Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

Implementação de classificador de tarefas mentais baseado em EEG

BARBOSA, André F. ; SOUZA, Bryan C. ; PEREIRA JUNIOR, Antônio ; MEDEIROS, Adelardo A. D.de, . Implementação de Classificador de Tarefas Mentais Baseado em EEG. In: CONGRESSO BRASILEIRO DE REDES NEURAIS, 9., 2009, Ouro Preto, MG. Anais... Ouro Preto, MG, 2009

Water loss management strategies for developing countries

Diese Arbeit beschäftigt sich mit nicht in Rechnung stellbaren Wasserverlusten in städtischen Versorgungsnetzen in Entwicklungsländern. Es soll das Wissen über diese Verluste erweitert und aufgezeigt werden, ob diese auf ein ökonomisch vertretbares Maß reduziert werden können. Die vorliegende Doktorarbeit untersucht solche unberechneten Wasserverluste und versucht, neben der Quantifizierung von Leckagen auch Entscheidungswerkzeuge für ein verbessertes Management der Versorgungsnetze in Entwicklungsländern zu erarbeiten. Als Fallstudie dient Harare, die Hauptstadt von Simbabwe. Wasserverluste in Verteilungsnetzen sind unvermeidbar, sollten aber auf ein ökonomisch tragbares Niveau reduziert werden, wenn ein nachhaltiger Betrieb erreicht werden soll. Wasserverluste können sowohl durch illegale und ungenehmigte Anschlüsse oder durch Undichtigkeiten im Verteilnetz, als auch durch mangelhafte Mess- und Berechnungssysteme entstehen. Es sind bereits viele Ansätze zur Verringerung von Verlusten in Wasserverteilsystemen bekannt geworden, entsprechend existieren dazu auch zahlreiche Methoden und Werkzeuge. Diese reichen von computergestützten Verfahren über gesetzliche und politische Vorgaben sowie ökonomische Berechnungen bis hin zu Maßnahmen der Modernisierung der Infrastruktur. Der Erfolg dieser Anstrengungen ist abhängig von der Umsetzbarkeit und dem Umfeld, in dem diese Maßnahmen durchgeführt werden. Die Bewertung der Arbeitsgüte einer jeden Wasserversorgungseinheit basiert auf der Effektivität des jeweiligen Verteilungssystems. Leistungs- und Bewertungszahlen sind die meist genutzten Ansätze, um Wasserverteilsysteme und ihre Effizienz einzustufen. Weltweit haben sich zur Bewertung als Indikatoren die finanzielle und die technische Leistungsfähigkeit durchgesetzt. Die eigene Untersuchung zeigt, dass diese Indikatoren in vielen Wasserversorgungssystemen der Entwicklungsländer nicht zur Einführung von Verlust reduzierenden Managementstrategien geführt haben. Viele durchgeführte Studien über die Einführung von Maßnahmen zur Verlustreduzierung beachten nur das gesamte nicht in Rechnung stellbare Wasser, ohne aber den Anteil der Leckagen an der Gesamthöhe zu bestimmen. Damit ist keine Aussage über die tatsächliche Zuordnung der Verluste möglich. Aus diesem Grund ist ein Bewertungsinstrument notwendig, mit dem die Verluste den verschiedenen Ursachen zugeordnet werden können. Ein solches Rechenwerkzeug ist das South African Night Flow Analysis Model (SANFLOW) der südafrikanischen Wasser-Forschungskommission, das Untersuchungen von Wasserdurchfluss und Anlagendruck in einzelnen Verteilbezirken ermöglicht. In der vorliegenden Arbeit konnte nachgewiesen werden, dass das SANFLOW-Modell gut zur Bestimmung des Leckageanteiles verwendet werden kann. Daraus kann gefolgert werden, dass dieses Modell ein geeignetes und gut anpassbares Analysewerkzeug für Entwicklungsländer ist. Solche computergestützte Berechnungsansätze können zur Bestimmung von Leckagen in Wasserverteilungsnetzen eingesetzt werden. Eine weitere Möglichkeit ist der Einsatz von Künstlichen Neuronalen Netzen (Artificial Neural Network – ANN), die trainiert und dann zur Vorhersage der dynamischen Verhältnisse in Wasserversorgungssystemen genutzt werden können. Diese Werte können mit der Wassernachfrage eines definierten Bezirks verglichen werden. Zur Untersuchung wurde ein Mehrschichtiges Künstliches Neuronales Netz mit Fehlerrückführung zur Modellierung des Wasserflusses in einem überwachten Abschnitt eingesetzt. Zur Bestimmung des Wasserbedarfes wurde ein MATLAB Algorithmus entwickelt. Aus der Differenz der aktuellen und des simulierten Wassernachfrage konnte die Leckagerate des Wasserversorgungssystems ermittelt werden. Es konnte gezeigt werden, dass mit dem angelernten Neuronalen Netzwerk eine Vorhersage des Wasserflusses mit einer Genauigkeit von 99% möglich ist. Daraus lässt sich die Eignung von ANNs als flexibler und wirkungsvoller Ansatz zur Leckagedetektion in der Wasserversorgung ableiten. Die Untersuchung zeigte weiterhin, dass im Versorgungsnetz von Harare 36 % des eingespeisten Wassers verloren geht. Davon wiederum sind 33 % auf Leckagen zurückzuführen. Umgerechnet bedeutet dies einen finanziellen Verlust von monatlich 1 Millionen Dollar, was 20 % der Gesamteinnahmen der Stadt entspricht. Der Stadtverwaltung von Harare wird daher empfohlen, aktiv an der Beseitigung der Leckagen zu arbeiten, da diese hohen Verluste den Versorgungsbetrieb negativ beeinflussen. Abschließend wird in der Arbeit ein integriertes Leckage-Managementsystem vorgeschlagen, das den Wasserversorgern eine Entscheidungshilfe bei zu ergreifenden Maßnahmen zur Instandhaltung des Verteilnetzes geben soll.

A survey of intrusion detection system technologies

This paper provides an overview of IDS types and how they work as well as configuration considerations and issues that affect them. Advanced methods of increasing the performance of an IDS are explored such as specification based IDS for protecting Supervisory Control And Data Acquisition (SCADA) and Cloud networks. Also by providing a review of varied studies ranging from issues in configuration and specific problems to custom techniques and cutting edge studies a reference can be provided to others interested in learning about and developing IDS solutions. Intrusion Detection is an area of much required study to provide solutions to satisfy evolving services and networks and systems that support them. This paper aims to be a reference for IDS technologies other researchers and developers interested in the field of intrusion detection.

Implementação de classificador de tarefas mentais baseado em EEG

BARBOSA, André F. ; SOUZA, Bryan C. ; PEREIRA JUNIOR, Antônio ; MEDEIROS, Adelardo A. D.de, . Implementação de Classificador de Tarefas Mentais Baseado em EEG. In: CONGRESSO BRASILEIRO DE REDES NEURAIS, 9., 2009, Ouro Preto, MG. Anais... Ouro Preto, MG, 2009

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.

Detecção do mal-posicionamento rotacional de dedos em dispositivos de captura de impressões digitais multivista sem toque utilizando redes neurais artificiais

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2016.

Um sistema para a inferência de variáveis usando redes neurais artificiais

This work aims to obtain a low-cost virtual sensor to estimate the quality of LPG. For the acquisition of data from a distillation tower, software HYSYS ® was used to simulate chemical processes. These data will be used for training and validation of an Artificial Neural Network (ANN). This network will aim to estimate from available simulated variables such as temperature, pressure and discharge flow of a distillation tower, the mole fraction of pentane present in LPG. Thus, allowing a better control of product quality

Sistema inteligente para estimar a porosidade em sedimentos a partir da análise de sinais GPR

This Thesis presents the elaboration of a methodological propose for the development of an intelligent system, able to automatically achieve the effective porosity, in sedimentary layers, from a data bank built with information from the Ground Penetrating Radar GPR. The intelligent system was built to model the relation between the porosity (response variable) and the electromagnetic attribute from the GPR (explicative variables). Using it, the porosity was estimated using the artificial neural network (Multilayer Perceptron MLP) and the multiple linear regression. The data from the response variable and from the explicative variables were achieved in laboratory and in GPR surveys outlined in controlled sites, on site and in laboratory. The proposed intelligent system has the capacity of estimating the porosity from any available data bank, which has the same variables used in this Thesis. The architecture of the neural network used can be modified according to the existing necessity, adapting to the available data bank. The use of the multiple linear regression model allowed the identification and quantification of the influence (level of effect) from each explicative variable in the estimation of the porosity. The proposed methodology can revolutionize the use of the GPR, not only for the imaging of the sedimentary geometry and faces, but mainly for the automatically achievement of the porosity one of the most important parameters for the characterization of reservoir rocks (from petroleum or water)

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

Deep Learning applicato alla diagnostica dei trasformatori

Il trasformatore è uno degli elementi più importanti di una rete di trasmissione; essendo il tramite fra reti di alta e media tensione, il suo corretto funzionamento garantisce l’alimentazione di tutti i dispositivi e carichi connessi alla linea. Oltre a questo, il trasformatore è anche l’elemento più costoso di tutta la linea elettrica; la sua manutenzione è di vitale importanza per evitare costi elevati per la sostituzione e disagi lungo la linea. Qui entra in gioco il ruolo della diagnostica; attraverso misure periodiche e mirate sul trasformatore è possibile agire tempestivamente ed evitare tutti i fenomeni precedentemente elencati. Nell’elaborato si tratterà l’analisi del trasformatore elettrico trifase durante il suo funzionamento, evidenziando i sottocomponenti e le rispettive criticità; inoltre, verranno mostrate le varie tecniche di diagnostica del trasformatore, in modo tale da poter estrarre un indice legato allo stato di vita, ossia l’Health Index. Ad oggi esistono diverse tecniche di approccio al calcolo dell’Health Index, quella che viene presentata è una tecnica del tutto innovativa, ossia sviluppare una rete neurale artificiale (Artificial Neural Network, ANN) in grado di prevedere lo stato del trasformatore basandosi su misure effettuate sullo stesso. Dunque, verranno presentante le basi per lo sviluppo di una rete neurale, partendo dall’analisi e formattazione dei dati, fino alla fase di ottimizzazione delle prestazioni. Infine, si attraverseranno tutte le fasi intermedie di realizzazione del progetto da cui l’elaborato prende il titolo; osservando l’evoluzione di una rete neurale che si trasforma da un programma scritto in ambiente Python a una applicazione pronta all’uso per gli operatori durante le operazioni di diagnostica.