After more than two decades, bullying and cyberbullying is still negatively impacting the lives of many of our youth and their families. The prevalence of the phenomenon is widespread and part of the everyday life activities. The impact of cyber aggression and violation can have severe consequences, up to the destruction of lives. While cyberbullying prevention programs exist, not much progress seems to have been made in the effort to combat the phenomenon. This research provides new insights into how to extract information by using existing research and online news articles, with the aim to create new or improve existing cyberbullying prevention efforts. The intent is to inform prevention programs.

This work presents comprehensive analytics of trajectories of functional loss and recovery using sequence analysis and clustering techniques. The study focuses on a large dataset consisting of assessments of activities of daily living conducted among nursing home residents. The first main part of this research involves converting the assessments into sequences of disability combinations and utilizing graphical tools and various indicators to gain valuable insights into the trajectories of functional disabilities over time. In the second part of the research, a novel clustering approach is introduced that combines Markov models with distance-based techniques. This hybrid methodology results in 13 distinct clusters of trajectories. The clusters are thoroughly examined, and representative sets are carefully selected based on various criteria. This selection process ensures that the chosen sets accurately represent the characteristics of each cluster. The findings of this study have significant implications for healthcare systems, including developing predictive models which can be utilized to forecast the trajectory of individual patients based on their cluster membership. This enables healthcare providers to anticipate disease progression, tailor treatments, and dynamically adjust care plans, resulting in improved patient outcomes and the overall quality of care. Moreover, the information derived from the analytics can aid in optimizing healthcare systems by facilitating resource allocation and cost optimization. The insights gained can also guide policymakers and families in planning appropriate care for patients. This research advances healthcare decision-making and ensures appropriate support. / Thesis / Master of Science (MSc) / The ability to independently perform activities of daily living (ADLs) is a crucial indicator of an individual's health status, and the loss of this ability can have a profound impact on their overall quality of life. Our research focuses on analyzing the trajectories of patients as they experience functional decline and recovery. While various techniques have been utilized to explore ADL trajectories, this study stands out by employing clustering and sequence analysis approaches to examine different groups of trajectories. To overcome the computational challenges involved, we propose a combined clustering approach. This hybrid approach consists of two phases: applying a Markov model prior to distance-based algorithms. The findings derived from our research hold significant applications in optimizing healthcare systems, improving health outcomes, facilitating the development of targeted and effective interventions that support patients in preserving their independence, and enhancing the quality of care.

No description available.

The scope of my research is web analytics. This paper describes the process of usability analysis of the website belonging to a company Sharden Hus situated in Stockholm. From the many existing tools of web analysis I chose two the most popular ones, Google Analytics and Yandex Metrics. In similar projects that I have read, the website redesign was based on both quantitative, statistical, and qualitative (user interviews, user tests) data. In contrast to the previously carried out projects on websites improvement with the help of similar tools, I decided to base the changes on the website only on quantitative data obtained with Google and Yandex counters. This was done in order to determine whether and how Google and Yandex tools can improve the website performance. And to see if web analytics counters may provide with sufficient statistical data enough for it's correct interpretation by a web analytics designer which would lead to the improvement of the web site performance.The results of my study showed that Google and Yandex counters isolated from qualitative methods can improve the website performance. In particular, the number of visits from the territory of Sweden was increased to almost double; the overall bounce rate reduced; the number of visits to the page containing order forms significantly increased.

Most epidemiological systems described in the literature are built for evaluation and analysis of specific diseases, such as Influenza-like-illness. The modeling environments that support these systems are implemented for specific diseases and epidemiological models. Hence they are not reusable or extendable. This thesis focuses on the design and development of an integrated analytical environment with flexible data integration methodologies and multi-level web services for evaluation and forecasting of various epidemics in different regions of the world. The environment supports analysis of epidemics based on any combination of disease, surveillance sources, epidemiological models, geographic regions and demographic factors. The environment also supports evaluation and forecasting of epidemics when various policy-level and behavioral interventions are applied, that may inhibit the spread of an epidemic. First, we describe data integration methodologies and schema design, for flexible experiment design, storage and query retrieval mechanisms related to large scale epidemic data. We describe novel techniques for data transformation, optimization, pre-computation and automation that enable flexibility, extendibility and efficiency required in different categories of query processing. Second, we describe the design and engineering of adaptable middleware platforms based on service-oriented paradigms for interactive workflow, communication, and decoupled integration. This supports large-scale multi-user applications with provision for online analysis of interventions as well as analytical processing of forecast computations. Using a service-oriented architecture, we have provided a platform-as-a-service representation for evaluation and forecasting of epidemics. We demonstrate the applicability of our integrated environment through development of the applications, DISIMS and EpiCaster. DISIMS is an interactive web-based system for evaluating the effects of dynamic intervention strategies on epidemic propagation. EpiCaster is a situation assessment and forecasting tool for projecting the state of evolving epidemics such as flu and Ebola in different regions of the world. We discuss how our platform uses existing technologies to solve a novel problem in epidemiology, and provides a unique solution on which different applications can be built for analyzing epidemic containment strategies. / Ph. D.

Denna studie undersöker ett företags applicering av maskininlärning som ska användas till data analytics. Genom att undersöka detta är målet med studien att få en insikt i hur deras applicering och förståelse av området påverkar hur väl de lyckas applicera maskininlärning i företaget. Studien genomfördes genom att samla in empiriska data i form av intervjuer med utvalda intressenter. Dessa intervjuer fokuserade på att utforska de utmaningar och hinder som företag står inför när de försöker applicera maskininlärning i sina verksamheter. Samtidigt samlades teoretiska data in genom en genomgång av relevant forskning och litteratur inom området för att ge en bredare kontext och förståelse för de identifierade utmaningarna. Resultaten av studien identifierade att brist på erfarenhet och kompetens inom området, resursbegränsningar, otydliga mål med appliceringen av maskininlärningstekniker var de centrala faktorerna som påverkar företagets förmåga att applicera maskininlärning för data analytics. Denna studie bidrar till en ökad förståelse för de specifika utmaningar som företag möter när de strävar efter att använda maskininlärningstekniker i verksamheten. Genom att identifiera dessa utmaningar kan företag och forskare arbeta mot att utveckla strategier och lösningar för att övervinna dem och därmed främja en mer effektiv och framgångsrik applicering av maskininlärning i olika företagsmiljöer. / This study investigates a company's application of machine learning to be applied to data analytics. By examining this, the aim of the study is to gain insight into how their application and understanding of the field affect their success in implementing machine learning within the company. The study was conducted by collecting empirical data in the form of interviews with selected stakeholders. These interviews focused on exploring the challenges and obstacles that companies face when attempting to implement machine learning in their operations. At the same time, theoretical data was gathered through a review of relevant research and literature in the field to provide a broader context and understanding of the identified challenges. The results of the study identified that a lack of experience and expertise in the field, resource constraints, and unclear goals for the implementation of machine learning techniques were the central factors affecting the company's ability to implement machine learning for data analytics. This study contributes to an increased understanding of the specific challenges that companies encounter when striving to use machine learning techniques in their operations. By identifying these challenges, companies and researchers can work towards developing strategies and solutions to overcome them, thus promoting a more effective and successful application of machine learning in various corporate environments.

The U.S. health care system is considered costly and highly inefficient, devoting substantial resources to the treatment of acute conditions in a hospital setting rather than focusing on prevention and keeping patients out of the hospital. The potential for cost savings is large; in the U.S. more than $30 billion are spent each year on hospitalizations deemed preventable, 31% of which is attributed to heart diseases and 20% to diabetes. Motivated by this, our work focuses on developing centralized and distributed learning methods to predict future heart- or diabetes- related hospitalizations based on patient Electronic Health Records (EHRs). We explore a variety of supervised classification methods and we present a novel likelihood ratio based method (K-LRT) that predicts hospitalizations and offers interpretability by identifying the K most significant features that lead to a positive prediction for each patient. Next, assuming that the positive class consists of multiple clusters (hospitalized patients due to different reasons), while the negative class is drawn from a single cluster (non-hospitalized patients healthy in every aspect), we present an alternating optimization approach, which jointly discovers the clusters in the positive class and optimizes the classifiers that separate each positive cluster from the negative samples. We establish the convergence of the method and characterize its VC dimension. Last, we develop a decentralized cluster Primal-Dual Splitting (cPDS) method for large-scale problems, that is computationally efficient and privacy-aware. Such a distributed learning scheme is relevant for multi-institutional collaborations or peer-to-peer applications, allowing the agents to collaborate, while keeping every participant's data private. cPDS is proved to have an improved convergence rate compared to existing centralized and decentralized methods. We test all methods on real EHR data from the Boston Medical Center and compare results in terms of prediction accuracy and interpretability.

No description available.

Today’s dynamic and increasingly competitive market had expanded complexities for global businesses pressuring companies to start leveraging on Big Data solutions in order to sustain the global competitions by becoming more data-driven in managing their supply chains.The main purpose of this study is twofold, 1) to explore the implications of applying analytics designing supply chain networks, 2) to investigate the link between operational and strategic management levels when making strategic decisions using Analytics.Qualitative methods have been applied for this study to gain a greater understanding of the Supply Chain Analytics phenomenon. An inductive approach in form of interviews, was performed in order to gain new empirical data. Fifteen semi-structured interviews were conducted with professional individuals who hold managerial roles such as project managers, consultants, and end-users within the fields of Supply Chain Management and Big Data Analytics. The received empirical information was later analyzed using the thematic analysis method.The main findings in this thesis relatively contradicts with previous studies and existing literature in terms of connotations, definitions and applications of the three main types of Analytics. Furthermore, the findings present new approaches and perspectives that advanced analytics apply on both strategic and operational management levels that are shaping supply chain network designs.

Digital analytics kallas den samling teknologier som med olika teknikeranalyserar digitala kanaler (webbsidor, email och även offline data) för attsöka förståelse för kunders beteenden och intentioner. Digital Analytics harblivit en mycket viktig komponent till en stor del webbaserade systemmiljöer,där den stödjer och underlättar affärer och beslutsfattande för organisationer.Men hur väl tillämpas dessa teknologier och hur ser den digitalatransformationen ut som utspelar sig inom organisationer, och hur kan manmäta denna digitala mognadsprocess?Denna studie tillämpar en Design Science Research-approach för att uppfyllamålet om att utveckla en Digital Analytics Maturity Model (DAMM) lämpligför små till medelstora företag, varav en expertpanel bestående av 6 st ledandeforskare inom mognadsforskning och Digital Analytic är tillsatt i formen av enDelphi-undersökning. Resultaten från studien visar bl.a att organisatoriskaaspekter spelar en viktig roll för Digital Analytics samt att utvecklingen av enfunktionsduglig DAMM som är redo att tas i burk är möjligt.