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title : ALGEBRA 2 Type de document : electronic document Auteur : D.Rabeh Bouden, Author Editeur : جامعة ميلة Date de publication : 2025 Nombre de pages : 139p. Dimensions : ISBN (ou autre code) : pb207 Note général : This textbook is specifically designed for first-year LMD students in Mathematics and
Computer Science. Chapter 1 introduces the fundamental concepts of vector spaces, with
classical examples from real, complex, and functional spaces. Chapter 2 focuses on linear mappings, including key notions such as kernel, image, and the rank-nullity theorem.
Chapter 3 deals with matrices and their standard operations, such as multiplication, transposition, determinant calculation, invertibility, and matrix rank. Finally, Chapter 4 is dedicated to systems of linear equations and presents various methods of solution, including
Gaussian elimination and Cramer’s rule. This course also includes a wide range of solved
exercises to help students reinforce their theoretical understanding through practice. We
hope this document meets the students’ expectations and effectively supports their academic success. At the end of this course, a list of references is provided for those who
wish to explore the subject in greater depth.Langue : English (eng) Lien vers la ressource électronique : http://dspace.centre-univ-mila.dz/jspui/handle/123456789/4588 ALGEBRA 2 [electronic document] / D.Rabeh Bouden, Author . - جامعة ميلة, 2025 . - 139p. ; PDF.
ISSN : pb207
This textbook is specifically designed for first-year LMD students in Mathematics and
Computer Science. Chapter 1 introduces the fundamental concepts of vector spaces, with
classical examples from real, complex, and functional spaces. Chapter 2 focuses on linear mappings, including key notions such as kernel, image, and the rank-nullity theorem.
Chapter 3 deals with matrices and their standard operations, such as multiplication, transposition, determinant calculation, invertibility, and matrix rank. Finally, Chapter 4 is dedicated to systems of linear equations and presents various methods of solution, including
Gaussian elimination and Cramer’s rule. This course also includes a wide range of solved
exercises to help students reinforce their theoretical understanding through practice. We
hope this document meets the students’ expectations and effectively supports their academic success. At the end of this course, a list of references is provided for those who
wish to explore the subject in greater depth.
Langue : English (eng)
Lien vers la ressource électronique : http://dspace.centre-univ-mila.dz/jspui/handle/123456789/4588 Réservation
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Barcode Call number Media type Location Section Statut 207 pb/207 Pedagogical publication Central Library Pedagogical publication AVAILABLE Numeric items
pb207.pngPortable Network GraphicsAn Intelligent Approach to Wild Fire Prediction: Case Study of Jijel Region
title : An Intelligent Approach to Wild Fire Prediction: Case Study of Jijel Region : 3rd cycle LMD Doctorate Degree in Computer Science Specialty: Artificial Intelligence and Its Applications Type de document : electronic document Editeur : جامعة ميلة Date de publication : 2026 Nombre de pages : 101p. Dimensions : Matériel d'accompagnement : قرص مضغوط ISBN (ou autre code) : D.N00402 Langue : English (eng) Langue originale : English (eng) Résumé : Wildfires have intensified across the Mediterranean under compounding heatwaves, drought,
and human pressure, making a shift from reaction to prevention essential. This thesis develops an operational wildfire susceptibility mapping (WSM) framework for Jijel Province
(Algeria) that integrates Geographic Information Systems (GIS) and machine learning. Fourteen conditioning factorstopography (elevation, slope, aspect), climate (minimum, maximum,
and average temperature, humidity, wind speed, precipitation), vegetation (Normalized Difference Vegetation Index, NDVI), and anthropogenic pressure (distance to roads and settlements)are harmonized on a 30,m grid and linked with civil protection fire records, yielding an analysis-ready matrix of approximately 2.6 million samples. Collinearity screening (maximum Variance Inflation Factor, VIF = 7.58; minimum tolerance = 0.11) and light k-means denoising of the Low class improve model stability We train Random Forest (RF), Extreme Gradient Boosting (XGBoost), a shallow Neural Network (NN), and Support Vector Machine (SVM) models with class balancing and probability calibration. On the internal holdout set, RF attains an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.99 and an F1-score of 0.98, with XGBoost and
NN performing similarly, and SVM showing solid performance (AUC = 0.94). Continuous susceptibility scores are calibrated and discretized into five classes (LowVery High). Independent time-forward validation overlays wildfire occurrences from 20242025 (Civil Protection
records and Moderate Resolution Imaging Spectroradiometer, MODIS, data) on the susceptibility maps: 87.73
The outputscalibrated probability surfaces, clearly legended risk classes, and uncertainty layersare suitable for integration into web-based GIS dashboards to support resource prepositioning, fuel-break maintenance at the wildlandurban interface (WUI), and thresholdbased early warning systems. Limitations (sparse meteorological stations and ignition geolocation uncertainty) and targeted improvements (denser in situ sensing and spatially blocked cross-validation) are discussed. The framework is transferable to similar Mediterranean environments.Lien vers la ressource électronique : https://syngeb.univ-mila.dz/fr/opac/result_details/949820 An Intelligent Approach to Wild Fire Prediction: Case Study of Jijel Region : 3rd cycle LMD Doctorate Degree in Computer Science Specialty: Artificial Intelligence and Its Applications [electronic document] . - جامعة ميلة, 2026 . - 101p. ; PDF + قرص مضغوط.
ISSN : D.N00402
Langue : English (eng) Langue originale : English (eng)
Résumé : Wildfires have intensified across the Mediterranean under compounding heatwaves, drought,
and human pressure, making a shift from reaction to prevention essential. This thesis develops an operational wildfire susceptibility mapping (WSM) framework for Jijel Province
(Algeria) that integrates Geographic Information Systems (GIS) and machine learning. Fourteen conditioning factorstopography (elevation, slope, aspect), climate (minimum, maximum,
and average temperature, humidity, wind speed, precipitation), vegetation (Normalized Difference Vegetation Index, NDVI), and anthropogenic pressure (distance to roads and settlements)are harmonized on a 30,m grid and linked with civil protection fire records, yielding an analysis-ready matrix of approximately 2.6 million samples. Collinearity screening (maximum Variance Inflation Factor, VIF = 7.58; minimum tolerance = 0.11) and light k-means denoising of the Low class improve model stability We train Random Forest (RF), Extreme Gradient Boosting (XGBoost), a shallow Neural Network (NN), and Support Vector Machine (SVM) models with class balancing and probability calibration. On the internal holdout set, RF attains an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.99 and an F1-score of 0.98, with XGBoost and
NN performing similarly, and SVM showing solid performance (AUC = 0.94). Continuous susceptibility scores are calibrated and discretized into five classes (LowVery High). Independent time-forward validation overlays wildfire occurrences from 20242025 (Civil Protection
records and Moderate Resolution Imaging Spectroradiometer, MODIS, data) on the susceptibility maps: 87.73
The outputscalibrated probability surfaces, clearly legended risk classes, and uncertainty layersare suitable for integration into web-based GIS dashboards to support resource prepositioning, fuel-break maintenance at the wildlandurban interface (WUI), and thresholdbased early warning systems. Limitations (sparse meteorological stations and ignition geolocation uncertainty) and targeted improvements (denser in situ sensing and spatially blocked cross-validation) are discussed. The framework is transferable to similar Mediterranean environments.Lien vers la ressource électronique : https://syngeb.univ-mila.dz/fr/opac/result_details/949820 Réservation
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Barcode Call number Media type Location Section Statut d inf1/75 D.N004/02 Thesis and Dissertation Central Library Dissertations and Theses Room AVAILABLE
title : Analysis 4 : Courses and Exercises with Solutions Type de document : electronic document Auteur : Smail, KAOUACHE, Author Editeur : جامعة ميلة Date de publication : 2026 Nombre de pages : 105p. Dimensions : Matériel d'accompagnement : قرص مضغوط ISBN (ou autre code) : pb/210 Langue : English (eng) Langue originale : English (eng) Lien vers la ressource électronique : http://dspace.centre-univ-mila.dz/jspui/handle/123456789/4593 Analysis 4 : Courses and Exercises with Solutions [electronic document] / Smail, KAOUACHE, Author . - جامعة ميلة, 2026 . - 105p. ; PDF + قرص مضغوط.
ISSN : pb/210
Langue : English (eng) Langue originale : English (eng)
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title : Compilation Type de document : electronic document Auteur : Abdelkamel, Hettab, Author Editeur : جامعة ميلة Date de publication : 2025 Nombre de pages : 138p. ISBN (ou autre code) : pb201 Langue : French (fre) Langue originale : French (fre) Lien vers la ressource électronique : http://dspace.centre-univ-mila.dz/jspui/handle/123456789/4571 Compilation [electronic document] / Abdelkamel, Hettab, Author . - جامعة ميلة, 2025 . - 138p.
ISSN : pb201
Langue : French (fre) Langue originale : French (fre)
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title : Cours Matériaux de Construction 2 (MDC 2) : Destiné aux étudiants 3ème licence en Génie Civil Type de document : electronic document Auteur : Tahar, TIOUA, Author Editeur : جامعة ميلة Date de publication : 2025 Nombre de pages : 106p. ISBN (ou autre code) : pb206 Langue : French (fre) Langue originale : French (fre) Lien vers la ressource électronique : http://dspace.centre-univ-mila.dz/jspui/handle/123456789/4585 Cours Matériaux de Construction 2 (MDC 2) : Destiné aux étudiants 3ème licence en Génie Civil [electronic document] / Tahar, TIOUA, Author . - جامعة ميلة, 2025 . - 106p.
ISSN : pb206
Langue : French (fre) Langue originale : French (fre)
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