GIS software selection: a multi criteria decision making approach

Building a new GIS project is a major investment. Choosing the right GIS software package is critical to the success and failure of such investment. The problem of selecting the most appropriate GIS software package for a particular GIS project is a multi criteria decision making (MCDM) problem. Solving this problem requires consideration of a comprehensive set of factors and balancing of multiple objectives in determining the suitability of particular software for building a defined GIS application. In this paper a MCDM technique, analytic hierarchy process (AHP), is used to assist system developers to select the most appropriate GIS software for a specific application. An AHP decision model is formulated and applied to a hypothetical case study to examine its feasibility in solving GIS software selection problem. The use of the proposed model indicates that it can be applied to improve the decision making process and to reduce the time taken to select a GIS software

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

The next generation 6G communication network is typically characterized by the full connectivity and coverage of Users Equipment (UEs). This leads to the need for moving beyond the traditional two-dimensional (2D) coverage service to the three-dimensional (3D) full-service one. The 6G 3D architecture leverages different types of non-terrestrial or aerial nodes that can act as mobile Base Stations (BSs) such as Unmanned Aerial Vehicles (UAVs), Low Altitude Platforms (LAPs), High-Altitude Platform Stations (HAPSs), or even Low Earth Orbit (LEO) satellites. Moreover, aided technologies have been added to the 6G architecture to dynamically increase its coverage efficiency such as the Reconfigurable Intelligent Surfaces (RIS). In this paper, an enhanced Computational Intelligence (CI) algorithm is introduced for optimizing the coverage of UAV-BSs with respect to their location from RIS in the 3D space of 6G architecture. The regarded problem is formulated as a constrained 3D coverage optimization problem. In order to increase the convergence of the proposed algorithm, it is hybridized with a crossover operator. For the validation of the proposed method, it is tested on different scenarios with large-scale coordinates and compared with many recent and hybrid CI algorithms, as Slime Mould Algorithm (SMA), Lévy Flight Distribution (LFD), hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and hybrid Grey Wolf Optimizer and Cuckoo Search (GWOCS). The experiment and the statistical analysis show the significant efficiency of the proposed algorithm in achieving complete coverage with a lower number of UAV-BSs and without constraints violation. </p

Green Communication for Sixth-Generation Intent-Based Networks:An Architecture Based on Hybrid Computational Intelligence Algorithm

The sixth-generation (6G) is envisioned as a pivotal technology that will support the ubiquitous seamless connectivity of substantial networks. The main advantage of 6G technology is leveraging Artificial Intelligence (AI) techniques for handling its interoperable functions. The pairing of 6G networks and AI creates new needs for infrastructure, data preparation, and governance. Thus, Intent-Based Network (IBN) architecture is a key infrastructure for 6G technology. Usually, these networks are formed of several clusters for data gathering from various heterogeneities in devices. Therefore, an important problem is to find the minimum transmission power for each node in the network clusters. This paper presents hybridization between two Computational Intelligence (CI) algorithms called the Marine Predator Algorithm and the Generalized Normal Distribution Optimization (MPGND). The proposed algorithm is applied to save power consumption which is an important problem in sustainable green 6G-IBN. MPGND is compared with several recently proposed algorithms, including Augmented Grey Wolf Optimizer (AGWO), Sine Tree-Seed Algorithm (STSA), Archimedes Optimization Algorithm (AOA), and Student Psychology-Based Optimization (SPBO). The experimental results with the statistical analysis demonstrate the merits and highly competitive performance of the proposed algorithm

Stakeholder engagement to evaluate tourist development plans with a sustainable approach

[EN] This study provides an evaluation of tourist development plans in the city of Cartagena de Indias (Colombia). Different stakeholders are involved in the search for solutions to this problem. The proposal is based on a model that combines two techniques, namely the analytic network process (ANP) and social network analysis (SNA). SNA is used to assess the relationships among stakeholders by identifying those who are most relevant and ANP is used to aggregate their opinions and evaluate tourist development plans of Cartagena to improve tourist experiences in a participatory way. The results suggest that the combination of SNA and ANP is a novel and suitable tool for strategic planning of a city.Bolivar Gana con CienciaGonzalez-Urango, H.; García-Melón, M. (2018). Stakeholder engagement to evaluate tourist development plans with a sustainable approach. Sustainable Development. 26(6):800-811. https://doi.org/10.1002/sd.1849S80081126

Leveraging Deep Learning and SNA approaches for Smart City Policing in the Developing World

Is it possible to identify crime suspects by their mobile phone call records? Can the spatial-temporal movements of individuals linked to convicted criminals help to identify those who facilitate crime? Might we leverage the usage of mobile phones, such as incoming and outgoing call numbers, coordinates, call duration and frequency of calls, in a specific time window on either side of a crime to provide a focus for the location and period under investigation? Might the call data records of convicted criminals' social networks serve to distinguish criminals from non-criminals? To address these questions, we used heterogeneous call data records dataset by tapping into the power of social network analysis and the advancements in graph convolutional networks. In collaboration with the Punjab Police and Punjab Information Technology Board, these techniques were useful in identifying convicted individuals. The approaches employed are useful in identifying crime suspects and facilitators to support smart policing in the fight against the country's increasing crime rates. Last but not least, the applied methods are highly desirable to complement high-cost video-based smart city surveillance platforms in developing countries

Abundance and morphometry changes across the high-mountain lake-size gradient in the tropical Andes of Southern Ecuador

The number, size, and shape of lakes are key determinants of the ecological functionality of a lake district. The lake area scaling relationships with lake number and volume enable upscaling biogeochemical processes and spatially considering organisms' metapopulation dynamics. These relationships vary regionally depending on the geomorphological context, particularly in the range of lake area 104 m2 and 50% of the water resources are held in a few ones (∼10) deeper than 18 m. Therefore, midlakes and large lakes are by far more biogeochemically relevant than ponds and shallow lakes in this tropical mountain lake district