SLIM EZZEDINE BEN CHAABANE

Experience and administrative positions

عضو لجنة الجودة والاعتماد الأكاديمي منذ سنة 2016
عضو لجنة البرامج والخطط الدراسية بقسم هندسة الحاسب بجامعة تبوك منذ سنة 2021
عضو لجنة مشاريع التخرج منذ سنة 2020

Qualifications

درجة الدكتـــــوراه: الهندسة الكهربائية سنة 2010 تخصص معالجة الإشارات الرقمية في المدرسة العليا للعلوم والتقنيات بتونس بجامعة تونس بالتعاون مع جامعة جول فارن فرنسا
درجة الماجستيـــــر: الية سنة 2006 في المدرسة العليا للعلوم والتقنيات بتونس بجامعة تونس و بالتعاون مع جامعة جول فارن فرنسا
درجة البكالوريوس في الهندسة الكهربائية الية وإعلامية صناعية 2004 في المدرسة العليا للعلوم والتقنيات بتونس بجامعة تونس

Research interests

معالجة الاشارات والصور الرقمية

التعرف على الاشخاص باستخدام تقنيات متقدمة

معالجة الصور الطبية

جدول عضو هيئة التدريس

الاسم: د. سليم عز الدين بن شعبان
الرتبة العلمية: أستاذ مشارك
الفصل الدراسي: 1447/1448 هـ - الفصل الثاني

الايميل:

s.chaabane@ut.edu.sa

الجوال: 0545540435

المقرر	النشاط	اليوم	الوقت	القاعة	الساعات
الرياضيات الحاسوبية	نظري	الأحد	05:00 م - 07:30 م	15-1-4-1	3
مقدمة في معالجة الإشارات	عملي	الخميس	10:00 ص - 11:40 ص	16-0-4-1	1
مقدمة في معالجة الإشارات	عملي	الاثنين	08:00 ص - 09:40 ص	16-0-4-1	1
مقدمة في معالجة الإشارات	نظري	الأحد	08:00 ص - 09:15 ص	23-1-4-1	3
مقدمة في معالجة الإشارات	نظري	الثلاثاء	08:00 ص - 09:15 ص	23-1-4-1	3
مقدمة في معالجة الإشارات	نظري	الأحد	09:30 ص - 10:45 ص	23-1-4-1	3
مقدمة في معالجة الإشارات	عملي	الخميس	11:00 ص - 12:15 م	23-1-4-1	1

الساعات المكتبية

اليوم	الوقت	المكتب
الأحد	12:30 م - 01:30 م	مكتب عضو هيئة التدريس بغرفة الجودة
الاثنين	10:00 ص -13:00 م	مكتب عضو هيئة التدريس بغرفة الجودة
الثلاثاء	12:30 م - 01:30 م	مكتب عضو هيئة التدريس بغرفة الجودة
الاربعاء	9:00 ص -11:00ص	مكتب عضو هيئة التدريس بغرفة الجودة

A 3D Processing Technique to Detect Lung Tumor

Sun, 1 Jan 2023 - 12:00

DOI

10.14569/ijacsa.2023.0140691

An Indirect Measurement Approach of a Uniform Transformer Winding Inductances Using Frequency Response Analysis Data: An Analytical Approach‏

Thu, 1 Jan 2026 - 12:00

DOI

10.1049/elp2.70155

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion.

Sat, 7 Jan 2023 - 12:00

DOI

10.32604/cmc.2023.032760

Deciphering immune evasion mechanisms in precision oncology: insights from single-cell multi-omics approaches

Thu, 13 Nov 2025 - 12:00

DOI

10.1097/js9.0000000000003911

Development and enhancement of metamaterial-inspired Ag-GaAs THz MIMO antenna with optimized diversity metrics using data-driven machine learning algorithms for future 6G networks‏

Mon, 29 Dec 2025 - 12:00

DOI

10.1038/s41598-025-28978-4

Development of Surveillance Robots Based on Face Recognition Using High-Order Statistical Features and Evidence Theory

Sat, 28 Feb 2026 - 12:00

DOI

10.3390/jimaging12030107

Face recognition based on convolution neural network and scale invariant feature transform

Fri, 1 Jan 2021 - 12:00

DOI

10.14569/ijacsa.2021.0120281

Face recognition based on statistical features and SVM classifier

Sat, 5 Feb 2022 - 12:00

DOI

10.1007/s11042-021-11816-w

Home security system with hybrid face recognition approach using convolutional neural Networks, fuzzy Logic, and SVM classifier

Fri, 6 Feb 2026 - 12:00

DOI

10.1007/s11042-026-21261-2

Iris Recognition System Using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robotic Control

Fri, 8 Nov 2024 - 12:00

DOI

10.3390/jimaging10110288

Optimization of energy consumption and thermal comfort in stratum ventilation systems using metaheuristic-tuned neural networks and generalized normal distribution algorithm

Sun, 1 Feb 2026 - 12:00

DOI

10.1016/j.enbuild.2025.116836

Power quality improvement and energy management in hybrid microgrids using a dual-optimization approach

Thu, 16 Oct 2025 - 12:00

DOI

10.1038/s41598-025-20001-0

Securing Restricted Zones with a Novel Face Recognition Approach Using Face Feature Descriptors and Evidence Theory

Thu, 1 Jan 2026 - 12:00

DOI

10.32604/cmc.2026.072054

Smart agriculture: IoT-Based smart irrigation with advanced fuzzy logic control

Mon, 3 Nov 2025 - 12:00

DOI

10.1016/j.eswa.2025.130168

Lightweight and Energy-Aware Intrusion Detection for Industrial IoT Using TinyML and Edge AI

Mon, 15 Jun 2026 - 12:00

DOI

10.1038/s41598-026-50690-0

Project Name	Project Description
KNN and fuzzy logic for segmentation and classification of cervical cancer from pap smear images	Cervical cancer is a significant public health issue affecting women worldwide By developing accurate methods for segmenting and classifying Pap smear cell images, it can be easily detected the cervical cancer and completely preventable in its early stages This work proposes a new cervical cell segmentation and classification method We will explore how the modified fuzzy c-means algorithm can enhance cervical cell segmentation, and how the K-nearest neighbors (KNN) algorithm can improve cervical cell classification The cervical cell region is isolated from its surroundings using the Gabor Filter and K-mean method On the other hand, the nucleus, cytoplasm, and background are defined instead by classifying the segments obtained from the initial step using the modified fuzzy c-means algorithm Finally, different characteristics of the cervical pap picture are retrieved and normalized using the min-max approach, and then cancer is classified according to its anomaly using the KNN algorithm This specific classifier must determine whether these new images belong to one of the seven classes

Project Name

Project Description

KNN and fuzzy logic for segmentation and classification of cervical cancer from pap smear images

Cervical cancer is a significant public health issue affecting women worldwide By developing accurate methods for segmenting and classifying Pap smear cell images, it can be easily detected the cervical cancer and completely preventable in its early stages This work proposes a new cervical cell segmentation and classification method We will explore how the modified fuzzy c-means algorithm can enhance cervical cell segmentation, and how the K-nearest neighbors (KNN) algorithm can improve cervical cell classification The cervical cell region is isolated from its surroundings using the Gabor Filter and K-mean method On the other hand, the nucleus, cytoplasm, and background are defined instead by classifying the segments obtained from the initial step using the modified fuzzy c-means algorithm Finally, different characteristics of the cervical pap picture are retrieved and normalized using the min-max approach, and then cancer is classified according to its anomaly using the KNN algorithm This specific classifier must determine whether these new images belong to one of the seven classes

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