Personalized marketing campaign for upselling using predictive modeling in the health insurance sector

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsNowadays, with the oversupply of several different solutions in the private Health Insurance sector and the constantly increasing demand for value for money services from the client’s perspective, it becomes clear that Insurance Companies shouldn’t only strive for excellence but also engage their client base by offering solutions that are more suitable to their needs. This project aims, using the power that predictive models can provide, to predict the existing Health Insurance clients who are willing to move in a higher tier product. The case presented above could be described under the term of upselling. The final model will be used for a personalized marketing campaign in one of the most prominent bancassurances in Portugal. At the moment the ongoing upselling campaign, uses only few eligibility criteria. The outcome of the model has as a goal to assign a probability to each client who is eligible to be contacted for this campaign. The data that were retrieved to train the model, had a buffer period of one week from when the ‘event’ took place. This is crucial for the business, because there is always the time-to-market parameter which should be taken into consideration in the real world. The tools that were used for completing this Data Mining project were mostly SAS Enterprise Guide and SAS Enterprise Miner. All the Data Marts that were needed for the particular project, were built and loaded in SAS, so there were no obstacles or connectivity issues. For data visualization and reporting, Microsoft PowerBI was used. Some of the tables in the Data Marts, are being updated in a daily and other in a monthly basis. Of course, all the historical information is being stored in separate tables, so there is no information loss or discrepancies. Finally, the methodology that was followed for the implementation of the Data Mining project was a hybrid framework between the SEMMA approach as it is the one that is proposed by SAS Institute to carry out the core tasks of model development and CRISP-DM

Analyzing the 'Leader-Laggard' Dynamic in the Context of EU Environmental Policy: A Federal Perspective

This thesis aims to explain and analyze the policy dynamics behind implementation patterns in multi-level policy settings using the EU as an example. It does so by examining the implementation of EU environmental policy in member states in light of the recent economic crisis. The analysis of implementation patterns in the EU seeks to provide an updated approach and an in-depth understanding of the ‘leader-laggard’ dynamic, namely the distinction between those states that are ‘frontrunners’ and ‘laggards’ in their environmental policy performance. The key issue captured is the various regulatory trends and policy outcomes at the EU level in terms of implementation performance. Two case studies one of a reputed ‘leader’ (UK) and one of traditional ‘laggard’ (Greece) are employed to better define and interpret this dynamic in practice. In this analysis, the use of federal theory as the main theoretical framework is very crucial for a contemporary theorization of implementation in the EU as a multi-level polity. Having the advantage that it is not dependent on a state-centric ontology, federalism provides an understanding of multi-level political relationships that are neither purely domestic nor purely international. Drawing on this analysis, the main findings show that the impact of economic crisis on environmental policy implementation was dependent on the economic level in member states, i.e. the less wealthy Southern member states performed worse than the richer Northern states. Moreover, there has been strong pressure in many states to relax environmental standards in the name of growth. A closer look at the case studies demonstrates that the issue of cost is very important. In this light, the pro-growth agenda is dominant considering the government priorities of the UK and Greece. Besides these factors, the better economic position, the well-functioning public administration, the strong administrative and institutional capacity of the state also allow the UK to better implement EU environmental policy in comparison to Greece. In this regard, the use of federal theory captures the importance of domestic political context in the implementation of EU environmental policy

What are the effects of COVID-19 on the environment?

Contrary to the socio-economic aspects of the coronavirus crisis, the environment has comparably attracted lesser attention. To the question, if there is a silver lining to the global pandemic, existing data and studies show that the environment is an unanticipated beneficiary that gives a glimmer of hope for the post-COVID-19 period. Acknowledging the difficulties in conducting in-depth evaluations over the environmental impacts as the pandemic still unfolds, some preliminary inferences can be drawn. The aim of this paper is to outline and analyse the indirect effects of COVID-19 on the environment for a better understanding and knowledge during the lockdown at the international level. Indicatively, some of the positive effects are met in the decrease of GHG emissions, the fall of fossil fuel consumption, the improved air and water quality, and the re-emergence of wildlife. On the other hand, key challenges lie in the significant increase in medical waste, waste management, and environmental pollution

Classifying distinct data types: textual streams protein sequences and genomic variants

Artificial Intelligence (AI) is an interdisciplinary field combining different research areas with the end goal to automate processes in the everyday life and industry. The fundamental components of AI models are an “intelligent” model and a functional component defined by the end-application. That is, an intelligent model can be a statistical model that can recognize patterns in data instances to distinguish differences in between these instances. For example, if the AI is applied in car manufacturing, based on an image of a part of a car, the model can categorize if the car part is in the front, middle or rear compartment of the car, as a human brain would do. For the same example application, the statistical model informs a mechanical arm, the functional component, for the current car compartment and the arm in turn assembles this compartment, of the car, based on predefined instructions, likely as a human hand would follow human brain neural signals. A crucial step of AI applications is the classification of input instances by the intelligent model. The classification step in the intelligent model pipeline allows the subsequent steps to act in similar fashion for instances belonging to the same category. We define as classification the module of the intelligent model, which categorizes the input instances based on predefined human-expert or data-driven produced patterns of the instances. Irrespectively of the method to find patterns in data, classification is composed of four distinct steps: (i) input representation, (ii) model building (iii) model prediction and (iv) model assessment. Based on these classification steps, we argue that applying classification on distinct data types holds different challenges. In this thesis, I focus on challenges for three distinct classification scenarios: (i) Textual Streams: how to advance the model building step, commonly used for static distribution of data, to classify textual posts with transient data distribution? (ii) Protein Prediction: which biologically meaningful information can be used in the input representation step to overcome the limited training data challenge? (iii) Human Variant Pathogenicity Prediction: how to develop a classification system for functional impact of human variants, by providing standardized and well accepted evidence for the classification outcome and thus enabling the model assessment step? To answer these research questions, I present my contributions in classifying these different types of data: temporalMNB: I adapt the sequential prediction with expert advice paradigm to optimally aggregate complementary distributions to enhance a Naive Bayes model to adapt on drifting distribution of the characteristics of the textual posts. dom2vec: our proposal to learn embedding vectors for the protein domains using self-supervision. Based on the high performance achieved by the dom2vec embeddings in quantitative intrinsic assessment on the captured biological information, I provide example evidence for an analogy between the local linguistic features in natural languages and the domain structure and function information in domain architectures. Last, I describe GenOtoScope bioinformatics software tool to automate standardized evidence-based criteria for pathogenicity impact of variants associated with hearing loss. Finally, to increase the practical use of our last contribution, I develop easy-to-use software interfaces to be used, in research settings, by clinical diagnostics personnel.Künstliche Intelligenz (KI) ist ein interdisziplinäres Gebiet, das verschiedene Forschungsbereiche mit dem Ziel verbindet, Prozesse im Alltag und in der Industrie zu automatisieren. Die grundlegenden Komponenten von KI-Modellen sind ein “intelligentes” Modell und eine durch die Endanwendung definierte funktionale Komponente. Das heißt, ein intelligentes Modell kann ein statistisches Modell sein, das Muster in Dateninstanzen erkennen kann, um Unterschiede zwischen diesen Instanzen zu unterscheiden. Wird die KI beispielsweise in der Automobilherstellung eingesetzt, kann das Modell auf der Grundlage eines Bildes eines Autoteils kategorisieren, ob sich das Autoteil im vorderen, mittleren oder hinteren Bereich des Autos befindet, wie es ein menschliches Gehirn tun würde. Bei der gleichen Beispielanwendung informiert das statistische Modell einen mechanischen Arm, die funktionale Komponente, über den aktuellen Fahrzeugbereich, und der Arm wiederum baut diesen Bereich des Fahrzeugs auf der Grundlage vordefinierter Anweisungen zusammen, so wie eine menschliche Hand den neuronalen Signalen des menschlichen Gehirns folgen würde. Ein entscheidender Schritt bei KI-Anwendungen ist die Klassifizierung von Eingabeinstanzen durch das intelligente Modell. Unabhängig von der Methode zum Auffinden von Mustern in Daten besteht die Klassifizierung aus vier verschiedenen Schritten: (i) Eingabedarstellung, (ii) Modellbildung, (iii) Modellvorhersage und (iv) Modellbewertung. Ausgehend von diesen Klassifizierungsschritten argumentiere ich, dass die Anwendung der Klassifizierung auf verschiedene Datentypen unterschiedliche Herausforderungen mit sich bringt. In dieser Arbeit konzentriere ich uns auf die Herausforderungen für drei verschiedene Klassifizierungsszenarien: (i) Textdatenströme: Wie kann der Schritt der Modellerstellung, der üblicherweise für eine statische Datenverteilung verwendet wird, weiterentwickelt werden, um die Klassifizierung von Textbeiträgen mit einer instationären Datenverteilung zu erlernen? (ii) Proteinvorhersage: Welche biologisch sinnvollen Informationen können im Schritt der Eingabedarstellung verwendet werden, um die Herausforderung der begrenzten Trainingsdaten zu überwinden? (iii) Vorhersage der Pathogenität menschlicher Varianten: Wie kann ein Klassifizierungssystem für die funktionellen Auswirkungen menschlicher Varianten entwickelt werden, indem standardisierte und anerkannte Beweise für das Klassifizierungsergebnis bereitgestellt werden und somit der Schritt der Modellbewertung ermöglicht wird? Um diese Forschungsfragen zu beantworten, stelle ich meine Beiträge zur Klassifizierung dieser verschiedenen Datentypen vor: temporalMNB: Verbesserung des Naive-Bayes-Modells zur Klassifizierung driftender Textströme durch Ensemble-Lernen. dom2vec: Lernen von Einbettungsvektoren für Proteindomänen durch Selbstüberwachung. Auf der Grundlage der berichteten Ergebnisse liefere ich Beispiele für eine Analogie zwischen den lokalen linguistischen Merkmalen in natürlichen Sprachen und den Domänenstruktur- und Funktionsinformationen in Domänenarchitekturen. Schließlich beschreibe ich ein bioinformatisches Softwaretool, GenOtoScope, zur Automatisierung standardisierter evidenzbasierter Kriterien für die orthogenitätsauswirkungen von Varianten, die mit angeborener Schwerhörigkeit in Verbindung stehen

Capturing protein domain structure and function using self-supervision on domain architectures

Predicting biological properties of unseen proteins is shown to be improved by the use of protein sequence embeddings. However, these sequence embeddings have the caveat that biological metadata do not exist for each amino acid, in order to measure the quality of each unique learned embedding vector separately. Therefore, current sequence embedding cannot be intrinsically evaluated on the degree of their captured biological information in a quantitative manner. We address this drawback by our approach, dom2vec, by learning vector representation for protein domains and not for each amino acid base, as biological metadata do exist for each domain separately. To perform a reliable quantitative intrinsic evaluation in terms of biology knowledge, we selected the metadata related to the most distinctive biological characteristics of a domain, which are its structure, enzymatic, and molecular function. Notably, dom2vec obtains an adequate level of performance in the intrinsic assessment—therefore, we can draw an analogy between the local linguistic features in natural languages and the domain structure and function information in domain architectures. Moreover, we demonstrate the dom2vec applicability on protein prediction tasks, by comparing it with state-of-the-art sequence embeddings in three downstream tasks. We show that dom2vec outperforms sequence embeddings for toxin and enzymatic function prediction and is comparable with sequence embeddings in cellular location prediction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Challenges and Adjustments of Healthcare and Labor Policies in Greece during the COVID-19 era: A Critical Assessment of the Key Social Policy Responses

The welfare state in Greece even before the outbreak of the global pandemic experienced multiple challenges and problems mainly as a result of its chronic structural, administrative, and financial problems which were further deteriorated by austerity measures. The pandemic that followed the ten-year economic crisis led to a new multifaceted crisis, adding further pressure on the National Health System as well as on the labor market, and precipitating the uptake of targeted measures and policies to support both the NHS with equipment, staff, and employment due to the imposition of national and local lockdowns. Confronted with such weaknesses, the establishment of a new welfare state would need to bear a higher degree of flexibility, inclusivity, and efficiency in order to live up to the increasing societal, health, and economic demands.  In this sense, the aim of this paper is to explore the variations in health and labor policies (two key pillars of the welfare state) during the COVID-19 pandemic and assess whether there is a need for further interventions with regard to the social security and prosperity of citizens

A Comparative Analysis of COVID-19 Effects on air Pollution in Ten EU Cities in 2020

The global pandemic has arguably induced many dramatic changes at all levels worldwide. The occurrence of some silver linings on the environment brought about a glimmer of hope and optimism. However, these are seen as rather short-lived and temporary mainly linked to lower economic output and the imposition of restrictive measures by the national governments to contain the spreading of the coronavirus. In such a context, the restart of the economy plausibly raises many concerns about the durability of those in the long run. An environmental sector that has attracted particular attention is air pollution which has seen significant improvements in urban centers and most polluted cities during the pandemic. Evidence shows that air pollution in the EU has decreased in 2020 as a result of reduced consumption of fossil fuels, road transport, lower economic output, and industrial activity, however, strong signs of retreat to pre-coronavirus levels are observed. The aim of this policy brief is to examine the effects of COVID-19 on air pollution by breaking down and comparing the average concentrations of three pollutants, nitrogen dioxide (NO2), and particulate matter (PM2.5), and (PM10), per month in ten major European cities in 2020 with the use of data from the European Environment Agency

Challenges and Adjustments of Healthcare and Labor Policies in Greece during the COVID-19 era: A Critical Assessment of the Key Social Policy Responses

The welfare state in Greece even before the outbreak of the global pandemic experienced multiple challenges and problems mainly as a result of its chronic structural, administrative, and financial problems which were further deteriorated by austerity measures. The pandemic that followed the ten-year economic crisis led to a new multifaceted crisis, adding further pressure on the National Health System as well as on the labor market, and precipitating the uptake of targeted measures and policies to support both the NHS with equipment, staff, and employment due to the imposition of national and local lockdowns. Confronted with such weaknesses, the establishment of a new welfare state would need to bear a higher degree of flexibility, inclusivity, and efficiency in order to live up to the increasing societal, health, and economic demands.  In this sense, the aim of this paper is to explore the variations in health and labor policies (two key pillars of the welfare state) during the COVID-19 pandemic and assess whether there is a need for further interventions with regard to the social security and prosperity of citizens

Διασκευές και επανεκτελέσεις στο «Φτωχό κομπολογάκι μου» του Γιώργου Μητσάκη. Διαφορετικές εκδοχές πάνω στο ίδιο υλικό.

Στην παρούσα εργασία, ένα τραγούδι γίνεται αφορμή ιστορικών και πολιτισμικών παρενθέσεων, επαγωγικής σκέψης, αναζήτηση ετερόκλητων ρεπερτορίων και εργαλείο για την κατανόηση της έννοιας της αισθητικής στην τέχνη και πιο συγκεκριμένα στην μουσική. Το “Φτωχό κομπολογάκι μου” του Γιώργου Μητσάκη είναι ένα τραγούδι που έχει διασκευαστεί πολλές φορές, σε διαφορετικές περιόδους από καλλιτέχνες που εκπροσωπούν διαφορετικά στυλ και απευθύνονται σε ανόμοια μεταξύ τους ακροατήρια. Ξεκινώντας από τον Μητσάκη και την εποχή του αστικού λαϊκού τραγουδιού της Αθήνας του 1940 το αναλυτικό μοντέλο που χρησιμοποιείται είναι: Στοιχεία από την βιογραφία του καλλιτέχνη, ιστορικά και πολιτισμικά γεγονότα της εποχής όπου παρατίθενται και εργαλειοποιούνται για την αισθητική ανάλυση που ακολουθεί, μουσικολογική ανάλυση και μια σύνοψη των παραπάνω στο πλαίσιο της αισθητικής. Οι διασκευές που εξετάζονται είναι του Μάνου Χατζιδάκι, της Λίτσα Διαμάντη και του Σταμάτη Κόκοτα. Σε κάθε περίπτωση η προσπάθεια που έγινε είναι μια αμερόληπτη, χωρίς την παρεμβολή του προσωπικού γούστου προσέγγιση στο κάθε πλαίσιο, με την ελπίδα πως αφορά μια ματιά που παρατηρεί πίσω από τα εμφανώς δοσμένα χαρακτηριστικά. Τελικά θα δούμε πως μια αισθητική τάση δεν προκύπτει μόνο από αυτούς που φαινομενικά την δημιουργούν, αλλά και από αυτούς που την υιοθετούν. Παρατίθενται απόψεις πάνω στο θέμα της αισθητικής και παρατηρούνται κάποιες άμεσες διεργασίες που μεταβάλλουν το περιεχόμενο της.In the present work, a song becomes an occasion for historical and cultural quotations, inductive thinking, search for diverse repertoires and a tool for understanding the concept of aesthetics in art and more specifically in music. "Ftwho kompologaki mou" by George Mitsakis is a song that has been adapted many times, in different periods by artists who represent different styles and are addressed to dissimilar audiences. Starting from Mitsakis and the era of the urban folk song of Athens in 1940, the analytical model used is: Elements from the artist's biography, historical and cultural events of the time where they are quoted and used for the aesthetic analysis that follows, musicological analysis and a summary of the above in the context of aesthetics. The covers under consideration are by Manos Hadjidakis, Litsa Diamanti and Stamatis Kokkotas. In any case, the effort made is an impartial, without the interference of personal taste approach in each context, with the hope that it concerns a perspective that observes behind the obviously given characteristics. Eventually we will see that an aesthetic trend arises not only from those who seemingly create it, but also from those who adopt it. Opinions are presented on the subject of aesthetics and some direct processes are observed that change its content

Begleitende Evaluierung IWB/EFRE AT 2014-20. Leistungspaket 1: Prioritätsachse 1 – Forschung, technologische Entwicklung und Innovation. Endbericht.

Die Geschäftsstelle der Österreichischen Raumordnungskonferenz (ÖROK GSt.) hat als Verwaltungsbehörde des österreichischen IWB/EFRE -Programms die Durchführung einer begleitenden Evaluierung des Programms beauftragt, die mit Zuschlagserteilung der Bundesbeschaffung GmbH vom 07.12.2017 an ein Konsortium bestehend aus ÖIR, convelop, KMUFA, ÖAR, ÖGUT sowie Spatial Foresight ging. Die Evaluierung wird in mehreren Leistungspaketen bearbeitet, die sich im Wesentlichen auf die Prioritätsachsen des Programms beziehen, ergänzt um Evaluierungen zu den Bereichen „Governance“, „Kommunikation“ und „Querschnittsthemen“. Die gegenständliche Evaluierung ist ein Bestandteil dieser begleitenden Evaluierung und befasst sich im Kern mit den Maßnahmen der Priorität 1 „Stärkung der regionalen Wettbewerbsfähigkeit durch Forschung, technologische Entwicklung und Innovation“, ergänzt um die Maßnahme 15 „F&I in CO2-Reduktionstechnologien“ (P3) sowie die Maßnahmen 16 „F&T-Infrastruktur (Wien)“ und 17 „Innovationsdienstleistungen (Wien)“ (beide PA4). Mit 226,4 Mio. € decken die behandelten Maßnahmen 42% der EFRE-Mittel des Gesamtprogramms ab. Die Evaluierung zeichnet insgesamt ein überwiegend positives Bild der FTI-Förderung im EFRE, das es gilt, auch in Zukunft aufrechtzuerhalten. Die Empfehlungen der FTI-Evaluierung zielen vor allem auf eine noch klarere Konzeption und Darstellung von FTI-Maßnahmen mit grundsätzlich unterschiedlichen Wirkungslogiken und eine Weiterführung bestehender Bemühungen zur Harmonisierung regional unterschiedlicher Abwicklungsmodi innerhalb dieser Maßnahmen ab. Daneben sollte der Anspruch der Anwendungsorientierung im Lichte der stark wahrgenommenen Grundlagenorientierung in der Praxis reflektiert werden. Auch im Bereich der Datenerfassung und -qualitätsicherung wurden Ansatzpunkte für Verbesserungen aufgezeigt