Hafed Zarzour

Université Mohamed Chérif Messaadia de Souk-Ahras · Department of Computer Science

Over the last few years, advanced deep learning-based computer vision algorithms are revolutionizing the manufacturing field. Thus, several industry-related hard problems can be solved by training these algorithms, including flaw detection in various materials. Therefore, identifying steel surface defects is considered one of the most important tas...

The digital revolution has an impact on educational systems, which makes a significant shift from traditional education to e-learning. Nowadays, many universities throughout the world use e-learning platforms as part of their learning approach. One of such systems is Massive Open Online Courses (MOOCs), which has seen great success and an increase...

Over the last few years, advanced deep learning-based computer vision algorithms are revolutionizing the manufacturing field. Thus, several industry-related hard problems can be solved by training these algorithms, including flaw detection in various materials. Therefore, identifying steel surface defects is considered one of the most important tas...

Collaborative filtering methods are often utilized in the industry of recommender systems. They work by identifying users with similar tastes and recommending items for each active user. Besides, clustering techniques are extensively utilized to create systems based on collaborative filtering recommendation in the context of big data. Nevertheless,...

The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is base...

In the recent years, recommender systems have begun to attract the attention of many online-based companies. While these systems are being developed to provide users with better recommendations, they suffer from the lack of explain-ability. The explainable recommendation systems are developed to solve the problem of why certain products or services...

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technolog...

Data scientists need to develop accurate and effective tools and techniques to handle a huge amount of data. Therefore, machine learning and deep learning algorithms have come to the life, especially with the impressive advances in both hardware and software fields. Many impressive existing services are helping us in our daily lives, such as Google...

Throughout history, science education has played a vital role in developing and modernizing the countries. The education in Algeria has been developing for the last years as a result of several reforms undertaken for enhancing the quality of learning and teaching in the whole education system, ranging from the primary school to higher education. He...

In the last few years, cluster ensembles have emerged as powerful techniques that integrate multiple clustering methods into recommender systems. Such integration leads to improving the performance, quality and the accuracy of the generated recommendations. This paper proposes a novel recommender system based on a cluster ensemble technique for big...

Wildfire is one of the most critical natural disasters that threaten wildlands and forest resources. Traditional firefighting systems, which are based on ground crew inspection, have several limits and can expose firefighters’ lives to danger. Thus, remote sensing technologies have become one of the most demanded strategies to fight against wildfir...

The different crop diseases are a serious threat resulting in significant yield losses, where their effective monitoring and accurate early identification techniques are considered crucial to ensure stable and reliable crop productivity and food security. The traditional methods often rely on human expert-based inspection of disease symptoms, which...

Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important tasks in a large number of computer vision-based applications. This crucial task needed to be done with high accuracy and speed. However, it is a very challenging task due to many characteristics related to the aerial images and the used hardware, such as diff...

Explainable recommendation systems have gained much attention in the last few years. Most of them use textual reviews to provide users with interpretability about why services or products are liked by users or recommended for them. Sentiment analysis has potential advantages to determine the attitudes of users in online communities using websites s...

With the increased development of technology in healthcare, a huge amount of data is collected from healthcare organizations and stored in distributed medical data centers. In this context, such data quantities, called medical big data, which include different types of digital contents such as text, image, and video, have become an interesting topi...

Recent advances in information filtering have resulted in effective recommender systems that are able to provide online personalized recommendations to millions of users from all over the world. However, most of these systems ignore the explanation purpose while producing recommendations with high-quality results. Moreover, the classification of re...

Enriching e-book systems with some features from social networking sites, such as Facebook, has the potential to affect the learning process for students. In this study, we investigated the behavioral patterns of students learning with a Facebook-based e-book approach. An experimentation was conducted in which data were collected from students’ log...

Due to the growing quantity of information available on the Web, recommender systems have become crucial component for the success of online shopping stores. However, most of the existing recommender systems were only designed to improve the recommendation results and ignore the explainable recommendation aspect. Therefore, in this paper we propose...

Nowadays, many companies through the world wide web like YouTube, Netflix, Aliexpress and Amazon, provide personalized services as recommendations. Recommender systems use the related information about products or services to suggest the most relevant of them to particular users. The recommendation is usually made based on the prediction of the use...

With the advent of Web 2.0, numerous collaborative annotation systems have been developed in an effort to enable distant users to annotate the same multimedia resources such as texts, audio, images, and videos. However, the existing systems do not support the semantic aspect of the data available on the Web and ignore the convergence aspect when ex...

In the last years, the concept of Massive Open Online Course (MOOC) is widely regarded as new, innovative and creative model for free online learning at large-scale participation from the most prestigious universities around the world. On the other hand, the intelligent tutoring systems (ITS) have been developed to support one of the most successfu...

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborati...

The success of applying deep learning to many domains has gained strong interest in developing new revolutionary recommender systems. However, there are little works studying these systems that employ deep learning; additionally, there is no study showing how to combine the users and items embedding with deep learning to enhance the effectiveness o...

Online music diffusion and distribution is becoming increasingly important and commercial and personal databases are increasing in a considerable way. Nowadays, it's necessary to have tools that allow classifying and reaching these bases by carrying out analyzes through musical contents. The work included in this paper consists of automatic predict...

Nowadays, automated evaluation of programs are even more significant where the number of students enrolled in programming courses is growing quickly, making a manual evaluation of programming assignment is a difficult task with very increasing workload for the teachers. This is why automated evaluation of student's programming assignments is highly...

This paper reviews the research articles published in the years between 2006 and 2017 with a focus on using Linked Data technologies for enhancing learning. For this purpose, a survey was conducted and articles were selected according to two main criteria: (1) the paper must be in the technology-enhanced learning domain and published in one of the...

With the increase of volume, velocity, and variety of big data, the traditional collaborative filtering recommendation algorithm, which recommends the items based on the ratings from those like-minded users, becomes more and more inefficient. In this paper, two varieties of algorithms for collaborative filtering recommendation system are proposed....

With the advent and explosive growth of the Web over the past decade, recommender systems have become at the heart of the business strategies of e-commerce and Internet-based companies such as Google, YouTube, Facebook, Netflix, LinkedIn, Amazon, etc. Hence, the collaborative filtering recommendation algorithms are highly valuable and play a vital...

Recently, an increased number of organizations and institutions publish and expose the contents of their data as Linked Data where it becomes possible to share, reuse and link data over geographically-dispersed datasets. However, the existing principles of Linked Data do not suffice when collaboratively editing the same distributed Linked Data stor...

Recently, the Massive Open Online Course (MOOC) has appeared as a new emerging method of online teaching with the advantages of low cost and unlimited participation as well as open access via the web. However, the use of facial emotion detection in MOOCs is still unexplored and challenging. In this paper, we propose a new innovative approach for fa...

In this study, a linked data-based annotation approach is proposed. A learning system has been developed based on the approach by providing an annotating function, a linked data enrichment function, a sharing function and faceted search function. To evaluate the effectiveness of this innovative approach, an experiment was carried out in which two c...

A data model for managing and resolving the problems that exist in cities such as water leak, street faults, broken street lights, and potholes. Classes: Detail, ReportOfFault, Address (label, coordinate), status (started, repairing, finished)

With the emergence of the Web 2.0, collaborative annotation practices have become more mature in the field of learning. In this context, several recent studies have shown the powerful effects of the integration of annotation mechanism in learning process. However, most of these studies provide poor support for semantically structured resources, mor...

In this study, a collaborative annotation approach based on Linked Data technology (CAALDT) is proposed for improving students’ learning. An experiment was conducted to evaluate the effectiveness of the proposed approach by comparing the learning performance of the students who learned with CAALDT and those who learned with the conventional collabo...

The rapid development of semantic Web and exponential growth in the use of the ontology in the field of smart cities, along with World Wide Web, make new and different multi-dimensional character of the smart cities a possibility, in which data is collected from various distributed systems. Consequently, in this paper, we exploit the concept of sem...

A good communication and interaction between citizens and the administration is important and crucial, also can greatly help in improving the quality of urban life of citizen. In this paper, we propose a semantic data model for managing and resolving the problems that exist in cities such as water leak, street faults, broken street lights, and poth...

In distributed collaborative systems for semantic stores editing, multiple users can add, delete and change RDF statements starting from the same replicas and achieving to the same results at the end of the collaborative session. To improve the performance for such systems, the development of an efficient awareness mechanism is very important in or...

In this paper we present CIA-Store, a new conflict-free replicated data type for scalable collaborative annotating distributed image stores based on Open Annotation Collaboration technologies. This approach aims to reduce the limitations of the traditional collaborative image annotation systems by providing a framework that enables multiple users t...

This paper describes a novel approach for developing a new generation of web-based geographical information System which enables to automatically publish on interactive maps the data extracted from DBpedia dataset. This integration can be regarded as the solution that connects the two domains, namely, Geographical Information System (GIS), and Link...

Collaborative video annotation system is groupware system which enables a virtual community of participants to share and annotate the same digital video file from geographically dispersed nodes interconnected via the network. The video annotation process allows participants to browse videos, add, delete or update annotations. However, the existing...

Collaborative image annotation is a useful strategy for assigning a set of labels, or keywords to an image, taking into account its content. While existing collaborative image annotation frameworks facilitate sharing, indexing, and understanding of huge number of images, newly-developed methods let group of users manage dynamically-updating data. C...

As the integration of disaster management, distributed computing, and collaborative technologies, collaborative disaster management systems can overcome the challenges, such as data replication and updating, real-time information dissemination, distributed resources sharing, and collaborative decision-making. Collaborative decision-making systems o...

An important topic within Computer Supported Collaborative Work is collaborative editing or authoring system, which has been an interesting research area by the release of Web 2.0 products including Social Networks, Wikipedia, CMS, Google Docs, Blogs and many, many more. SPARQL/UPDATE is emerging as a reaction to this challenge. However, the curren...

Commutative Replicated Data Type CRDT is a convergence philosophy invented as a new generation of technique that ensures consistency maintenance of replica in collaborative editors without any difficulty over Peer-to-Peer P2P networks. This technique has been successfully applied to different data representation types in scalable collaborative edit...

Nowadays, there are increasing interests in developing methods for synchronizing distributed triple-stores by ensuring eventual data consistency in distributed architecture. The most well-known of them have been designed to serve as a common replicated data type (CRDT), where all concurrent operations commute independently of the centralized contro...

Cet article a pour but de présenter une architectu re générique d'un environnement collaboratif flexible sur le Web offrant des solutions de travail intégré pour la co nstruction interactive d'ontologies partagées à base de GRID qui supporte les deux modes de collaboration : le mode synchrone et asynchrone en s'appuyant sur l'architecture de servic...