10 documents found, page 1 of 1

Unraveling the root causes of faults in mobile communicatios: A comparative ana...

The escalating demand and complexity of monitoring services handled by Network Operations Centers (NOCs) have led Mobile Network Operators (MNOs) to prioritize automated solutions for network fault detection and diagnosis. Consequently, various Machine Learning (ML)-based Root Cause Analysis (RCA) systems have been developed, however their lack of explainability poses a challenge due to the predominantly black-...

Imparcialidade administrativa e controle jurisdicional da decisão = Administrat...

Submitted by rcaetano@stj.jus.br (rcaetano@stj.jus.br) on 2022-02-16T19:08:21Z No. of bitstreams: 2 imparcialidade_administrativa_controlo_duarte.pdf: 3071253 bytes, checksum: 00332b5335cf54daf536515a3a915730 (MD5) license.txt: 1239 bytes, checksum: c9b4c351324448672315a00808efb725 (MD5); Approved for entry into archive by betanial@stj.jus.br (betanial@stj.jus.br) on 2022-02-16T22:31:46Z (GMT) No. of bitstreams...

Reflexos procedimentais do princípio da imparcialidade administrativa = Adminis...

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Root cause analysis of low throughput situations using boosting algorithms and ...

Detecting and diagnosing the root cause of failures in mobile networks is an increasingly demanding and time consuming task, given its technological growing complexity. This paper focuses on predicting and diagnosing low User Downlink (DL) Average Throughput situations, using supervised learning and the Tree Shapley Additive Explanations (SHAP) method. To fulfill this objective, Boosting classification models a...

Fault management preventive maintenance approach in mobile networks using seque...

Mobile networks' fault management can take advantage of Machine Learning (ML) algorithms making its maintenance more proactive and preventive. Currently, Network Operations Centers (NOCs) still operate in reactive mode, where the troubleshoot is only performed after the problem identification. The network evolution to a preventive maintenance enables the problem prevention or quick resolution, leading to a grea...

A novel approach for user equipment indoor/outdoor classification in mobile net...

The ability to locate users and estimate traffic in mobile networks is still one of the major challenges when it comes to planning and optimizing the networks. Since indoor location is not always possible or precise, having the ability to distinguish indoor from outdoor traffic can be a valuable alternative and/or improvement. In this paper, two different machine learning algorithms are presented to classify a ...

An enhanced capacity model based on network measurements for a multi-service 3G...

With the ongoing growth on mobile networks utilization, new challenges come up in order to achieve a better efficient resource network management. The purpose of this paper is to present a multi-service platform based on admission curves for Third Generation (3G) and beyond mobile networks, depending on some cell characteristics, which are calculated based on real measurements. The model considers admission cur...

An improved capacity model based on radio measurements for a 4G and beyond wire...

The mobile networks utilization is increasingly high, which implies a efficient resource network management coupled with a realistic capacity model. The aim of this paper is to present a capacity platform for Fourth Generation (4G) mobile networks, based on real measurements. The core of the proposed method is the deployment of a Multiple Linear Regression (MLR) model, based on propagation conditions, channel q...

Introdução à deep web

Trabalho baseado no relatório para a disciplina “Sociologia das Novas Tecnologias de Informação” no âmbito do Mestrado Integrado de Engenharia e Gestão Industrial, da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa em 2015-16. O trabalho foi orientado pelo Prof. António Brandão Moniz do Departamento de Ciências Sociais Aplicadas (DCSA) na mesma Faculdade.; A Deep Web é a parte da Internet que ...

Legislação N.º 53 (Outubro-Dezembro 2010)