KhalidBentaleb

bank-spring-batch

Calculator

Catalogue-Product-App-Back-End-With-Spring-Boot

Catalogue-Product-App-Front-End-With-Angular

Chatito

🎯🗯 Generate datasets for AI chatbots, NLP tasks, named entity recognition or text classification models using a simple DSL!

Game3D

Gestion_Ressources_Humains

HACKATHON-Inbox-Maroc

Prix du Hackathon Inbox Maroc

MoodBot_DarijaMorocco

Moodbot using Rasa in Darija Morocco Lanuage

Parallelization-of-heuristic-algorithms-and-their-implementation-in-Python

This project presents the parallel implementation of the A* algorithm using Python. Experimental results are compared with the sequential version of algorithm A* to find an optimal or near optimal path between two points in a grid. Parallelization is adopted in order to run several independent program tasks simultaneously to minimize the execution time of the entire program. The execution time is the biggest drawback of the algorithm A*, because it examines each of the neighboring nodes, from a starting node to an arrival node. Therefore, the fact that different threads are running simultaneously to find the path of each neighbor from the starting node to the arrival node should significantly reduce the computation time. To evaluate the performance of the parallel version of the A* algorithm we used two parameters; the execution time and the speedup. The simulations are conducted with a different number of threads.

KhalidBentaleb/bank-spring-batch

KhalidBentaleb/Calculator

KhalidBentaleb/Catalogue-Product-App-Back-End-With-Spring-Boot

KhalidBentaleb/Catalogue-Product-App-Front-End-With-Angular

KhalidBentaleb/Chatito

🎯🗯 Generate datasets for AI chatbots, NLP tasks, named entity recognition or text classification models using a simple DSL!

KhalidBentaleb/Game3D

KhalidBentaleb/Gestion_Ressources_Humains

KhalidBentaleb/HACKATHON-Inbox-Maroc

Prix du Hackathon Inbox Maroc

KhalidBentaleb/MoodBot_DarijaMorocco

Moodbot using Rasa in Darija Morocco Lanuage

KhalidBentaleb/Parallelization-of-heuristic-algorithms-and-their-implementation-in-Python

This project presents the parallel implementation of the A* algorithm using Python. Experimental results are compared with the sequential version of algorithm A* to find an optimal or near optimal path between two points in a grid. Parallelization is adopted in order to run several independent program tasks simultaneously to minimize the execution time of the entire program. The execution time is the biggest drawback of the algorithm A*, because it examines each of the neighboring nodes, from a starting node to an arrival node. Therefore, the fact that different threads are running simultaneously to find the path of each neighbor from the starting node to the arrival node should significantly reduce the computation time. To evaluate the performance of the parallel version of the A* algorithm we used two parameters; the execution time and the speedup. The simulations are conducted with a different number of threads.

KhalidBentaleb/rasa-masterclass

Data and code files for specific Rasa Masterclass episodes

KhalidBentaleb/Squash_Game

KhalidBentaleb/Stock-Price-Prediction-Using-Machine-Learning-Deep-Learning-Regression-Case

KhalidBentaleb/textSQL

KhalidBentaleb/Tourism_Website