Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, “Bat-Cluster (BC).” This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values

Forced force directed placement: a new algorithm for large graph visualization

International audienceGraph Visualization is a technique that helps users to easily comprehend connected data (social networks, semantic networks, etc.) based on human perception. With the prevalence of Big Data, these graphs tend to be too large to decipher by the user’s visual abilities alone. One of the leading causes of this problem is when the nodes leave the visualization space. Many attempts have been made to optimize large graph visualization, but they all have limitations. Among these attempts, the most famous one is the Force Directed Placement Algorithm. This algorithm can provide beautiful visualizations for small to medium graphs, but when it comes to larger graphs it fails to keep some independent nodes or even subgraphs inside the visualization space. In this paper, we present an algorithm that we have named "Forced Force Directed Placement". This algorithm provides an enhancement of the classical Force Directed Placement algorithm by proposing a stronger force function. The “FForce”, as we have named it, can bring related nodes closer to each other before reaching an equilibrium position. This helped us gain more display space and that gave us the possibility to visualize larger graphs

Agent intelligent de crawling et de scraping pour le SIE XEW

International audienceCette communication décrit les fonctionnalités de l’agent intelligent XEW-CR (Explore every where) qui vise à répondre à la problématique de trouver l’information utile dans un environnement web, caractérisé par l’abondance et l’hétérogénéité des formats de données et des informations disponibles sur un sujet. La fonctionnalité crawling de l’outil permet de parcourir, d’indexer et de cartographier les pages web en se basant sur le contenu de la page ou de l’URL. Le scraping est moins limitatif car cela consiste à extraire le contenu des pages web pour l’utiliser à des fins de data mining et de stockage de l’information utile dans une base de donnée décisionnelle

Mining unstructured data for a competitive intelligence system XEW

International audienceNowadays, there is a vast amount of information available in line. One of the major unsolved problems in the Competitive Intelligence (CI) is the management of unstructured data. The unstructured data such as multimedia files, documents, comments, customer support request, news, emails, reports and web pages are difficult to capture and store in the common database system. This paper will explained the main process of unstructured data, based on the web services technologies for a Competitive Intelligence System (CIS) XEW. This process could help organization to understand the significance of exploitation and transformation data in supporting decision making process

SDV-XEW : la big data visualisation au service de l'intelligence économique

International audienc

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions

Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix

Conception Agile d'un Système d'Intelligence Economique

International audienc

Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix

Overview of Data Visualization

International audienceData visualization can be defined as data transformation into interactive visual representations. It is very important since it allows users to have an insightful vision on a subject that might interest thems. The Big Data phenomenon has urged scientists to develop and dedicate an entire research field to data visualization since it allows the user to easily have an idea on the content provided by his different data sources, based on his visual abilities. In this paper, we will present an overview of the literature related to this topic starting by its definition then moving to its challenges and later, presenting its methods and comparing some of its most used tools