Accurate and rapid detection of brain tumors is vital for timely diagnosis and treatment planning. This paper proposes an improved computer-aided diagnosis (CAD) system enhanced with artificial intelligence (AI) techniques for automated brain tumor segmentation from magnetic resonance imaging (MRI). The proposed framework consists of three main stages: (1)
image preprocessing using spatial filters and contrast enhancement, (2) segmentation using a deep learning-based thresholding and region proposal network, and (3) post-processing based on morphological operations and adaptive masking. Experimental results on a public dataset show an average accuracy of 97% and a Dice similarity coefficient above 0.86. Compared to traditional segmentation techniques, the integration of AI significantly improves robustness, reduces
false positives, and enhances boundary precision, making this system suitable for clinical implementation.
Auteurs : Abdellatif BOUZID-DAHO
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Ces dernières années, le nombre de documents et de textes complexes a connu une croissance exponentielle, nécessitant une compréhension approfondie des méthodes d’apprentissage automatique pour classer les textes avec précision dans de nombreuses applications. De nombreuses approches d’apprentissage automatique ont obtenu des résultats remarquables dans le domaine du traitement du langage naturel.
La classification de texte consiste à classer automatiquement un ensemble de documents dans plusieurs catégories prédéfinies en fonction de leur contenu et de leur sujet. L’objectif principal de la classification de texte est de permettre aux utilisateurs d’extraire des informations des ressources textuelles et de traiter conjointement des processus tels que la recherche, la classification et les techniques d’apprentissage automatique afin de classifier différentes catégories.
Le succès de ces algorithmes d’apprentissage repose sur leur capacité à comprendre des modèles complexes et des relations non linéaires dans les données. Cependant, identifier des structures, des architectures et des techniques adaptées pour la classification de texte constitue un défi pour les chercheurs.
Dans cet article, un aperçu des algorithmes de classification de texte est présenté. Cet aperçu couvre différentes méthodes
d’extraction des caractéristiques textuelles, des techniques de réduction de la dimensionnalité, des algorithmes et
techniques existants. Enfin, les différentes méthodes de classification de texte par apprentissage profond sont comparées et
résumées.
Auteurs : Hadjer MEZIANI , Sabah KIRAT
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Industrial data integration is the process of gathering, combining, and analyzing data from various sources to create a single, cohesive picture of the data for analysis and decision-making. Many industries, such as manufacturing, energy, transportation, and healthcare, depend on this integration because it helps businesses maximize productivity, cut expenses,and raise quality.
Industrial data can come from various sources, including sensors, machinery, devices, and databases. It can also be of different types, including unstructured, semi-structured, and structured data. Creating a coherent and significant whole out of all these disparate data sources is the challenge of industrial data integration.
Industrial data integration requires the application of expert methodologies and methods. The aim is to build a data infrastructure that can manage massive amounts of data, process it fast and precisely, and deliver real-time insights into industrial process performance.
This paper explains industrial data integration techniques and their challenges. Some case studies reported in the literature draw attention to the insights gained from successful data integration initiatives and use their benefits and best practices.
Auteurs : Lydia Lakhdari , Leila Zemmouchi-Ghomari
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Processes are essential to every organization’s successful and effective operation. They are essential to many facets of an organization and, when well thought out and carried out, provide a host of advantages. In actuality, procedures aid in the accomplishment of the goals of the company, so they must continuously evolve and adapt to the demanding environment of today. Consequently, it is undeniable that numerous researchers have been concerned about their improvement for many years. However, the public administration processes are more constraining than those in the private sector, simply because they must, first of all, comply with their legal basis in addition to being free of structural anomalies.
The purpose of this study is to support process verification throughout the design phase. In fact, we suggest a method that guarantees the analysis of legal conformity of public procedures in a form of process model.
Auteurs : Kaouther MEZAACHE , Latifa MAHDAOUI
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يُحدث التطوّر التكنولوجي اليوم جملة من الإشكاليّات على المستوى الاجتماعي والمهنيّ. فإلى جانب آليّة المهام نتحدث اليوم عن دمقرطة التقنيّة التي تُشكل خطر كبير على مهنة المصمّم الغرافيكي خاصّة مع التطوّر الكبير الذي شهده الذّكاء الاصطناعيّ. ممّا يدفعنا للتساؤل عن مستقبل هذا الاختصاص وعن تأثير هذا التطوّر على التفكير الابداعي لدي المصمّم الغرافيكيّ، وكيف يُغيّر هذا التطور (الذّكاء الاصطناعي والقوالب الجاهزة) عمليّة تصميم الصّورة؟ ولإلجابة عن هذه التساؤلات اعتمدنا في المرحلة الأولى على المقابلات الشبه منظمة لاستجواب مجموعة من المتمرّسين في ميدان التّصميم الغرافيكي وذلك بهدف الكشف عن مدى تفاقم هذه الإشكاليّة والتعرّف على مدى تأثير دمقرطة التقنيّة ودمقرطة تصميم الصّورة والقوالب الجاهزة والذّكاء الاصطناعي على مهنة المصمّم الغرافيكيّ. وفي المرحلة الثانيّة اعتمدنا دراسة مقارنة بين تصميم بالاعتماد على الذّكاء البشري وتصميم بالاعتماد على الذّكاء الاصطناعي وذلك للتعرّف على مدى قدرة الذّكاء الاصطناعي على الابداع والخلق، والتعرّف على مدى خطورة هذه الطّفرة التكنولوجيا على مهنة التّصميم. وكنتيجة لهذا التمشي وجدنا أنّ دمقرطة التقنيّة والصّورة لا تُمكن الإنسان العادي من إعداد تصميم احترافي وذلك لكون التّصميم فكر قبل كلّ شيء. ولتحقيق ذلك يجب على المستخدم أن يكون على دراية ومعرفة كبيرة بهذا المجال. وتتجسد العلاقة بين المصمّم والذّكاء الاصطناعي على شكل شراكة وتعاون، فالذّكاء الاصطناعي هو عبارة عن برمجيّات ذكيّة تعمل على ترجمة التّعليمات المكتوبة إلى محتوى بصري فيرتبط مفهوم الخلق والابداع بمدى إبداع المستخدم وقدرة الآلة على الترجمة البصريّة.
خلاصة القول مهما بلغت التقنيّة من تطوّر تبقى أداة لخدمة التفكير الابداعي للمصمّم
Auteurs : شوريّة نهى
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The issue 27, volume 2 (2023) of the Information Processing at the Digital Age
Journal is a special issue that publishes the papers of the NLP challenge hold at
CERIST on March 29 th , 2023. This challenge was the first of its kind in Algeria and aimed to promote NLP and bring together researchers and students from NLP research teams. Two main tasks were proposed: Opinion mining and Sentiment Analysis, and Information Retrieval. We received 23 papers. 11 of them were selected for participation at the challenge day.
During the organization of this challenge, we noticed a great interest for the first task, especially the subtask 1.c Arabic Sentiment Analysis and Fake News Detection within Covid-19 and the subtask 1.d Arabic Hate Speech and Offensive Language Detection on Social Networks. Hence, this issue contains five papers addressing subtask 1.c, five papers addressing subtask 1.d, and one paper addressing subtask 1.b Multilingual Sentiment Analysis in Twitter.
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In the last decade, social media and internet involvement in people’s life raised new challenges that modern AI needs to deal with. Textual data is generated every time an article is published or an online post is shared or even a simple
comment is made. Among these challenges, we find text classification which is used to identify the general meaning of a set of words using AI methods. This paper presents our participation to the CERIST Natural Language Processing
Challenge, where we proposed a simple yet effective convolutional neural network architecture that can be used for text classification and sentiment analysis. We tested our proposition on 5 different tweets datasets, Hate Speech, Fake News, Arabic Covid Sentiment, Arabic Sentiment, and English Sentiment, and obtained respectively 99,85%, 99,86%, 99,58%, 97,97%, 95,65% accuracy on the training subset and 98,43%, 94,74%, 87,53%, 54,90%, 60,62% accuracy on the validation subset.
Auteurs : Zoubir TALAI , Nada KHERICI
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Arabic language is one of the most popular languages and it is widely used in social media networks. During the pandemics, the spread of fake news, rumors, hate speech and spams increased dramatically which makes the detection of
the misinformation sources very important and very helpful to control the situation. A lot of Arabic natural language processing (ANLP) works are proposed in the literature to solve such problems, in this paper we propose a time efficient and high precision and accuracy algorithm for Arabic Hate speech detection.
A classical Machine Learning (ML) logistic regression algorithm is used in this ANLP work to detect hate speech, the data of this work are collected from Twitter social media during the COVID-19 pandemic, we use 80% of the data to train our algorithm and 20% of data to test it. The proposed algorithm has high accuracy and precision in the tested comments (a precision of 88.77% an accuracy of 98.48%). This work shows that, the classical ML algorithms have good performances in such problems.
Auteurs : Abdelmounim Sellidj
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During Covid-19 pandemic period, people worldwide turned to use social media network to express their opinions and general feelings. Social media platforms like Twitter have become widespread tools for broadcasting and distributing
news and opinions. This paper presents our participation to CERIST Natural Language Processing Challenge, task1.c: Arabic sentiment analysis and fake news detection within covid-19. This complex task is further increased when dealing with dialects that do not have the structure of Modern Standard Arabic (MSA). We introduce an experiment of sentiment analysis of Arabic tweets within covid-19 using machine learning algorithms. The used Arabic dataset was provided by the challenge organizers and it contains 4,128 tweets labeled as Positive, Negative and Neutral for training and 1,034 tweets unlabeled for testing Hadj Ameur & Aliane, 2021. In this experiment the opinions are classified by various machine learning classifiers including Support Vector Machine (SVM), Logistic Regression (LR), Multinomial Naïve Bayes (NB) and K-Nearest Neighbors (KNN). The experimental results indicated that the highest accuracy (94%) was obtained using the Logistic-Regression and SVM among other with training times of 8609s.
Auteurs : Yousra F.G.Elhakeem , Safa Eltayeb, Mohammed Aldawsari , Omer Salih Dawood Omer
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