Qualifications: PhD, Particles Physics, Specialized Master, Quantum Physics, General Master, Applied Physics
Dr. Khaled Belkadhi is an Associate Professor at the Mediterranean School of Business (MSB) in Tunisia. He holds a Ph.D. in Particle Physics from the University of Tunis El Manar and has an extensive background in both academia and research. His career includes working on hadronic calorimetry for the International Linear Collider at CERN, Switzerland and Monte Carlo simulation for internal and external dosimetry. Dr. Belkadhi teaches a range of subjects, including mathematics, machine learning for business analytics, and data analysis with Python. His research focuses on applied mathematics, Monte Carlo simulation, machine learning, and radiation dosimetry. He has published several papers in leading journals and has received the 2023 MSB Best Teacher Award.
Toward a stochastically robust normalized impact factor against fraud and scams.
https://link.springer.com/article/10.1007/s11192-020-03577-4
Radiation dose for external exposure to gamma-ray using artificial neural network and MC simulation.
https://pdfs.semanticscholar.org/7c79/6f565001febd4d33825663ffe6775351d29b.pdf
Why Would Firms Fund (Directly or Indirectly) Basic Research? Research Coffee Corner-8th Session
Detecting Fraudulent Financial Statements Using Machine Learning Techniques: A comparative Analysis
Detecting Fraudulent Financial Statements Using Machine Learning Techniques. Research Coffee Corner - Session 4, SMU
Basic Research and Market Concentration. 9th Euro-African Conference in Finance and Economics (CEAFE/MWET)
Basic Research and Market Concentration. Journée Méditerranéenne Modélisation et Analyse Statistique et Economique
Interpolation Based IoT Sensors Selection
Detecting Fraudulent Financial Statements Using Machine Learning Techniques: A Comparative Analysis, MASE Seminar Modélisation et Analyse Statistique et Economique
Detecting Fraudulent Financial Statements Using Artificial Neural Networks , MASE Seminar Modélisation et Analyse Statistique et Economique
First results of the CALICE SDHCAL technological prototype
Construction and commissioning of a technological prototype of a high-granularity semi-digital hadronic calorimeter
Performance of Glass Resistive Plate Chambers for a high-granularity semi-digital calorimeter
Dose calculation using a numerical method based on Haar wavelets integration
Dose calculation using Haar wavelets with buildup correction
Evaluation of the Hubbell rectangular source integral using Haar wavelets method
Hubbell rectangular source integral calculation using a fast Chebyshev wavelets method
Voxel-based internal dose prediction using machine learning with organ-specific features and Monte Carlo simulations
https://www.sciencedirect.com/science/article/abs/pii/S0969806X24009897?via%3Dihub
