2,145 research outputs found

    Mobility in Lisbon based on smartphone data

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    This research covers five months (September, October, November, December 2021, and January 2022) of georeferenced data of the Vodafone mobile phone service, provided by the municipality of Lisbon (CML). The motivation of this research regards the fact that the urban mobility study with mobile phone data is a relatively unexplored topic. This study focused on the city of Lisbon, with a case study conducted in the parish of Santa Maria Maior with the aim to understand the urban mobility patterns of mobile phone users. The number of roaming and non-roaming devices in the case study is related to the subject of a vibrant neighborhood and tourism, characterized by transportation and historical points of interest. We used a data mining approach to analyze mobility trends, adopting a CRISP-DM methodology, to perform statistical analysis, visualization, and clustering (DBSCAN) methods. Results showed eight clusters in Santa Maria Maior, with outstanding clusters along 28-E electric tram and Lisbon Cruise Terminal. Foremost, we looked at these two clusters and performed a forecast model with Prophet, resulting in downward trend, influenced by the pandemic restrictions in December and January data. This thesis contributes considerably to the digital transformation of Lisbon into a smart city by understanding urban mobility patterns with smartphone data of no roaming and roaming users.Este estudo abrange cinco meses (setembro, outubro, novembro, dezembro de 2021 e janeiro de 2022) de dados georreferenciados do serviço da operadora móvel Vodafone, fornecido pela Câmara Municipal de Lisboa (CML). A motivação da tese considera o facto de o estudo da mobilidade urbana com dados de telemóveis ser um tópico relativamente inexplorado. Este estudo centrou-se na cidade de Lisboa, com um caso de estudo na freguesia de Santa Maria Maior com o objetivo de compreender os padrões de mobilidade urbana dos utilizadores da rede móvel. O número de dispositivos de nãoroaming e roaming no caso de estudo está relacionado com o tema das ‘vibrant neighborhoods’ e turismo, caracterizado por pontos de interesse históricos e de transportes. Utilizámos uma abordagem de ‘data mining’ para analisar as tendências de mobilidade, adotando uma metodologia CRISP-DM, para realizar análise estatística, visualização e agrupamentos (DBSCAN). Os resultados mostraram nove agrupamentos em Santa Maria Maior, dos quais dois agrupamentos de destaque, um ao longo do elétrico 28-E e outro à volta do Terminal de Cruzeiros de Lisboa. Em primeiro lugar, analisámos estes dois agrupamentos e realizámos análises de previsão, resultando numa tendência decrescente, como consequência das restrições da pandemia nos meses de dezembro e janeiro. Esta tese contribui consideravelmente para a transformação digital de Lisboa numa cidade inteligente, ao compreender os padrões de mobilidade urbana com dados dos utilizadores da rede móvel em não-roaming e roaming

    Novel System and Method For Telephone Network Planing Based on Neutrosophic Graph

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    Telephony is gaining momentum in the daily lives of individuals and in the activities of all companies. With the great trend towards telephony networks, whether analogue or digital known as Voice over IP (VoIP), the number of calls an individual can receive becomes considerably high. However, effective management of incoming calls to subscribers becomes a necessity. Recently, much attention has been paid towards applications of single-valued neutrosophic graphs in various research fields. One of the suitable reason is it provides a generalized representation of fuzzy graphs (FGs) for dealing with human nature more effectively when compared to existing models i.e. intuitionistic fuzzy graphs (IFGs), inter-valued fuzzy graphs (IVFGs) and bipolar-valued fuzzy graphs (BPVFGs) etc. In this paper we focused on precise analysis of useful information extracted by calls received, not received due to some reasons using the properties of SVNGs. Hence the proposed method introduced one of the first kind of mathematical model for precise analysis of instantaneous traffic beyond the Erlang unit. To achieve this goal an algorithm is proposed for a neutrosophic mobile network model (NMNM) based on a hypothetical data set. In addition, the drawback and further improvement of proposed method with a mathematical proposition is established for it precise applications

    A Study of recent classification algorithms and a novel approach for biosignal data classification

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    Analyzing and understanding human biosignals have been important research areas that have many practical applications in everyday life. For example, Brain Computer Interface is a research area that studies the connection between the human brain and external systems by processing and learning the brain signals called Electroencephalography (EEG) signals. Similarly, various assistive robotics applications are being developed to interpret eye or muscle signals in humans in order to provide control inputs for external devices. The efficiency for all of these applications depends heavily on being able to process and classify human biosignals. Therefore many techniques from Signal Processing and Machine Learning fields are applied in order to understand human biosignals better and increase the efficiency and success of these applications. This thesis proposes a new classifier for biosignal data classification utilizing Particle Swarm Optimization Clustering and Radial Basis Function Networks (RBFN). The performance of the proposed classifier together with several variations in the technique is analyzed by utilizing comparisons with the state of the art classifiers such as Fuzzy Functions Support Vector Machines (FFSVM), Improved Fuzzy Functions Support Vector Machines (IFFSVM). These classifiers are implemented on the classification of same biological signals in order to evaluate the proposed technique. Several clustering algorithms, which are used in these classifiers, such as K-means, Fuzzy c-means, and Particle Swarm Optimization (PSO), are studied and compared with each other based on clustering abilities. The effects of the analyzed clustering algorithms in the performance of Radial Basis Functions Networks classifier are investigated. Strengths and weaknesses are analyzed on various standard and EEG datasets. Results show that the proposed classifier that combines PSO clustering with RBFN classifier can reach or exceed the performance of these state of the art classifiers. Finally, the proposed classification technique is applied to a real-time system application where a mobile robot is controlled based on person\u27s EEG signal

    Enhancing bank direct marketing through data mining

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    The financial crisiscreated pressure on banksdue to credit restriction, increasing competition for deposits retention and demanding efficiency improvements of direct marketing campaigns. Our research conducted a data mining project on direct marketing campaigns for depositssubscriptionsby using recent data of a Portuguese retail bank. We used the Support Vector Machine (SVM) data mining technique for modeling and evaluated it through a sensitive analysis. The findings revealed previously unknown valuable knowledge, such as the best months for campaigns to occur, and optimal call duration. Such knowledge can be used to improve campaign efficiency

    Uncertainty and Congestion Elimination in 4G Network Call Admission Control using Interval Type-2 Intuitionistic Fuzzy Logic

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    The management and control of the global growth and complex nature of wireless Fourth Generation (4G) Networks elicits the need for Call Admission Control (CAC). However, CAC faces the challenge of network congestion, thereby deteriorating the network Quality of Service (QoS) due to inherent imprecision and uncertainties in the QoS data which leads to difficulties in measuring some objective and constraints of QoS using crisp values. Previous researches have shown the strength of Interval Type-2 Fuzzy Logic System (IT2FLS) in coping adequately with linguistic uncertainties. Intuitionistic fuzzy sets (IFSs) have indicated their ability to further reduce uncertainty by handling conflicting evaluation involving membership (M), nonmembership (NM) and hesitation. This paper applies the Interval Type-2 Intuitionistic Fuzzy Logic System (IT2IFLS) in solving CAC problem in order to achieve a better QoS in 4G Networks

    Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

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    Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental study of all the method using common data was given to demonstrate the accuracy and complexity. This paper may serve as a stepping stone for future researchers in cross-disciplinary field of machine learning aided malware forensics.Comment: 37 Page
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