In this work, we propose a methodology to improve the precision of cell traffic forecasting with a machine learning approach. To develop this methodology, we first performed a systematic analysis in order to reduce bias by selecting the cells with less missing data occurrences. Then, we selected the features and trained a classifier to allocate the cells between predictable and non- predictable, taking into account previous traffic forecast error. The Naive Bayes classifier and Holt-Winters method was selected to perform the proposed methodology in real time. The system was applied to a set of 786 cells in a real network. The classifier presented a 91% accuracy, which leads the predictable cells, using Holt-Winters, to present an average RMSE of 2.74%. This means that it is now possible to implement optimisation algorithms that are highly sensitive to traffic prediction.

This paper addresses the problem of automatic classification of fish species, by using image analysis techniques and artificial immune systems. Unlike most common methodologies, which are based on manual estimations that lead to significant time and financial constraints, we present an automatic framework based on (i) two well-known robust feature extraction techniques: Scale-Invariant Feature Transform and Principal Component Analysis for parameterizing shape, appearance and motion, (ii) two immunological algorithms: Artificial Immune Network and Adaptive Radius Immune Algorithm for clustering individuals of the same species, and (iii) a simple nearest neighbor classification strategy. The framework was successfully validated with images of fish species that have significant economic impact, achieving overall accuracy as high as 92%.

In radio access network planning and optimisation, the estimation and evaluation of cell coverage is crucial. For it, operators use several methodologies, ranging from theoretical propagation models for planning cell coverage, to drive test measurements for evaluating the performance within a cell. A novel propagation model is proposed which combines drive test measurements, cell reach statistics, antenna radiation patterns, terrain morphologies, and classical theoretical propagation models. Considering a realistic scenario of an LTE cell, the use of a classical propagation model such as Walfish- Ikegami results in an average error of -27 dB of estimations with respect to measurements, evidencing a pessimistic estimation of signal. By opposition, the proposed model fits with DT measurements.

Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigated silver nanostars (AgNS) incubated with human plasma, deposited on a simple aluminum foil substrate, and utilizing Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning (ML) to provide a proof-of-concept for rapid differentiation of stroke types. These are the seminal steps for the development of low-cost pre-hospital diagnostics at point-of-care, with potential for improving patient outcomes. The proposed SERS assay aims to classify plasma from stroke patients, differentiating hemorrhagic from ischemic stroke. Silver nanostars were incubated with plasma and spiked with glial fibrillary acidic protein (GFAP), a biomarker elevated in hemorrhagic stroke. SERS spectra were analyzed using ML to distinguish between hemorrhagic and ischemic stroke, mimicked by different concentrations of GFAP. Key innovations include optimized AgNS–plasma incubates formation, controlled plasma-to-AgNS ratios, and a low-cost aluminum foil substrate, enabling results within 15 min. Differential analysis revealed stroke-specific protein profiles, while ML improved classification accuracy through ensemble modeling and feature engineering. The integrated ML model achieved rapid and precise stroke predictions within seconds, demonstrating the assay’s potential for immediate clinical decision-making.

The modelling of real-world data as complex networks is ubiquitous in several scientific fields, for example, in molecular biology, we study gene regulatory networks and protein–protein interaction (PPI)_networks; in neuroscience, we study functional brain networks; and in social science, we analyse social networks. In contrast to theoretical graphs, real-world networks are better modelled as realizations of a random process. Therefore, analyses using methods based on deterministic graphs may be inappropriate. For example, verifying the isomorphism between two graphs is of limited use to decide whether two (or more) real-world networks are generated from the same random process. To overcome this problem, in this article, we introduce a semi-parametric approach similar to the analysis of variance to test the equality of generative models of two or more complex networks. We measure the performance of the proposed statistic using Monte Carlo simulations and illustrate its usefulness by comparing PPI networks of six enteric pathogens.

With the increasing number of mobile subscribers worldwide, there is a need for fast and reliable algorithms for planning/optimization of mobile networks, especially because, in order to maintain a network’s quality of service, an operator might need to deploy more equipment. This paper presents a quick and reliable way to automatically plan a set of frequencies in a cellular network, using both cloud technologies and linear programming. We evaluate our pattern in a realistic scenario of a Global System for Mobile communications protocol (GSM) network and compare the results to another already implemented commercial tool. Results show that even though network quality was similar, our algorithm was twelve times faster and used four times less memory. It was also able to frequency plan seventy cells simultaneously in less than three minutes. This mechanism was successfully integrated in the professional tool Metric, and is currently being used for cellular planning. Its extension for application to 3/4/5G networks is under study.