Papers by Mohamed Ridda LAOUAR
Applied Soft Computing, Nov 1, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Using Semantic Web and Linked Data for Integrating and Publishing Data in Smart Cities
Proceedings of the 7th International Conference on Software Engineering and New Technologies
Web of Data, Semantic Web, and Linked Data offer a set of dataset to exploit it in the different ... more Web of Data, Semantic Web, and Linked Data offer a set of dataset to exploit it in the different domain especially in smart cities. In this paper we illustrate the methodology used to extract data from different datasets, enrich them and publish them under the Linked Open Data paradigm to resolve the problem that exists in cities. The citizen helps public administer to declare the problems and fault. In order to test our methodology, we have chosen the dataset DBpedia among the set of Linked Open Data. Finally the tool SPARQL enables to launch queries to extract, publish and enrich Linked Open Data on the web.
Enhancing Resiliency Feature in Smart Grids through a Deep Learning Based Prediction Model
Recent Advances in Computer Science and Communications
Background: Enhancing the resiliency of electric power grids is becoming a crucial issue due to t... more Background: Enhancing the resiliency of electric power grids is becoming a crucial issue due to the outages that have recently occurred. One solution could be the prediction of imminent failure that is engendered by line contingency or grid disturbances. Therefore, a number of researchers have initiated investigations to generate techniques for predicting outages. However, extended blackouts can still occur due to the frailty of distribution power grids. Objective: This paper implements a proactive prediction model based on deep-belief networks to predict the imminent outages using previous historical blackouts, trigger alarms, and suggest solutions for blackouts. These actions can prevent outages, stop cascading failures and diminish the resulting economic losses. Methods: The proposed model is divided into three phases: A, B and C. The first phase (A) represents the initial segment that collects and extracts data and trains the deep belief network using the collected data. Phase B...
The International Arab Journal of Information Technology
Nowadays, with the developments witnessed by the Internet, algorithms have come to control all as... more Nowadays, with the developments witnessed by the Internet, algorithms have come to control all aspects of digital content. Due to its Arabic roots, it is ironic to find that Arabic Quranic content is still thirsty to benefit from computer linguistics, especially with the advent of artificial intelligence algorithms. The massive spread of Islamic-typed websites and applications has led to a widespread of digital Quranic content. Unfortunately, such content lacks censorship and can rarely match resourcefulness. It is quite difficult, especially for a non-native speaker of the Arabic language, to distinguish and authenticate the provided Quranic verses from the non-Quranic Arabic texts. Text processing techniques classified outside the field of Natural Language Processing (NLP) give less qualified results, especially with Arabic texts. To address this problem, we propose to explore Word Embeddings (WE) with Deep Learning (DL) techniques to identify Quranic verses in Arabic textual cont...
E-Iibraries Decision Support System for E-Resources Selection
Proceedings of the 8th International Conference on Information Systems and Technologies, 2018
E-resources acquisition is the most important task in academic digital libraries e-collection dev... more E-resources acquisition is the most important task in academic digital libraries e-collection development. Due to the augmentation of e-resources and users population, the librarians face many challenges in the selection of e-resources, the main challenge is the determination of users' information needs. As a solution to the above challenge, and in the aim of responding to the following question: what are the e-resources needed by users? Users' queries are studied in the aim of determining the set of databases, journals/periodicals, e-books and other electronic resources for purchase or subscription. In this paper we propose a decision support system based on PROMETHEE II method and revised Simos' procedure for selecting e-resources in order to help academic digital libraries librarians to choose the best e-resources to acquire. At first, users' queries are analyzed in order to extract each user preferences. Then, the revised Simos' procedure is used to derive the criteria weights. Finally, we recalled the PROMETHEE II method in order to rank the e-resources according to each user' preferences.
Urban projects planning by multi-objective ant colony optimization algorithm
Proceedings of the 8th International Conference on Information Systems and Technologies, 2018
The urban projects planning is known as a very complex task. It consists of finding out adequate ... more The urban projects planning is known as a very complex task. It consists of finding out adequate solutions to fit common problems such as inner city decay, overcrowding, and traffic congestion, in order to manage the city in a rational way with sustainable alternatives. The aim is to meet human urban needs, and to reach a high level of efficiency, with the best employment of available resources. Therefore, a set of projects that maximize utility functions is chosen. The evaluation of these functions is built on urban projects criteria with respect to political, financial, ecological and other socioeconomic constraints. This paper aims to present how to deal with urban projects planning, using multi-objective ant colony optimization (MACO) algorithm to fit urban projects to appropriate areas, respecting related constraints.
Quran content representation in NLP
Proceedings of the 10th International Conference on Information Systems and Technologies, 2020
Word representation is a starting point for Natural Language Processing (NLP). These representati... more Word representation is a starting point for Natural Language Processing (NLP). These representations transform words into symbolic vectors of a given length that reveal the hidden linguistic and semantic similarities. This paper presents a study of the various word representation tools used for the content of the texts of the holy Quran in Arabic, which include the two main representation forms: Local representation and Distributed representation, with the aim of using them in different artificial intelligence subsets such as "machine learning" and "deep learning" algorithms that require NLP.
Multi-agent Reinforcement Learning for Urban Projects Planning
Proceedings of the 7th International Conference on Software Engineering and New Technologies, 2018
Actually, planners always need more and more updated plans to satisfy eventual changes. Those pla... more Actually, planners always need more and more updated plans to satisfy eventual changes. Those plans usually bring immediate and even sustainable solutions. According to the nature of problems, and decision-makers yearnings, the allocated time to establish plans is still tight. Therefore, adequate technics and methods are suitable to tackle this problem. Recently, a primer research field called Machine learning (ML), whose technics are based on learning by studying data or by applying known rules to categorize things, to predict outcomes, to identify patterns, or to detect unexpected behaviors. Reinforcement learning (RL) is an active research field of ML, based on learning how to map situations to actions, so as to maximize a numerical reward. By employing RL methods, the aim is to provide better plans for urban projects, wherein they are modeled to form a multi-agents system, acting cooperatively and optimally.
Intelligent Decision Support System for Electric Power Restoration
Proceedings of the 7th International Conference on Software Engineering and New Technologies, 2018
Despite all the technological advancement in the field of power grids, there is still a need to e... more Despite all the technological advancement in the field of power grids, there is still a need to enhance those grids, especially in case of extreme events as being the leading cause of continuous blackouts. The recent severe blackouts have highlighted the prominence of improving the resilience of the electric power grid. There has been a steep interesting in the last couple of years to this issue from the power industry and a number of researchers were motivated to diagnose the issue via attempting to suggest ways to improve the self-healing ability. Nevertheless, issues pertinent to validity and resiliency are still raised. This paper proposes an architecture for intelligent decision support system based on deep learning algorithms that can help operators to decide what to do against blackout. The system can offer decision support in the power restoration process. The system aim to restore power in a quick and effective manner in order to reduce blackout duration as well as the economic losses.
Improving Intelligent Decision Making in Urban Planning
International Journal of Business Analytics, 2021
Generally, decision making in urban planning has progressively become difficult due to the uncert... more Generally, decision making in urban planning has progressively become difficult due to the uncertain, convoluted, and multi-criteria nature of urban issues. Even though there has been a growing interest to this domain, traditional decision support systems are no longer able to effectively support the decision process. This paper aims to elaborate an intelligent decision support system (IDSS) that provides relevant assistance to urban planners in urban projects. This research addresses the use of new techniques that contribute to intelligent decision making: machine learning classifiers, naïve Bayes classifier, and agglomerative clustering. Finally, a prototype is being developed to concretize the proposition.
Diabetes mellitus early stage risk prediction using machine learning algorithms
2021 International Conference on Networking and Advanced Systems (ICNAS), 2021
Diabetic patients are on the rise. Diabetes is one of the most debilitating diseases. Undiagnosed... more Diabetic patients are on the rise. Diabetes is one of the most debilitating diseases. Undiagnosed and untreated diabetes can lead to a number of health issues, including heart disease and stroke. It is necessary for the patient to visit a diagnostic institution and contact a doctor. With the advent of machine learning, this important problem has been overcome. A primary objective of this work is to build a model that can reliably predict a person’s probability of developing diabetes. To detect diabetes at an early stage, six supervised machine learning classification methods and a hybrid model based on the top three findings are employed. UCI’s machine learning repository provides access to the Pima Indians Diabetes Database, which is used in the experiments. All of them are evaluated based on a variety of measures. It is highlighted that the hybrid model which got an accuracy of 90,62% performs better than other state-of-the-art methods.
Decision Support System Architecture for Smart Grid’s Hardening Against Weather Hazard
Weather hazards are nowadays threatening the resiliency of electric power grids, causing a limitl... more Weather hazards are nowadays threatening the resiliency of electric power grids, causing a limitless number of blackouts in different parts of the world. Therefore, predicting the imminent failure resulting from grid disturbances would improve the quality of those grids. Even though some investigations have been conducted by researchers in the field to create new strategies of predicting outages and restoring power, extreme events can still provoke extended blackouts due to the liability of the existing distribution power grid. This paper suggests the enactment of a Decision Support System Architecture that works on resiliency feature hardening against weather hazards.
2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016
The rapid development of semantic Web and exponential growth in the use of the ontology in the fi... more 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 semantic Web for designing a new smart city ontology that is considered as a system of systems. Such ontology is beneficial for both the citizens and the administrators as it allows interoperability among different systems and frameworks.
Towards blockchain-based urban planning
Proceedings of the 9th International Conference on Information Systems and Technologies, 2019
The potential of urban planning to solve environmental problems in general and manage waste, in p... more The potential of urban planning to solve environmental problems in general and manage waste, in particular, is important because the illegal dumping of solid waste is one of the events related to illegal waste treatment activities. The waste management process is very poor, especially when it comes to confirming the correct destination for the delivery of waste. In this work, we are proposing a chain-based approach to waste tracking to enable waste data reporting in a single system. The blockchain is a technology that has already proven itself in the financial sector. It securely records transactions in a large, time-stamped ledger using the proof of work algorithm (PoW), and allows us to write smart contracts on the ethereum network. The application of this technology to the waste sector will enable reliable, transparent and secure recording of all waste movements, thus enabling waste to be traced from source to treatment and disposal. Users will be able to import and export data through a user interface offering different levels of functionality and access. It will also identify and act on illegal wasteful crimes.
Prediction of breakdowns in smart grids
Proceedings of the International Conference on Computing for Engineering and Sciences, 2017
Progressively become more difficult the process of handling breakdowns and blackouts. Due to the ... more Progressively become more difficult the process of handling breakdowns and blackouts. Due to the uncertain, intricate and the nature of electric issues, traditional power grids are no longer convenient. These problems sheds light on the need for technology improvement. Although, in Quite recently years, considerable attention has been paid to the prediction of failures, breakdowns and blackouts in smart grid; it still represent a critical issue. This paper highlight the problem of prediction of breakdowns in smart grids, we addresses the integration of novel techniques that contribute to the prediction process by using machine learning tools (Naive Bayes). Hence, we endeavor to offer a novel approach that may improve the resilience of the smart grid.
Automatic Parameter Tuning of K-Means Algorithm for Document Binarization
Proceedings of the 7th International Conference on Software Engineering and New Technologies, 2018
The document binarization is a primary processing step toward document recognition system. It goa... more The document binarization is a primary processing step toward document recognition system. It goals to separate the foreground from the document background. In this paper, we propose an algorithm for the binarization of document images degraded by using the clustering algorithm K-Means with automatic parameter tuning. It uses the K-Means algorithm to classify the document image into three classes as background, foreground and noise labels. Experimental results show that our method is more robust to the state of the art on recent benchmarks on the H-DIBCO 2016 dataset.
A Multi-Agent System for Ubiquitous Learning for Hospital Education
International Journal of Information Systems in the Service Sector, 2021
Healthcare information systems (HIS) have become an important area of research due to ever-increa... more Healthcare information systems (HIS) have become an important area of research due to ever-increasing healthcare costs to the national economy. Many recent technological developments such as mobile technology and cloud computing have profoundly affected the current state of HIS and further facilitated the developments of ubiquitous computing and ubiquitous learning systems (ULS). The authors propose a multi-agent system for ubiquitous learning (MASUL) to facilitate various learning tasks. They use JADE (Java agent development framework) for developing the multi-agent system. MASUL provides a series of functionalities that can be used by the patients and clinicians. The system simplifies the mechanisms to access learning information via mobile devices, and it also facilitates the learning-teaching process centered on the physical surroundings of the user.
A multi-criteria process to resolve conflict in the composition of aspectual requirements
Human Systems Management, 2014
The requirements composition is an important activity in Aspect-Oriented Requirements Engineering... more The requirements composition is an important activity in Aspect-Oriented Requirements Engineering (AORE). This activity is used to define functionalities of the future system. It consists of the composition of aspectual requirements with base requirements. This composition may raise conflicting situations between aspectual requirements. However, a conflict appear when two or more aspectual requirements (aspect) that contribute negatively to each other and have the same degree of importance must to be composed together to define functionalities of the future system. This conflict need to be identified and resolved. In this paper, we propose a multi-criteria process to resolve conflicts in the context of AORE. This process use fuzzy measure and Choquet integral to take into account the interaction between these criteria when they are not mutually independent.
International Journal of Information Systems and Social Change, 2019
In recent years, academic digital libraries have become a very important source of information. A... more In recent years, academic digital libraries have become a very important source of information. Academic digital libraries provide a rich collection in order to satisfy user need for information. The augmentation of user population and the volume of new publications causes many challenges to librarians in the collection development process and determining user needs of information is the fundamental challenge that librarians face. This article presents a demand-driven collection development decision support system based on the PROMETHEE II method. The DSS supports the librarians to make decisions in the collection development process to provide a rich collection that meets the users' needs. The DSS evaluates and determines a set of electronic resources for purchase, subscription, contract reviewing or cancelation. The decision support system extracts users' queries from log files to determine user preferences. Then, the revised Simos' procedure is used to derive the crit...
International Journal of Computer Applications in Technology, 2018
Mobile Cloud Computing (MCC) has gained a significant attention these past years. MCC consists of... more Mobile Cloud Computing (MCC) has gained a significant attention these past years. MCC consists of migrating mobile applications from the constrained mobile devices to the cloud. This task is highly complicated and demanding, therefore several novel methods, tools, and approaches are introduced to tackle this complexity. At this point, we argue that a simulation of the deployment mechanisms for accessing cloud services and testing mobile cloud applications in the cloud environment is a mandatory phase prior to real deployment in a real environment. A simulation will offer the developer a controllable and cost-free environment to test and evaluate applications' performance according to different predefined scenarios. As MCC lacks tools of simulation of its aspects, to fill this gap we propose Mobile Cloud Simulation (MC-Sim) toolkits based on CloudSim.
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Papers by Mohamed Ridda LAOUAR