Recommender systems (RSs) consist of predicting missing ratings based on the observed ones. This problem corresponds to matrix completion where users are its rows and items are its columns and it contains the observed ratings. An efficient RS is the one promoting the personal relevancy of its users which is not the case in the matrix completion process. It takes into account all the rates for prediction without including the users’ and items’ characteristics. Patterns are the key enablers of such solutions. In this work, we present a three-mode tensor representation with two aspects namely user–item interactions (observed ratings) and item–item relationship (detected similarities). To capture the similarities, the patterns are grouped in an equivalent manner using a bi-quantum clustering process (Quantum K-means). This step is adopted to consider only the relevant observed ratings in the prediction process and express item-to-item relationship using fidelity distance. Then, for each missing rating r that a user u might give to an item i in the future, a sub-tensor is created according to the detected patterns. This sub-tensor then is completed by minimizing its rank. This problem is NP-hard, hence a surrogate is used which is the nuclear norm. The effectiveness of the proposed approach is measured according to information retrieval evaluation criteria: precision, recall and [Formula: see text]-measure. The proposed approach improved the precision of the state-of-the-art methods by 20[Formula: see text].

Abstract Faster than classical algorithms, quantum algorithms benefit from the superposition property of quantum information to offer significant speedup to complex algorithms. Therefore, quantum computing can be used to help machine learning algorithms by boosting their performance and accelerate the processing of time‐consuming ones. Clustering algorithms are very complex unsupervised learning algorithms. Indeed, the similarity calculation (distance) between input vectors is a resource‐consuming step, especially when working with large datasets. In this article, we propose a new better‐performing recommender system that operates as a combination of an adapted quantum K‐means algorithm and the singular value decomposition (SVT) algorithm. We integrate the developed quantum clustering algorithm to a prediction process of the proposed recommender system using matrix completion. To the best of our knowledge, no system with such details was proposed in the literature. The system was applied on the MovieLens dataset without a dimensionality reduction step and evaluated according to measures of information retrieval systems. The results of the quantum K‐means algorithm show that the quantum version leads to a logarithmic reduction of the time complexity compared to the classical algorithm. The proposed system has proved to be better than the previous tested ones in terms of precision and recall.

In recent years, dictionary learning has shown to be an efficient tool in recovering images from their degraded, damaged or incomplete version. Especially, for medical images that contain significant details and characteristics. In this work, the authors are interested in this unsupervised learning technique for discovering and visualising the underlying structure of a medical image. Therefore, an adaptive bi‐dictionary learning model for recovering magnetic resonance (MR) image from undersampled measurements is introduced. The proposed model learns two dictionaries, one over the underlying image and the other over its sparse gradient. Hence, the algorithm minimises a linear combination of three terms corresponding to the least‐squares data fitting, dictionary learning over the pixel domain, and gradient‐based dictionary. Numerically, experimental results on several MR images demonstrate that the proposed bi‐dictionary framework can improve reconstruction accuracy over other methods.

Rapid progress in technologies has led to the development of small sensor nodes. A wireless sensor network (WSN) is an interconnected collection of a large number of these small sensor nodes that is used to monitor and record the physical environment. WSNs have applications in diverse scenarios. They play an important role in tracking and monitoring in different domains, such as environmental research, military, and health care. In most of these applications, the WSN is composed of a large number of nodes deployed in an area of interest, and not all nodes are directly connected to the base station (BS). In some cases, batteries of nodes cannot be recharged or changed. For that, the most solution required to overcome these problems is to optimize energy consumed during communication. Data transmission in networks is maintained by routing protocols, which are responsible for discovering the required paths. This paper presents an improvement of the Geographic Adaptive Fidelity (GAF) routing protocol created on a smart actives node selection. The routing process works on cooperative agents communication where another node is activated in the same grid if the data collected are considered as important data, and a heuristic method is used to find an optimal path in terms of energy to transmit data collected until reaching the BS. Simulation results prove that the cooperative agents GAF (CAGAF) routing protocol proposed is more efficient compared to the basic version in terms of considering important data, energy consumed, and dead nodes.

Here, the authors propose a spatially weighted super‐resolution (SR) algorithm, which takes into consideration the distribution of every information that characterise different image areas. The authors investigate to use a combined spatially weighted regularisation of the bilateral total variation and a second‐order term increasing then the robustness of the proposed SR approach with respect to blur and noise degradations. In addition, the authors propose an iterative Bregman iteration algorithm to resolve the obtained optimisation SR problem. As a result, this regularisation is more efficient and easier to implement; moreover, it preserves well the smooth regions of the image and also sharp edges. Using different simulated and real tests, the authors prove the efficiency of the proposed algorithm compared to some SR methods.

Systems of data integration using ontologies aim to implement a collaborative environment between sources for sharing data and services to respond a user request for information. Their users’ requests are an exact expression of their needs. However, the multiplicity of data sources, their scalability and the increasing difficulty to control their descriptions and their contents are the reasons behind the implacability of this assumption today. The users now may not know the data sources they questioned, nor their description or content. Consequently, their queries reflect no more a need that must be satisfied but an intention that must be refined according to data sources available at the time of interrogation. In this work, we present a semantic-based approach to enrich user’ queries expressed in SPARQL Language by his preferences in order to adapt the returned results and make them more precise and more relevant. The proposed approach is applied on a movies management system based on the standard MovieLens dataset. The obtained results are compared to existing approaches according to precision and recall measures. Our approach improved the precision with 26% and the recall with 7% comparing to those of previous study using collaborative filtering.

Spatiotemporal data mining studies the field of discovering interesting patterns from large spatiotemporal databases. Although these databases generate a huge volume of data daily from satellite images and mobile sensors like GPS, among these data we find first spatiotemporal and geographical data; secondly, the trajectories browsed by moving objects in some time intervals. Combination of these types of data leads to producing semantic trajectory data. Enriching trajectories with semantic geographical information leads to ease queries, analysis, and mining, in order to give more meaning to behaviors potentially extracted from trajectories. Therefore, applying mining techniques on semantic trajectories continue to prove to be a success story in discovering useful and nontrivial behavioral patterns of moving objects. The purpose of this paper is to make an overview of spatiotemporal knowledge discovery (STKD) and techniques recently used to extract knowledge from spatiotemporal data based on analysis of recent literature. Then leading towards a deeper analysis about semantic trajectory knowledge discovery as a specified field from STKD that integrates trajectory sample points with geographical data before applying mining techniques in order to extract behavioral knowledge from semantic trajectories which can be more useful and significant for the application users.

The emergence of new applications that need to share information between different sources of information, such as e-learning, e-commerce, e-government, and electronic libraries requires interoperability between information systems. The information systems are designed and developed by different organizations to be autonomous and heterogeneous sources which lead to multiple structuring formats and different interpretations of the same data. So to simplify data access, the virtual integration of heterogeneous, autonomous and distributed data sources is required based on the couple mediator-wrapper and founded on ontologies that provide a consensual terminology. The mediator masks the heterogeneity and distributes the data sources, while the adapter accommodates queries to data sources formats. Information systems exchange data and services thanks to the rich context that the semantic web offers. It aims to provide data understandable by humans and machines that perform automatic processing by software modules following a semantic enrichment. This article is a survey of the state of art that compare the previous works according to several criteria.

The Qur'an is the religious text of Islam, distinguished by its miraculous style, it is considered as the basic reference for all Islamic sciences, and therefore it's very sensitive to model its content for fear to make bad assumptions and axioms. In recent years a number of researches has been done to facilitate the retrieval of knowledge from the Qur'an, but most of the available researches are using human readable data resources and therefore cannot be reused and linked using semantic web technologies, this is why in this project we will adopt an approach that enables humans and computers to understand the Qur'an knowledge throughout the creation of a Qur'anic ontology. The goal of the ontology is to build a computational model capable of representing as much as possible of the concepts mentioned on the Qur'an and the relationships between them using Protégé-OWL. The ontology can be queried using SPARQL queries, we also developed a search engine that will parse user's questions by extracting and stemming the keywords, and then it generates SPARQL queries using the concepts defined by the ontology.