes applications
MyModels
My thesis source codes, ABM, EBM and Hybrid model for controlling the spread of tuberculosis (TB) in a Multi-scale environment. Implemented using GAMA PLATFORM
OCR_Lingala
Enhancing Printed Lingala Script Recognition using Deep Learning Techniques
PercoMCV-Code-source
PercoMCV is a novel approach for the community detection in social networks. This approach (called PercoMCV) is based on a two-step algorithm. The first step consists on the use of the clique percolation method and the second step, based on the output of the first one, measures the influence of network nodes (in order to reduce the number of unclassified nodes) by using the Eigenvector Centrality method. The approach is then tested on artificial and real networks and compared to other community detection algorithms.
TBS-and-TBMR
Description: A mathematical model that allows to simulate the spread of TBS and TBMR in DR Congo
sedjokas's Repositories
sedjokas/PercoMCV-Code-source
PercoMCV is a novel approach for the community detection in social networks. This approach (called PercoMCV) is based on a two-step algorithm. The first step consists on the use of the clique percolation method and the second step, based on the output of the first one, measures the influence of network nodes (in order to reduce the number of unclassified nodes) by using the Eigenvector Centrality method. The approach is then tested on artificial and real networks and compared to other community detection algorithms.
sedjokas/ForDrBisuta
sedjokas/IMAGE-PROCESSING-CODE
This repository content my source code of SIFT descriptor in C and C++ with OpenCV. A short source code of David Lowe is integrated in our code. Enjoy !
sedjokas/myApply
Mes applications
sedjokas/MyModels
My thesis source codes, ABM, EBM and Hybrid model for controlling the spread of tuberculosis (TB) in a Multi-scale environment. Implemented using GAMA PLATFORM
sedjokas/OCR_Lingala
Enhancing Printed Lingala Script Recognition using Deep Learning Techniques
sedjokas/TBS-and-TBMR
Description: A mathematical model that allows to simulate the spread of TBS and TBMR in DR Congo
sedjokas/EBM-TBMR
MODEL 2 : A mathematical model that allows to simulate the spread of TBS and TBMR in DR Congo
sedjokas/Enhanced-Transfer-Model
Xception-Based Model Enhanced by Transfer Learning for Precise Diagnosis of COVID-19 from X-ray Thorax Images
sedjokas/Evacuation_Hospital_Building
GAML source code for Integration of Evacuee Categorization in an Agent-Based Simulation for Enhancing Building Evacuation in Case of Fire Breakout
sedjokas/RN_Affectation_Transportaion
Develop and evaluate a new hybrid algorithm based on classical methods and neural networks for solving the transportation problem, in order to improve the accuracy, computation speed and robustness of the problem solving.
sedjokas/ABM-TB
MODEL 3: An Agent-Based Model for controlling the spread of tuberculosis in a city
sedjokas/ABM-Tubersulosis
MODEL 3: An Agent-Based model for controlling the spread of tuberculosis in a city.
sedjokas/CoSiT
An Agent-Based Model
sedjokas/Covid_Dynamycs_HomeCare
A mathematical model of Covid-19 dynamics considering Hospitalized and Home-care Patients Groups. We used differential equations to describe the spread of the disease in the population.
sedjokas/EBM-TB
MODEL 1: A mathematical model for controlling tuberculosis in a country, case study of DRC
sedjokas/Fraud_Detection
Enhancing Bank Fraud Detection in Digital Transactions
sedjokas/HybridModelTB
MODEL4: A Hybrid model for controlling tuberculosis in a multi-scale environment
sedjokas/R0_profil
Plot R0 profil using python
sedjokas/statistics_TB_data_RDC
Statistics on data provided by the PNLT (Programme National de Lutte contre la Tuberculose) of DRC
sedjokas/TB_Hybrid_Model
Hybrid model for controlling the spread of tuberculosis (TB) in a Multi-scale environment. Implemented using GAMA PLATFORM