Abdelaziz El Fazziki

A Service Oriented Information System: A Model Driven Approach

Generally the information system development is designing the systems with the use of visual design tools and takes the target modelling artefact as a basis to manually define the software application code. The process of mapping business requirements to the application code involves several parts that make the whole development process again very sensitive to errors. In this paper, we present a model-driven approach to overcome the gap between business requirements on one hand and service oriented architecture on the other. For this, we focus on the development of service oriented information system (SOIS) and a set of model transformation rules. Different ways of transforming a model into another exist. The choice of a target model differs according to quality criteria and is determined on the basis of specific requirements. The development process proposed is based on a BPMN meta-model, SOAML meta-model and a component meta-model. The approach leading elements are: the meta-modelling and automated mapping rules. Finally, we illustrate our proposal with a case study.

A Multi-Agent Based Simulation Model for Rail–Rail Transshipment: An Engineering Approach for Gantry Crane Scheduling

Le Havre Port Authority is putting into service a multimodal hub terminal with massified hinterland links (trains and barges) in order to restrict the intensive use of roads, to achieve a more attractive massification share of hinterland transportation and to provide a river connection to its maritime terminals that do not currently have one. This paper focuses on the rail–rail transshipment yard of this new terminal. In the current organizational policy, this yard is divided into two equal operating areas, and, in each one, a crane is placed, and it is equipped with reach stackers to enable container moves across both operating areas. However, this policy causes poor scheduling of crane moves, because it gives rise to many crane interference situations. For the sake of minimizing the occurrence of these undesirable situations, this paper proposes a multi-agent simulation model including an improved strategy for crane scheduling. This strategy is inspired by the ant colony approach and it is governed by a new configuration for the rail yard’s working area that eliminates the use of reach stackers. The proposed simulation model is based on two planner agents, to each of which a time-horizon planning is assigned. The simulation results show that the model developed here is very successful in significantly reducing unproductive times and moves (undesirable situations), and it outperforms other existing simulation models based on the current organizational policy.

An Agent-Oriented Information System: A Model Driven Approach

To face the challenges of rapid enterprises environment change, enterprises need agile information systems that are flexible, reactive and adaptive. The process of mapping business requirements to the system functionalities involves several constraints that make the whole development process again very susceptible to errors. In this paper, we present a model-driven approach combined with software agent to develop an agile information system. In this work, we focus on the development of multi-agent systems (MAS) and a set of model transformation rules. Different ways of transforming a model into another exist. The choice of a target model differs according to quality criteria and is determined on the basis of specific requirements. The development process proposed is based separate aspects of systems, a BPMN metamodel, AML agent meta-model and a JADEX meta-model and the automated transformation rules with ATL language. The approach leading elements are: the meta-modeling, and mapping rules. Finally, we illustrate our proposals with a case study. General Terms Information system development, Model Driven Architecture, multiagent systems, business process modeling

An Agent Based Traffic Regulation System for the Roadside Air Quality Control

This paper describes an on-road air quality monitoring and control approach by proposing an agent-based system for modeling the urban road network infrastructure, establishing the real-time and predicted air pollution indexes in different road segments and generating recommendations and regulation proposals for road users. This can help by reducing vehicle emissions in the most polluted road sections, optimizing the pollution levels while maximizing the vehicle flow. For this, we use data sets gathered from a set of air quality monitoring stations, embedded low-cost e-participatory pollution sensors, contextual data, and the road network available data. These data are used in the air quality indexes calculation and then the generation of a dynamic traffic network. This network is represented by a weighted graph in which the edges weights evolve according to the pollution indexes. In this paper, we propose to combine the benefits of agent technology with both machine learning and big data tools. An artificial neural networks model and the Dijkstra algorithm are used for air quality prediction and the least polluted path finding in the road network. All data processing tasks are performed over a Hadoop-based framework: HBase and MapReduce.

An Image Processing Based Framework Using Crowdsourcing for a Successful Suspect Investigation

The invasion of new technologies in people’s life has allowed a great interactive collaboration between citizens and law enforcement agencies. The appearance of crowdsourcing has become a new source of research and development especially in the suspect investigation domain that needs the combination of human intelligence and the technical tools to lead the investigation towards the greatest results. The objective of this paper is to exploit the pervasiveness of image processing techniques (face detection and recognition) to design a crowdsourcing framework that may be chiefly used by government authorities to identify a suspect. This framework is primarily based on the surveillance video analysis and the sketch generation tools supported by the intelligence of the crowd.

Towards an agent based traffic regulation and recommendation system for the on-road air quality control

This paper presents an integrated and adaptive problem-solving approach to control the on-road air quality by modeling the road infrastructure, managing traffic based on pollution level and generating recommendations for road users. The aim is to reduce vehicle emissions in the most polluted road segments and optimizing the pollution levels. For this we propose the use of historical and real time pollution records and contextual data to calculate the air quality index on road networks and generate recommendations for reassigning traffic flow in order to improve the on-road air quality. The resulting air quality indexes are used in the system’s traffic network generation, which the cartography is represented by a weighted graph. The weights evolve according to the pollution indexes and path properties and the graph is therefore dynamic. Furthermore, the systems use the available pollution data and meteorological records in order to predict the on-road pollutant levels by using an artificial neural network based prediction model. The proposed approach combines the benefits of multi-agent systems, Big data technology, machine learning tools and the available data sources. For the shortest path searching in the road network, we use the Dijkstra algorithm over Hadoop MapReduce framework. The use Hadoop framework in the data retrieve and analysis process has significantly improved the performance of the proposed system. Also, the agent technology allowed proposing a suitable solution in terms of robustness and agility.

Integration of non-functional requirements in a service-oriented and model-driven approach

To face the problems of scalability and complexity of information systems (IS), conceptual models must be able to understand the requirements needed for its development. Beyond the consideration of functional requirements, other more critical requirements have emerged: Non-functional requirements to reflect complex situations that occur in the real world. In this work, we introduce an approach for the integration of non-functional requirements in the conception of information systems. The proposed approach is an approach based on service-oriented architectures (SOA), model driven architecture (MDA), and automatic transformations of models.

Object detection based on HOG features: Faces and dual-eyes augmented reality

Histogram of oriented gradients have been widely used for classification, face detection and recognition. In this paper we present a virtual eye glasses try-on system based on augmented reality and HOG features for face and eyes detection. Machine learning algorithms are used for real time eyes tracking, the resulting face and eyes positions are continuously utilized to overlay the glasses image over the face. The system helps evaluating glasses before trying them in the store and makes possible the design of its own style.

A Gamification and Objectivity Based Approach to Improve Users Motivation in Mobile Crowd Sensing

The advent of new communication and information technologies offers great potential for capturing and transmitting information related to mobility. The use of these technologies makes it possible to collect information and transmit it in a participative production (crowdsourcing) perspective for organizational government services such as suspect investigation. The objective of this work is to improve the process of identifying suspects by combining collective intelligence with mobile devices. To do this, this article proposes an approach for the development of a framework based on the gathering of information by the crowd (crowd sensing), their filtering and their analysis. This framework increases the user participation by integrating the gamification technique as a motivation approach. The reliability of the crowd sensed information, in turn, is provided by an objectivity analysis algorithm. The experimental results of the case study, carried out through AnyLogic simulations, show that the methods and technologies incorporated in the suspect identification procedures accelerated the search and location process by ensuring high system performance as well as by improving the quality of the sensed data.

Towards a pervasive information system for patient triage and referral in emergency departments

Every day, many people come to emergency departments; the orientation and placement of these patients in a waiting list according to the seriousness of their health conditions is an important task that requires a lot of skilled human resources and time. In addition, some people abuse the system by seeking care for minor problems, which significantly increases the emergency department overcrowding. To facilitate and accelerate the process of triage and referral in emergency departments, we propose to create a pervasive environment with a set of sensors. The information system (IS) of this space must exactly determine the patients state of health and whether he or she must be presented urgently to a specialist, or may be queued according to a given order of priorities; in some cases, he or she must be referred to specialised emergency (maternity, psychiatry, cardiology, etc.). In this document, we present the architecture, implementation and simulation of our system for triage and referral in emergency departments.