There are several areas in which organisations can adopt technologies that will support decision-making: artificial intelligence is one of the most innovative technologies that is widely used to assist organisations in business strategies, organisational aspects and people management. In recent years, attention has increasingly been paid to human resources (HR), since worker quality and skills represent a growth factor and a real competitive advantage for companies. After having been introduced to sales and marketing departments, artificial intelligence is also starting to guide employee-related decisions within HR management. The purpose is to support decisions that are based not on subjective aspects but on objective data analysis. The goal of this work is to analyse how objective factors influence employee attrition, in order to identify the main causes that contribute to a worker’s decision to leave a company, and to be able to predict whether a particular employee will leave the company. After the training, the obtained model for the prediction of employees’ attrition is tested on a real dataset provided by IBM analytics, which includes 35 features and about 1500 samples. Results are expressed in terms of classical metrics and the algorithm that produced the best results for the available dataset is the Gaussian Naïve Bayes classifier. It reveals the best recall rate (0.54), since it measures the ability of a classifier to find all the positive instances and achieves an overall false negative rate equal to 4.5% of the total observations.

Typical mountain Search and Rescue (SaR) operations require the localization of the persons involved in accidents in harsh environments. ARVA and RECCO are the current standards for the localization in snowy environments although their radio range is limited to some tens of meters. In this paper, we prove by experimental results that the Long Range (LoRa) Low-Power Wide-Area Network (LPWAN) technology is very promising for SaR applications due to its extended radio range. A LoRa-based system for SaR operations is presented and analyzed. The localization of the persons is obtained through an algorithm based on path loss measurements. Radio path loss models of body-worn LoRa devices in harsh mountain environments are derived by measurements. We observed that, although the communication range of LoRa decreases from kilometres to hundreds of meters in the tested environments, at least 50% of the transmitted packets can be received at distances about five times greater than those achievable with golden standard technologies such as ARVA. The performances of the considered localization algorithm are analyzed on the basis of the collected data. The achievable accuracy is in the order of meters around the true position for a relatively large number of available path loss measurements. Lastly, we propose and detail a LoRa-based system for SaR operations. .

WiMAX paves the way for wireless DSL to play a significant role in the broadband wireless access market, especially for rural areas with low population density. Conventional cellular planning methods can be used for point-to-multipoint network design. As an alternative, the Fractional Frequency Reuse (FFR) planning strategy has been recently proposed for cellular systems based on the OFDMA/OFDM radio interface (e.g., WiMAX). In this article we analyze the FFR scheme in rural areas evaluating the increase of the overall system capacity. FFR performances are reported in terms of the average number of bits that can be transmitted per symbol in the area. Finally, comparisons with classical frequency reuse planning are analyzed taking into consideration rural environment characteristics. We show that the FFR scheme can provide extra capacity, slightly penalizing the users at the cell edge.

The telecommunications systems are in continuous evolution. After voice, video, mobile internet, and Internet of Things, what services will be supported in the near future? In the paper, three envisioned services are highlighted, which will be provided in the coming years by new telecommunication systems: immersive communications, everything connected, and high-positioning. The author provides a comprehensive description of their characteristics and investigates the developments that will be implemented in 3GPP Releases 17, Release 18, and Release 19, including technologies that could be integrated for supporting the three new services. In order to evaluate the performance of the new technologies and services, it is important to define appropriate Key Performance Indicators (KPIs). The paper reports and proposes new KPIs for network evaluation to support specific new services such as virtual/mixed reality, smart sensors, and gesture recognition, then facilitating the effective design of the next-generation network and its performance assessment optimally. Requirements of the major application fields that will see widespread adoption in the next 3–8 years due to these developments are also investigated. Finally, the paper further outlines the most promising enabling technologies, supporting the three bearer services.

Classical single carrier cell planning is extended to multi-carrier systems operating over frequency selective channels. Extension is achieved by expressing the link outage probability in terms of the statistics of the effective SINR. Two approximations for the link outage probability are obtained by considering log-normal and Gaussian assumptions for the derivation of the statistics of the exponential effective SINR. A simplified planning procedure is presented and applied to the calculation of the network parameters for the typical two cells interference scenario. Results having accuracy within one dB are obtained for a number of sub-carriers greater than six and for an outage target of 5%.

Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.