Agriculture consumes a significant proportion of water reserves in irrigated areas. Improving irrigation is becoming essential to reduce this high-water consumption by adapting supplies to crop needs and avoiding losses. This global issue has prompted many scientists to think about sustainable solutions using innovative technologies. This study aims to solve the problem of equitable distribution of irrigation water to crops using the PSO (Particle Swarm Optimization) algorithm. The problem involves the allocation of water between N crops, taking into account the priorities of each crop, the minimum and maximum water thresholds required, and the total amount of water available. A precise modeling of the problem is proposed, followed by a description of the PSO algorithm adapted to this situation. The implementation of the algorithm in Python is also presented. The results and performance of the algorithm are discussed, showing its ability to find a fair and efficient solution for irrigation water allocation.

Nowadays, the Internet of Things touches all areas of our daily life, such as industry, economy, energy and agriculture. If we extend these domains to solutions related to smart homes and cars, we will count more than 50 billion connected devices in 2020. These applications transmit a high amount of data on the internet through IoT communication protocols. In some cases, the security aspect is required as the exchanged data can be sensitive. Therefore, it is necessary to develop a means to assess the confidence we can assign to such transmission protocols. In this context, the fault injection characterization mechanism speeds up the fault introduction into a transmission protocol to observe its reaction and to assess its resilience to application conditions with risks of errors occurring. This paper presents a systematic approach to identifying the moment of fault injection in the messaging protocol Message Queuing Telemetry Transport (MQTT). MQTT protocol handles exchanged messages across a distributed system where the injection instant cannot be defined through a time value as the synchronization of the distributed components is not guaranteed. New algorithms are introduced: (1) extract the send/receive messages' pairs, (2) timestamp the communication events using the vector clock, (3) filter the sending events and (4) generate alternate sent messages sequences. Events models for the publisher/broker provided services are generated. These services are: connect, disconnect and publish, obeying some required properties for services' quality.

The efficient management of water resources is a major issue in the field of sustainable development. Several models of solving this problem can be found in the literature, especially in the agricultural sector which represents the main consumer through irrigations. Therefore, Irrigation management is an important and innovative field that has been the subject of several types of research and studies to deal with the different activities, behaviors, and conflicts between the different users. This article presents a methodology for developing an intelligent irrigation system that determines the water requirement of each farm according to the water loss due to the process of evapotranspiration. The water requirement is calculated from data collected from a series of sensors installed in the plantation farm. This project focuses on smart irrigation based on IoT that is effective and can be used by farmer associations whose endowments and irrigation planning are defined according to the need and quantity of water available in the rural municipality.

Microservices have emerged as an alternative solution to many existing technologies allowing to break monolithic applications into “small” fine-grained, highly-cohesive, and loosely-coupled units. However, identifying microservices remains a challenge that could undermine this migration success. This paper proposes an approach for microservices automatic-identification from a set of business processes (BP). The approach is multi-models combining different independent models that represent a BP's control dependencies, data dependencies, semantic dependencies, respectively. the approach is also based on collaborative clustering. A case study about renting bikes is adopted to illustrate and demonstrate the approach. In term of precision, the results show how BPs as inputs permit to generate better microservices compared to other approaches discussed in the paper, as well.

In conjunction with the rapid growth and adoption of social media, people are more and more willing to share their personal experiences and opinions about products and/or services with the community. Opinions could be the basis of developing systems that would advise future users on how to proceed with any purchase without risking any disappointment. Unfortunately, opinions are not always genuine due to for instance, biased users as well as mixed feedback coming from the same users (i.e., multi-identity). This paper presents an approach for opinion analysis using credibility as a decisive criterion for supporting future users make sound decisions. The effectiveness of this approach has been tested using opinions posted on Twitter.

Crowdsourcing consists of tools and methods that harness the potential of the crowd (intelligence and sense of creativity). It is increasingly used in different fields especially in some extreme emergency cases that require broad collaboration of all citizens. This collaboration will be unsuccessful or at least a heavy work without the ubiquity of new information technologies and the emergence of smartphones and mobile devices. They become more and more powerful mainly with the ‘anytime and anywhere’ Internet access. The contribution of this paper is twofold: we introduce a framework that can be widely used by government organizations to stimulate the crowd participation in a missing child case and we also reveal the importance of crowdsourcing in an e-government context to cope with society's changes.

Numerous studies have discussed the benefits of using social networks, even companies started to exploit the usefulness of this valuable information sources. Collecting social data then integrating them into a CRM (Customer Relationship Management) has led companies to understand the customer needs and therefore to improve the development process of their products or their services quality. In this work, we propose a multi-agent framework for analyzing extracted opinions from social media. In the development process, we were brought to consider the huge volumes of data (Big Data) and the response time. To do so, an architecture based on Map/Reduce analysis using Hadoop was made in order to perform the data refinement (classify or remove special words or delete the unvaluable reviews) and sentiment analysis (Sentigem). Finally, a study case using Twitter (Twitter4J API) as a data source, was made to verify the effectiveness of the proposed framework.

The increasing number of people using social media to express their personal experiences has led to an emerging interest in supporting social media analysis for marketing, opinion analysis and understanding community cohesion. Social customer relationship management (SCRM) systems have become an interesting research area. Generally analysis needs to be done on large volumes of data (Big Data) in an efficient and timely manner. In this research, we propose a social intelligence framework that can extract and consolidate the reviews expressed via social media to help enterprises to know more about the customers' opinion toward the target products. This goal can be achieved by analyzing the reviewers' knowledge and authority and their opinion, sentiment (SentiGem) toward the target products, after filtering the extracted tweets from Twitter using Twitter4J. Additionally we present an architecture based on Map/Reduce analysis using Hadoop.

In this paper, we study some feature descriptors and detectors based on invariants type and use the most stable and robust keypoints for an efficient object detection. From many points of view local descriptors are relatively different, the way of their extraction and the type of the invariance, the goal of this paper is to examine existing feature detectors and apply the most appropriate and efficient on our Content-based image retrieval engine (CBIR).

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.