BILAL Lounnas

ريزوق زغلاش منار , يوسفي نور الهدى

Supply chain visibility and monitoring

مقري ليلى , خليفي زينب

Crowdsensing monitoring

أحديبي حليمة السعدية , توميات إيمان

Desktop application for managing a dental clinic

مادي عبد الستار , صابر أحمد أمين

Developement of a management desktop application

Hamrit Mohamed Lamine , Fekkar Lamri

Face recognition based on deep learning techniques

Ahlem YOUCEFI , Rim BEZAZ

OCR detection of checkboxes from PDF files.

MAKRI aymen abderraouf , SAOUDI omar

Desktop application connected to an ERP platform to manage the work of street vendors

AHMED Nedjm eddine , CHETRAUI Nassim

Desktop application connected to an ERP platform to manage the sales process, from design to order fulfillment

SLIMANE KADI CHEYMA

Analyzing Data Using Apriori Algorithm

MAZOUZI AYMENE MOUNSSIF

Creation of an E-commerce Mobile Application

Daikach Said

Towards an Information System based Optimization for Fuel Delivery Management (NAFTAL/M'sila study case)

BOUDJELLAL Houssam , HAMRIT Charafeddine

Development of an application to share health things

BRAHIMI AHMED RAMI , ABLI MOHAMMED ELAMIN

Development of application for transporting things

BRAHIMI MOHAMED MEZIANE , GHADBANE YASSINE

Development of a system for electronic health record (EHR)

سعدون بلال

Development of search engine based on vector space model

محروق عبد الحميد

Developpement d'un ERP industrial

حماني أمير

Development of a tool for optimizing Vehicle Routing Problem (VRP).

بونصلة أسامة

Design & implementation of a platform (website) for professional’s services

جمعي عبير

Implementation of a motif discovery algorithm, for analyzing protein/DNA sequences.

Abstract: Medical datasets often contain a large number of correlated and redundant variables, which can negatively impact the performance of disease prediction models. Principal Component Analysis (PCA) is a widely used dimensionality-reduction technique that transforms high-dimensional medical data into a smaller set of uncorrelated components while preserving most of the original variance. This study investigates the effectiveness of PCA as a preprocessing step for disease prediction. By reducing noise and emphasizing the most informative patterns in the data, PCA enables machine learning classifiers to achieve improved accuracy, faster training time, and reduced risk of overfitting. The proposed PCA-based workflow demonstrates how dimensionality reduction can enhance predictive performance in various medical applications, particularly those involving complex multivariate datasets such as genetic profiles, laboratory measurements, and imaging features.

Citation

BILAL Lounnas , Bureau de la stratégie de numérisation , ,(2025-12-09), Dimensionality Reduction in Medical Data: A PCA-Based Approach for Disease Prediction,The Fourth National Conference on "Mathematics, Biology and Medicine" 2025 - Modeling of Epidemic Propagation Phenomena,University of mohamed boudiaf, Msila

abstract

Citation

HADJI Ayat , Belgacem Brahimi , BILAL Lounnas , WAFA Bouras , ,(2025-12-03), bloom taxonomy question classification across languages opportunities and challenges in arabic NLP,National conference on artificial intelligence - techniques and recent application,University of ziane achour djelfa

The rapid development of the internet and social network platforms has given rise to a new field of research, social network analysis. This field of research has many fundamental problems, one of which is community detection. The objective of this research is to understand hidden connections among individuals. However, uncovering these connections are still challenging, despite the existence of several methods. In this paper, we propose a new algorithm called MCCD (Modified Cosine for Community Detection) for community detection in social networks based on node similarity. Our algorithm consists of two steps. In the first step, we use a novel cosine similarity formula to identify initial communities. In the second step, we merge these communities based on a new similarity measure. MCCD can be used in two different ways. The first way uses K as an input to identify the exact communities. The second way does not require K and aims to provide the best partitioning by maximizing modularity. Our algorithm has been tested on a variety of artificial and real-world networks, and the experimental results demonstrate its superiority over existing methods in detecting communities.

Citation

BILAL Lounnas , , (2024-07-03), A robust two-step algorithm for community detection based on node similarity, The Journal of Supercomputing, Vol:80, Issue:15, pages:23592-23608, Springer

The community structure is proving to have a very important role in the understanding of complex networks, but discovering them remains a very diÕcult problem despite the existence of several methods. In this article, we propose a novel algorithm for discovering communities in complex networks based on a modiÒed random walk (RW) and label propagation algorithm (LPA). First, we calculate the similarity between nodes based on the new formula of RW. Then, the labels are propagated by the obtained similarity of the Òrst step using LPA. Finally, the third step will be a new measure to Ònd the optimal partitioning of communities. Experimental results obtained on several real and synthetic networks reveal that our algorithm outperforms existing methods in Ònding communities.

Citation

Makhlouf Benazi , BILAL Lounnas , Rabah Mokhtari , , (2022), A complex network community detection algorithm based on random walk and label propagation, Transactions on Emerging Telecommunications Technologies., Vol:33, Issue:9, pages:73-91, Wiley