A surprise announcement by then prime minister Tony Blair in 1998 led to the creation of a new senior clinical role - the nurse consultant.

Work in progress: The evolving nurse consultant role has all-round benefits and should be embraced, finds Frances Pickersgill

While the Internet of Things (IoT) is driving a transformation of current society toward a smarter one, new challenges and opportunities have arisen to accommodate the demands of IoT development. Low power wireless devices are, undoubtedly, the most viable solution for diverse IoT use cases. Among such devices, Bluetooth low energy (BLE) beacons have emerged as one of the most promising due to the ubiquity of Bluetooth-compatible devices, such as iPhones and Android smartphones. However, for BLE beacons to continue penetrating the IoT ecosystem in a holistic manner, interdisciplinary research is needed to ensure seamless integration. This paper consolidates the information on the state-of-the-art BLE beacon, from its application and deployment cases, hardware requirements, and casing design to its software and protocol design, and it delivers a timely review of the related research challenges. In particular, the BLE beacon’s cutting-edge applications, the interoperability between packet profiles, the reliability of its signal detection and distance estimation methods, the sustainability of its low energy, and its deployment constraints are discussed to identify research opportunities and directions.

BLE Beacons for Internet of Things Applications: Survey, Challenges, and Opportunities

A systematic multicriteria decision analysis technique is described for contractor selection and bid evaluation based on utility theory and which permits different types of contractor capabilities to be evaluated A UK case study is used to illustrate the technique The theoretical basis and the advantages of the technique are also presented

Contractor selection using multicriteria utility theory: an additive model

This paper develops a novel face recognition technique called Complete Gabor Fisher Classifier (CGFC). Different from existing techniques that use Gabor filters for deriving the Gabor face representation, the proposed approach does not rely solely on Gabor magnitude information but effectively uses features computed based on Gabor phase information as well. It represents one of the few successful attempts found in the literature of combining Gabor magnitude and phase information for robust face recognition. The novelty of the proposed CGFC technique comes from (1) the introduction of a Gabor phase-based face representation and (2) the combination of the recognition technique using the proposed representation with classical Gabor magnitude-based methods into a unified framework. The proposed face recognition framework is assessed in a series of face verification and identification experiments performed on the XM2VTS, Extended YaleB, FERET, and AR databases. The results of the assessment suggest that the proposed technique clearly outperforms state-of-the-art face recognition techniques from the literature and that its performance is almost unaffected by the presence of partial occlusions of the facial area, changes in facial expression, or severe illumination changes.

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The complete gabor-fisher classifier for robust face recognition

A systematic review of IoT in healthcare: Applications, techniques, and trends

The 5th Generation (5G) network design is still under exploration and will require an innovative approach to network monitoring and management with a relentless focus on customer needs. Among the recent technologies, software-defined networking (SDN) and network functions virtualization (NFV), which are already offering network programmability and automation, have been proposed to improve resource management in 5G systems. Furthermore, the enhanced telecom operations map (eTOM) can be used to manage both End-to-End (E2E) service and end-customer experience. In this paper, we illustrate a novel SDN-based architecture for 5G networks that will be able to enhance quality-of-experience (QoE) monitoring and management by integrating SDN and eTOM. This architecture is deployed and evaluated by simulations of a service-level agreement (SLA) verification scenario. As a result, the implemented platform is capable of identifying SLA violation and can successfully enforce suitable mechanisms to dynamically adjust network parameters, thereby satisfying the SLA constraints.

Novel QoE monitoring and management architecture with eTOM for SDN-based 5G networks

With the huge advances in the field of wireless communication technologies and the availability of large volume of data on customers, it becomes imperative to develop a system that can be used in shopping center to make shopping more enjoyable and efficient for the customer and the monitoring real-time easier for the store's management. A Geomarketing system based on Bluetooth Low Energy (BLE) is developed. This BLE-based system consists of three key components (i) Server component (ii) smartphone component, and (iii) beacons (BLE). The smartphone application detects the location of customers by using BLE technology, this data will be transferred to server component by WIFI, and the server application sends personalized promotions such as mobile advertisements or e-coupons to customers' smartphones. In this paper, a highly scalable marketing system using Beacon and the smartphone applicationwas built to send push notifications to users based on the current location of customer inside the mall. Content was provided to people in order to increase sales. This work aims to demonstrate that the geomarketing system is able to detect the location of the customer, transmit the location's data via WIFI, display personalized promotions in a smartphone. The results show that implementation of Bluetooth Low Energy (BLE) technology in this system highly reduces the power consumption of the long-term monitoring system.

Bluetooth Low Energy (BLE) based geomarketing system

— The Chiu’s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. In addition, these rules are not explicit for the expert. In this paper, we develop a method which generates Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps: first, it uses the subtractive clustering principle to estimate both the number of clusters and the initial locations of a cluster centers. Each obtained cluster corresponds to a Mamdani fuzzy rule. Then, it optimizes the fuzzy model parameters by applying a genetic algorithm. This method is illustrated on a traffic network management application. We suggest also a Mamdani fuzzy rules generation method, where the expert wants to classify the output variables in some fuzzy predefined classes. Keywords — Fuzzy entropy, fuzzy inference systems, genetic algorithms, network management, subtractive clustering I. INTRODUCTION LUSTERING is a fundamental method in data mining and pattern recognition areas. Fuzzy clustering allows natural grouping of data in a large data set and provides a basis for constructing rule-based fuzzy model [2], [8], [16]. Chiu developed a fuzzy clustering approach [4], [5], called subtractive clustering, to extract the Takagi–Sugeno fuzzy rules from data, where the rule output is not fuzzy but a linear function of inputs. To optimize these rules, Chiu used the ANFIS (Adaptive Neuro-Fuzzy Inference System) approach [11], [12], [13]. ANFIS uses backpropagation learning to determine premise parameters and least mean square estimation to determine the consequent parameters (to learn the parameters related to membership functions). In this paper, we recall initially the subtractive clustering, then we propose a new approach.

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An Adaptive Fuzzy Clustering Approach for the Network Management

Big Data is at use every single day, from the adoption of Internet through social networks, mobile devices, connected objects, videos, blogs and others. Big Data real-time processing have received a growing attention especially with the expansion of data in volume and complexity. Big data is created every day, from the use of the Internet through social networks, mobile devices, connected objects, videos, blogs and others. In order to ensure a reliable and a fast real-time information processing, powerful tools are essential for the analysis and processing of Big Data. Standards MapReduce frameworks such as Hadoop MapReduce face some limitations for processing real-time data of various formats. In this paper, we highlight the implementation of the de-facto standard Hadoop MapReduce and also the implementation of the framework Apache Spark. Thereafter, we conduct experimental simulations to analyze a real-time data stream using Spark and Hadoop. To further enforce our contribution, we introduce a comparison of the two implementations in terms of architecture and performance with a discussion to feature the results of simulations. The paper discusses also the drawbacks of using Hadoop for real-time processing.

Real-time data analysis using Spark and Hadoop

Summary In this paper, we describe an adaptive system conceived in order to generate pedagogical paths which are adapted to the learner profile and to the current formation pedagogical objective. We have studied the problem as an "Optimization Problem". Using Genetic Algorithms, the system seeks an optimal path starting from the learner profile to the pedagogic objective passing by intermediate courses. To prepare the courses for adaptation, the application creates a descriptive sheet for resources, in XML format, while its integration in the database. Some experiments are added to this paper.

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Mostafa Bellafkih

Papers

Novel QoE monitoring and management architecture with eTOM for SDN-based 5G networks

Bluetooth Low Energy (BLE) based geomarketing system

An Adaptive Fuzzy Clustering Approach for the Network Management

Real-time data analysis using Spark and Hadoop

Adaptive E-learning using Genetic Algorithms