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Despite the fact that agent technologies have widely gained popularity in distributed systems, their potential for advanced management of vehicle traffic has not been sufficiently explored. This paper presents a traffic simulation framework based on agent technology and fuzzy logic. The objective of this framework is to act on the phase layouts represented by its sequences and length to maximize throughput and fluidize traffic at an isolated intersection and for the whole multi-intersection network, through both inter- and intra-intersection collaboration and coordination. The optimizing of signal layouts is done in real time, and it is not only based on local stream factors but also on traffic stream conditions in surrounding intersections. The system profits from agent communication and collaboration as well as coordination features, along with decentralized organization, to decompose the traffic control optimization into subproblems and enable the distributed resolution. Thus, the separate parts can be resolved rapidly by parallel tasking. It also uses fuzzy technology to handle the uncertainty of traffic conditions. An instance of the proposed framework was validated and designed in the ANYLOGIC simulator. Instantiation results and analysis denote that the designed system can significantly develop the efficiency at an individual intersection as well as in the multi-intersection network. It reduces the average travel delay and the time spent in the network compared to multi-agent-based adaptative signal control systems.

Given the growing scarcity and strong demand for water resources, the sustainability of water resource management requires an urgent policy of measures to ensure the rational use of these resources. The heterogeneous properties of groundwater systems are related to the dynamic temporal-spatial patterns that cause great difficulty in quantifying their complex processes, while good regional groundwater level forecasts are completely required for managing water resources to guarantee suitable support of water demands within any area. Water managers and farmers need intelligent groundwater and irrigation planning systems and effective mechanisms to benefit from the scientific and technological revolution, particularly the artificial intelligence engines, to enhance the water support in their water use planning practices. Therefore, this work aims to improve the groundwater level prediction based on the previous measures for better planning of hydraulic resource use. For this concern, the suggested method starts with data-preprocessing using the Principal Component Analysis method. Next, we validated the effectiveness of the hybrid artificial neural network, combined with an extended genetic algorithm for the hyperparameters and weight optimization, in predicting the groundwater levels in a selected monitoring well in California. The evaluation results have demonstrated the performance of the optimized ANN-GA model. HIGHLIGHTS Smart Groundwater Governance Framework.; Pertinent Data Extraction with Principal Component Analysis.; The Artificial Neural Networks’ hyper-parameters optimization.; The Artificial Neural Networks’ weights optimization.; Improving the groundwater levels prediction is primordial to sustainable water management.;

Artificial technologies are rapidly becoming one of the most powerful and popular technologies for solving complicated problems involving distributed systems. Nevertheless, their potential for application to advanced artificial transportation systems has not been sufficiently explored. This paper presents a traffic optimization system based on agent technology and fuzzy logic that aims to manage road traffic, prioritize emergency vehicles, and promote collective modes of transport in smart cities. This approach aims to optimize traffic light control at a signalized intersection by acting on the length and order of traffic light phases in order to favor priority flows and fluidize traffic at an isolated intersection and for the whole multi-intersection network, through both inter- and intra-intersection collaboration and coordination. Regulation and prioritization decisions are made on real-time monitoring through cooperation, communication, and coordination between decentralized agents. The performance of the proposed system is investigated by implementing it in the AnyLogic simulator, using a section of the road network that contains priority links. The results indicate that our system can significantly increase the efficiency of the traffic regulation system.

There is a significantly increasing demand for monitoring systems for elderly people in the health‐care sector. As the aging population increases, patient privacy violations and the cost of elderly assistance have driven the research community toward computer vision and image processing to design and deploy new systems for monitoring the elderly in the authors’ society and turning their living houses into smart environments. By exploiting recent advances and the low cost of three‐dimensional (3D) depth sensors such as Microsoft Kinect, the authors propose a new skeleton‐based approach to describe the spatio‐temporal aspects of a human activity sequence, using the Minkowski and cosine distances between the 3D joints. We trained and validated their approach on the Microsoft MSR 3D Action and MSR Daily Activity 3D datasets using the Extremely Randomised Trees algorithm. The results are very promising, demonstrating that the trained model can be used to build a monitoring system for the elderly using open‐source libraries and a low‐cost depth sensor.

Augmented reality has gained an increasing research interest over the few last years. Customers requirements have become more intense and more demanding, the need of the different industries to re-adapt their products and enhance them by recent advances in the computer vision and more intelligence has become a necessary. In this work we present a marker-less augmented reality application that can be used and expanded in the e-commerce industry. We take benefit of the well known boosting techniques to train and evaluate different face detectors using the multi-block local binary features. The work purpose is to select the more relevant training parameters in order to maximize the classification accuracy. Using the resulted face detector, the position of the face will serve as a marker in the proposed augmented reality.

MOOCs are becoming more and more involved in the pedagogical experimentation of universities whose infrastructure does not respond to the growing mass of learners. These universities aim to complete their initial training with distance learning courses. Unfortunately, the efforts made to succeed in this pedagogical model are facing a dropout rate of enrolled learners reaching 90% in some cases. This makes the coaching, the group formation of learners, and the instructor/learner interaction challenging. It is within this context that this research aims to propose a predictive model allowing to classify the MOOCs learners into three classes: the learners at risk of dropping out, those who are likely to fail and those who are on the road to success. An automatic determination of relevant attributes for analysis, classification, interpretation and prediction from MOOC learners data, will allow instructors to streamline interventions for each class. To meet this purpose, we present an approach based on feature selection methods and ensemble machine learning algorithms. The proposed model was tested on a dataset of over 5,500 learners in two Stanford University MOOCs courses. In order to attest its performance (98.6%), a comparison was carried out based on several performance measures.