Although cloud network flows are similar to conventional network flows in many ways, there are some major differences in their statistical characteristics. However, due to the lack of adequate public datasets, the proponents of many existing cloud intrusion detection systems (IDS) have relied on the DARPA dataset which was obtained by simulating a conventional network environment. In the current thesis, we show empirically that the DARPA dataset by failing to meet important statistical characteristics of real-world cloud traffic data centers is inadequate for evaluating cloud IDS. We analyze, as an alternative, a new public dataset collected through cooperation between our lab and a non-profit cloud service provider, which contains benign data and a wide variety of attack data. Furthermore, we present a new hypervisor-based cloud IDS using an instance-oriented feature model and supervised machine learning techniques. We investigate 3 different classifiers: Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms. Experimental evaluation on a diversified dataset yields a detection rate of 92.08% and a false-positive rate of 1.49% for the random forest, the best performing of the three classifiers. / Graduate

Coronal Mass Ejections (CMEs) and solar flares are energetic events taking place at the Sun that can affect the space weather or the near-Earth environment by the release of vast quantities of electromagnetic radiation and charged particles. Solar active regions are the areas where most flares and CMEs originate. Studying the associations among sunspot groups, flares, filaments, and CMEs is helpful in understanding the possible cause and effect relationships between these events and features. Forecasting space weather in a timely manner is important for protecting technological systems and human life on earth and in space. The research presented in this thesis introduces novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this work consists of three stages: (1) designing computer tools to find the associations among sunspot groups, flares, filaments, and CMEs (2) applying machine learning algorithms to the associations¿ datasets and (3) studying the evolution patterns of sunspot groups using time-series methods. Machine learning algorithms are used to provide computerised learning rules and models that enable the system to provide automated prediction of CMEs, flares, and evolution patterns of sunspot groups. These numerical rules are extracted from the characteristics, associations, and time-series analysis of the available historical solar data. The training of machine learning algorithms is based on data sets created by investigating the associations among sunspots, filaments, flares, and CMEs. Evolution patterns of sunspot areas and McIntosh classifications are analysed using a statistical machine learning method, namely the Hidden Markov Model (HMM).

No description available.

yes / Coinciding with advances in whole-slide imaging scanners, it is become essential to automate the conventional image-processing techniques to assist pathologists with some tasks such as mitotic-cells detection. In histopathological images analysing, the mitotic-cells counting is a significant biomarker in the prognosis of the breast cancer grade and its aggressiveness. However, counting task of mitotic-cells is tiresome, tedious and time-consuming due to difficulty distinguishing between mitotic cells and normal cells. To tackle this challenge, several deep learning-based approaches of Computer-Aided Diagnosis (CAD) have been lately advanced to perform counting task of mitotic-cells in the histopathological images. Such CAD systems achieve outstanding performance, hence histopathologists can utilise them as a second-opinion system. However, improvement of CAD systems is an important with the progress of deep learning networks architectures. In this work, we investigate deep YOLO (You Only Look Once) v2 network for mitotic-cells detection on ICPR (International Conference on Pattern Recognition) 2012 dataset of breast cancer histopathology. The obtained results showed that proposed architecture achieves good result of 0.839 F1-measure.

In this research, the focus was on the development of a Battery Cycler system with the primary objective of accurately predicting both the State of Charge (SoC) and State of Health (SoH) for batteries. Machine learning techniques, specifically MLP Regression, K-Nearest Neighbor, and Decision Tree Regression, were harnessed for a comprehensive analysis. The data collection and processing phase spanned 44 days. The findings underscore the potential of employing relatively uncomplicated machine learning models for the prediction of SoC and SoH. Particularly noteworthy was the strong performance of K-Nearest Neighbor, especially after deliberate optimization efforts were applied. Despite the simplicity of these techniques, the results suggest significant promise for battery management and health assessment. Nevertheless, challenges linked to temperature fluctuations and current noise were identified as factors impacting predictive performance. Mitigating these challenges is imperative to enhance the robustness and precision of predictive models in future iterations. The implications of this work extend to broader applications in battery management systems, offering insights into potential avenues for optimizing battery usage, extending longevity, and enhancing overall performance. Leveraging machine learning methodologies in a straightforward manner, this research is anticipated to contribute to advancements in battery health monitoring and management, setting the stage for more intricate models in subsequent studies. / I denna forskning har vi tagit oss an utvecklingen av ett battericykelsystem med målet att noggrant förutsäga både laddningstillstånd (SoC) och hälsotillstånd (SoH) för batterier. Genom att utnyttja maskininlärningstekniker utförde vi en omfattande analys med hjälp av MLP-regression, K-Nearest Neighbor och Decision Tree regression. Över en tidsperiod av 44 dagar samlades batteridata in och bearbetades noggrant. Resultatet understryker möjligheten att använda relativt okomplicerade maskininlärningsmodeller för att förutsäga både laddningstillstånd och hälsotillstånd. Särskilt K-Nearest Neighbor framstår som en lovande kandidat med tanke på förutsägbarhetsnoggrannheten den visat, särskilt efter medvetna ansträngningar för optimering. Trots teknikernas enkelhet antyder våra resultat en betydande potential för batterihantering och hälsobedömning. Emellertid har utmaningar som temperaturvariationer och strömbuller identifierats som faktorer som påverkar förutsägelseprestanda. Att bemöta dessa utmaningar är avgörande för att öka robustheten och precisionen i våra förutsägelsemodeller i framtida utföranden.. Denna forsknings arbetsresultat sträcker sig till bredare användningsområden inom batterihanteringssystem och erbjuder insikter i möjliga riktningar för att optimera batterianvändning, livslängd och övergripande prestanda. Genom att utnyttja maskininlärningsmetoder förutser vi att denna forskning kommer att bidra till framsteg inom övervakning och hantering av batterihälsa, och lägga grunden för mer sofistikerade modeller i framtiden.

[ES] La presente tesis aborda el problema del encaminamiento en las redes definidas por software (SDN). Específicamente, aborda el problema del diseño de un protocolo de encaminamiento basado en inteligencia artificial (AI) para garantizar la calidad de servicio (QoS) en transmisiones multimedia. En la primera parte del trabajo, el concepto de SDN es introducido. Su arquitectura, protocolos y ventajas son comentados. A continuación, el estado del arte es presentado, donde diversos trabajos acerca de QoS, encaminamiento, SDN y AI son detallados. En el siguiente capítulo, el controlador SDN, el cual juega un papel central en la arquitectura propuesta, es presentado. Se detalla el diseño del controlador y se compara su rendimiento con otro controlador comúnmente utilizado. Más tarde, se describe las propuestas de encaminamiento. Primero, se aborda la modificación de un protocolo de encaminamiento tradicional. Esta modificación tiene como objetivo adaptar el protocolo de encaminamiento tradicional a las redes SDN, centrado en las transmisiones multimedia. A continuación, la propuesta final es descrita. Sus mensajes, arquitectura y algoritmos son mostrados. Referente a la AI, el capítulo 5 detalla el módulo de la arquitectura que la implementa, junto con los métodos inteligentes usados en la propuesta de encaminamiento. Además, el algoritmo inteligente de decisión de rutas es descrito y la propuesta es comparada con el protocolo de encaminamiento tradicional y con su adaptación a las redes SDN, mostrando un incremento de la calidad final de la transmisión. Finalmente, se muestra y se describe algunas aplicaciones basadas en la propuesta. Las aplicaciones son presentadas para demostrar que la solución presentada en la tesis está diseñada para trabajar en redes heterogéneas. / [CA] La present tesi tracta el problema de l'encaminament en les xarxes definides per programari (SDN). Específicament, tracta el problema del disseny d'un protocol d'encaminament basat en intel·ligència artificial (AI) per a garantir la qualitat de servici (QoS) en les transmissions multimèdia. En la primera part del treball, s'introdueix les xarxes SDN. Es comenten la seva arquitectura, els protocols i els avantatges. A continuació, l'estat de l'art és presentat, on es detellen els diversos treballs al voltant de QoS, encaminament, SDN i AI. Al següent capítol, el controlador SDN, el qual juga un paper central a l'arquitectura proposta, és presentat. Es detalla el disseny del controlador i es compara el seu rendiment amb altre controlador utilitzat comunament. Més endavant, es descriuen les propostes d'encaminament. Primer, s'aborda la modificació d'un protocol d'encaminament tradicional. Aquesta modificació té com a objectiu adaptar el protocol d'encaminament tradicional a les xarxes SDN, centrat a les transmissions multimèdia. A continuació, la proposta final és descrita. Els seus missatges, arquitectura i algoritmes són mostrats. Pel que fa a l'AI, el capítol 5 detalla el mòdul de l'arquitectura que la implementa, junt amb els mètodes intel·ligents usats en la proposta d'encaminament. A més a més, l'algoritme intel·ligent de decisió de rutes és descrit i la proposta és comparada amb el protocol d'encaminament tradicional i amb la seva adaptació a les xarxes SDN, mostrant un increment de la qualitat final de la transmissió. Finalment, es mostra i es descriuen algunes aplicacions basades en la proposta. Les aplicacions són presentades per a demostrar que la solució presentada en la tesi és dissenyada per a treballar en xarxes heterogènies. / [EN] This thesis addresses the problem of routing in Software Defined Networks (SDN). Specifically, the problem of designing a routing protocol based on Artificial Intelligence (AI) for ensuring Quality of Service (QoS) in multimedia transmissions. In the first part of the work, SDN is introduced. Its architecture, protocols and advantages are discussed. Then, the state of the art is presented, where several works regarding QoS, routing, SDN and AI are detailed. In the next chapter, the SDN controller, which plays the central role in the proposed architecture, is presented. The design of the controller is detailed and its performance compared to another common controller. Later, the routing proposals are described. First, a modification of a traditional routing protocol is discussed. This modification intends to adapt a traditional routing protocol to SDN, focused on multimedia transmissions. Then, the final proposal is described. Its messages, architecture and algorithms are depicted. As regards AI, chapter 5 details the module of the architecture that implements it, along with all the intelligent methods used in the routing proposal. Furthermore, the intelligent route decision algorithm is described and the final proposal is compared to the traditional routing protocol and its adaptation to SDN, showing an increment of the end quality of the transmission. Finally, some applications based on the routing proposal are described. The applications are presented to demonstrate that the proposed solution can work with heterogeneous networks. / Rego Máñez, A. (2020). Intelligent multimedia flow transmission through heterogeneous networks using cognitive software defined networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/160483 / TESIS

<p>Ni-based superalloys continue to exert themselves as the industry standards in high stress and highly corrosive/oxidizing environments, such as are present in a gas turbine engine, due to their excellent high temperature strengths, thermal and microstructural stabilities, and oxidation and creep resistances. Gas turbine engines are essential components for energy generation and propulsion in the modern age. However, Ni-based superalloys are reaching their limits in the operating conditions of these engines due to their melting onset temperatures, which is approximately 1300 °C. Therefore, a new class of materials must be formulated to surpass the capabilities Ni-based superalloys, as increasing the operating temperature leads to increased efficiency and reductions in fuel consumption and greenhouse gas emissions. One of the proposed classes of materials is termed refractory complex concentrated alloys, or RCCAs, which consist of 4 or more refractory elements (in this study, selected from: Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, and W) in equimolar or near-equimolar proportions. So far, there have been highly promising results with these alloys, including far higher melting points than Ni-based superalloys and outstanding high-temperature strengths in non-oxidizing environments. However, improvements in room temperature ductility and high-temperature oxidation resistance are still needed for RCCAs. Also, given the millions of possible alloy compositions spanning various combinations and concentrations of refractory elements, more efficient methods than just serial experimental trials are needed for identifying RCCAs with desired properties. A coupled computational and experimental approach for exploring a wide range of alloy systems and compositions is crucial for accelerating the discovery of RCCAs that may be capable of replacing Ni-based superalloys. </p> <p>In this thesis, the CALPHAD method was utilized to generate basic thermodynamic properties of approximately 67,000 Al-bearing RCCAs. The alloys were then down-selected on the basis of certain criteria, including solidus temperature, volume percent BCC phase, and aluminum activity. Machine learning models with physics-based descriptors were used to select several BCC-based alloys for fabrication and characterization, and an active learning loop was employed to aid in rapid alloy discovery for high hardness and strength. This method resulted in rapid identification of 15 BCC-based, four component, Al-bearing RCCAs exhibiting room-temperature Vickers hardness from 1% to 35% above previously reported alloys. This work exemplifies the advantages of utilizing Integrated Computational Materials Engineering- and Materials Genome Initiative-driven approaches for the discovery and design of new materials with attractive properties.</p> <p> </p> <p><br></p>

In recent biomedical research, graph representation learning and cell profiling techniques have emerged as transformative tools for analyzing high-dimensional biological data. The integration of these methods, as investigated in this study, has facilitated an enhanced understanding of complex biological systems, consequently improving drug discovery. The research aimed to decipher connections between chemical structures and cellular phenotypes while incorporating other biological information like proteins and pathways into the workflow. To achieve this, machine learning models' efficacy was examined for classification and regression tasks. The newly proposed graph-level and bio-graph integrative predictors were compared with traditional models. Results demonstrated their potential, particularly in classification tasks. Moreover, the topology of the COVID-19 BioGraph was analyzed, revealing the complex interconnections between chemicals, proteins, and biological pathways. By combining network analysis, graph representation learning, and statistical methods, the study was able to predict active chemical combinations within inactive compounds, thereby exhibiting significant potential for further investigations. Graph-based generative models were also used for molecule generation opening up further research avenues in finding lead compounds. In conclusion, this study underlines the potential of combining graph representation learning and cell profiling techniques in advancing biomedical research in drug repurposing and drug combination. This integration provides a better understanding of complex biological systems, assists in identifying therapeutic targets, and contributes to optimizing molecule generation for drug discovery. Future investigations should optimize these models and validate the drug combination discovery approach. As these techniques continue to evolve, they hold the potential to significantly impact the future of drug screening, drug repurposing, and drug combinations.

Erfolgreiche Unternehmen denken intensiv über den eigentlichen Nutzen ihres Unternehmens für Kunden nach. Diese versuchen, ihrer Konkurrenz voraus zu sein, und zwar durch gute Ideen, Innovationen und Kreativität. Dabei wird Erfolg anhand von Metriken gemessen, wie z.B. der Anzahl der loyalen Kunden oder der Anzahl der Käufer. Gegeben, dass der Wettbewerb durch die Globalisierung, Deregulierung und technologische Innovation in den letzten Jahren angewachsen ist, spielen die richtigen Entscheidungen für den Erfolg gerade im operativen Geschäft der sämtlichen Bereiche des Unternehmens eine zentrale Rolle. Vor diesem Hintergrund entstammen die in der vorliegenden Arbeit zur Evaluation der Methoden des maschinellen Lernens untersuchten Entscheidungsprobleme vornehmlich der Entscheidungsunterstützung. Hierzu gehören Klassifikationsprobleme wie die Kreditwürdigkeitsprüfung im Bereich Credit Scoring und die Effizienz der Marketing Campaigns im Bereich Direktmarketing. In diesem Kontext ergaben sich Fragestellungen für die korrelativen Modelle, nämlich die Untersuchung der Eignung der Verfahren des maschinellen Lernens für den Bereich des Credit Scoring, die Kalibrierung der Wahrscheinlichkeiten, welche mithilfe von Verfahren des maschinellen Lernens erzeugt werden sowie die Konzeption und Umsetzung einer Synergie-Heuristik zwischen den Methoden der klassischen Statistik und Verfahren des maschinellen Lernens. Desweiteren wurden kausale Modelle für den Bereich Direktmarketing (sog. Uplift-Effekte) angesprochen. Diese Themen wurden im Rahmen von breit angelegten empirischen Studien bearbeitet. Zusammenfassend ergibt sich, dass der Einsatz der untersuchten Verfahren beim derzeitigen Stand der Forschung zur Lösung praxisrelevanter Entscheidungsprobleme sowie spezifischer Fragestellungen, welche aus den besonderen Anforderungen der betrachteten Anwendungen abgeleitet wurden, einen wesentlichen Beitrag leistet. / Nowadays right decisions, being it strategic or operative, are important for every company, since these contribute directly to an overall success. This success can be measured based on quantitative metrics, for example, by the number of loyal customers or the number of incremental purchases. These decisions are typically made based on the historical data that relates to all functions of the company in general and to customers in particular. Thus, companies seek to analyze this data and apply obtained knowlegde in decision making. Classification problems represent an example of such decisions. Classification problems are best solved, when techniques of classical statistics and these of machine learning are applied, since both of them are able to analyze huge amount of data, to detect dependencies of the data patterns, and to produce probability, which represents the basis for the decision making. I apply these techniques and examine their suitability based on correlative models for decision making in credit scoring and further extend the work by causal predictive models for direct marketing. In detail, I analyze the suitability of techniques of machine learning for credit scoring alongside multiple dimensions, I examine the ability to produce calibrated probabilities and apply techniques to improve the probability estimations. I further develop and propose a synergy heuristic between the methods of classical statistics and techniques of machine learning to improve the prediction quality of the former, and finally apply conversion models to turn machine learning techqiques to account for causal relationship between marketing campaigns and customer behavior in direct marketing. The work has shown that the techniques of machine learning represent a suitable alternative to the methods of classical statistics for decision making and should be considered not only in research but also should find their practical application in real-world practices.

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.