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

Téléchargement : PDF

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

Téléchargement : PDF

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

Téléchargement : PDF

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.

Téléchargement : PDF

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

Téléchargement : PDF

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

Téléchargement : PDF

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

Téléchargement : PDF

This paper describes our participation in the shared task of hate speech detection, which is one of the subtasks of the CERIST NLP Challenge 2022. Our experiments evaluate the performance of six transformer models and their
combination using 2 ensemble approaches. The best results on the training set, in a five-fold cross validation scenario,were obtained by using the ensemble approach based on the majority vote. The evaluation of this approach on the test set resulted in an F1-score of 0.60 and Accuracy of 0.86.

Auteurs : Angel Felipe Magnossão de Paula , Imene Bensalem , Paolo Rosso , Wajdi Zaghouani

Téléchargement : PDF

The present paper is about the participation of our team “techno” at CERIST Natural Language Processing Challenge. We used an available dataset for task1.c: Arabic sentiment analysis and fake news detection within covid-19. It comprises 4128 tweets for sentiment analysis task and 8661 tweets for fake news detection task. We used natural language processing tools with the combination of the most renowned pre-trained language models BERT (Bidirectional Encoder Representations from Transformers). The results shows the efficacy of pre-trained language models as we attained an accuracy of 0.93 for the sentiment analysis task and 0.90 for the fake news detection task.

Auteurs : Rabia Bounaama , Mohammed El Amine Abderrahim

Téléchargement : PDF

The containment measures caused by the coronavirus pandemic have stimulated the use of social networks as a means of exchanging information, communication, and combating social distancing. This paper presents our participation in the NLP Challenge2022 competition initiated by RESEARCH CENTRE FOR SCIENTIFIC AND TECHNICAL INFORMATION (CERIST). The competition focuses on the task of detecting Arabic hate speech and offensive language on social networks, specifically analyzing Twitter messages related to the COVID-19 pandemic and classifying users’ sentiments as either hateful or not. In the present work, we propose a model based on recurrent neural networks, more precisely the Bidirectional long-term memory (Bi-LSTM). We trained the model using a dataset constructed by the authors of this challenge. As a result, we achieves an accuracy of 96.35 %.

Auteurs : Hakim Bouchal , Ahror BELAID

Téléchargement : PDF