559 research outputs found

    Management of Cloud systems applied to eHealth

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    This thesis explores techniques, models and algorithms for an efficient management of Cloud systems and how to apply them to the healthcare sector in order to improve current treatments. It presents two Cloud-based eHealth applications to telemonitor and control smoke-quitting and hypertensive patients. Different Cloud-based models were obtained and used to develop a Cloudbased infrastructure where these applications are deployed. The results show that these applications improve current treatments and that can be scaled as computing requirements grow. Multiple Cloud architectures and models were analyzed and then implemented using different techniques and scenarios. The Smoking Patient Control (S-PC) tool was deployed and tested in a real environment, showing a 28.4% increase in long-term abstinence. The Hypertension Patient Control (H-PC) tool, was successfully designed and implemented, and the computing boundaries were measuredAquesta tesi explora tèniques, models i algorismes per una gestió eficient en sistemes al Núvol i com aplicar-ho en el sector de la salut per tal de millorar els tractaments actuals. Presenta dues aplicacions de salut electrònica basades en el Núvol per telemonitoritzar i controlar pacients fumadors i hipertensos. S'ha obtingut diferents models basats en el Núvol i s'han utilitzat per a desenvolupar una infraestructura on desplegar aquestes aplicacions. Els resultats mostren que aquestes aplicacions milloren els tractaments actuals així com escalen a mesura que els requeriments computacionals augmenten. Múltiples arquitectures i models han estat analitzats i implementats utilitzant diferents tècniques i escenaris. L'aplicació Smoking Patient Control (S-PC) ha estat desplegada i provada en un entorn real, aconseguint un augment del 28,4% en l'absistinència a llarg termini de pacients fumadors. L'aplicació Hypertension Patient Control (H-PC) ha estat dissenyada i implementada amb èxit, i els seus límits computacionals han estat mesurats.Esta tesis explora ténicas, modelos y algoritmos para una gestión eficiente de sistemas en la Nube y como aplicarlo en el sector de la salud con el fin de mejorar los tratamientos actuales. Presenta dos aplicaciones de salud electrónica basadas en la Nube para telemonitorizar y controlar pacientes fumadores e hipertensos. Se han obtenido diferentes modelos basados en la Nube y se han utilizado para desarrollar una infraestructura donde desplegar estas aplicaciones. Los resultados muestran que estas aplicaciones mejoran los tratamientos actuales así como escalan a medida que los requerimientos computacionales aumentan. Múltiples arquitecturas y modelos han sido analizados e implementados utilizando diferentes técnicas y escenarios. La aplicación Smoking Patient Control (S-PC) se ha desplegado y provado en un entorno real, consiguiendo un aumento del 28,4% en la abstinencia a largo plazo de pacientes fumadores. La aplicación Hypertension Patient Control (H-PC) ha sido diseñada e implementada con éxito, y sus límites computacionales han sido medidos

    Cyber Ethics 4.0 : Serving Humanity with Values

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    Cyber space influences all sectors of life and society: Artificial Intelligence, Robots, Blockchain, Self-Driving Cars and Autonomous Weapons, Cyberbullying, telemedicine and cyber health, new methods in food production, destruction and conservation of the environment, Big Data as a new religion, the role of education and citizens’ rights, the need for legal regulations and international conventions. The 25 articles in this book cover the wide range of hot topics. Authors from many countries and positions of international (UN) organisations look for solutions from an ethical perspective. Cyber Ethics aims to provide orientation on what is right and wrong, good and bad, related to the cyber space. The authors apply and modify fundamental values and virtues to specific, new challenges arising from cyber technology and cyber society. The book serves as reading material for teachers, students, policy makers, politicians, businesses, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and solution

    Methods to Improve the Prediction Accuracy and Performance of Ensemble Models

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    The application of ensemble predictive models has been an important research area in predicting medical diagnostics, engineering diagnostics, and other related smart devices and related technologies. Most of the current predictive models are complex and not reliable despite numerous efforts in the past by the research community. The performance accuracy of the predictive models have not always been realised due to many factors such as complexity and class imbalance. Therefore there is a need to improve the predictive accuracy of current ensemble models and to enhance their applications and reliability and non-visual predictive tools. The research work presented in this thesis has adopted a pragmatic phased approach to propose and develop new ensemble models using multiple methods and validated the methods through rigorous testing and implementation in different phases. The first phase comprises of empirical investigations on standalone and ensemble algorithms that were carried out to ascertain their performance effects on complexity and simplicity of the classifiers. The second phase comprises of an improved ensemble model based on the integration of Extended Kalman Filter (EKF), Radial Basis Function Network (RBFN) and AdaBoost algorithms. The third phase comprises of an extended model based on early stop concepts, AdaBoost algorithm, and statistical performance of the training samples to minimize overfitting performance of the proposed model. The fourth phase comprises of an enhanced analytical multivariate logistic regression predictive model developed to minimize the complexity and improve prediction accuracy of logistic regression model. To facilitate the practical application of the proposed models; an ensemble non-invasive analytical tool is proposed and developed. The tool links the gap between theoretical concepts and practical application of theories to predict breast cancer survivability. The empirical findings suggested that: (1) increasing the complexity and topology of algorithms does not necessarily lead to a better algorithmic performance, (2) boosting by resampling performs slightly better than boosting by reweighting, (3) the prediction accuracy of the proposed ensemble EKF-RBFN-AdaBoost model performed better than several established ensemble models, (4) the proposed early stopped model converges faster and minimizes overfitting better compare with other models, (5) the proposed multivariate logistic regression concept minimizes the complexity models (6) the performance of the proposed analytical non-invasive tool performed comparatively better than many of the benchmark analytical tools used in predicting breast cancers and diabetics ailments. The research contributions to ensemble practice are: (1) the integration and development of EKF, RBFN and AdaBoost algorithms as an ensemble model, (2) the development and validation of ensemble model based on early stop concepts, AdaBoost, and statistical concepts of the training samples, (3) the development and validation of predictive logistic regression model based on breast cancer, and (4) the development and validation of a non-invasive breast cancer analytic tools based on the proposed and developed predictive models in this thesis. To validate prediction accuracy of ensemble models, in this thesis the proposed models were applied in modelling breast cancer survivability and diabetics’ diagnostic tasks. In comparison with other established models the simulation results of the models showed improved predictive accuracy. The research outlines the benefits of the proposed models, whilst proposes new directions for future work that could further extend and improve the proposed models discussed in this thesis

    Multi-Agent Systems

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    This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

    A Psychosocial Behavioral Attribution Model: Examining the Relationship Between the “Dark Triad” and Cyber-Criminal Behaviors Impacting Social Networking Sites

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    This study proposes that individual personality characteristics and behavioral triggering effects come together to motivate online victimization. It draws from psychology’s current understanding of personality traits, attribution theory, and criminological research. This study combines the current computer deviancy and hacker taxonomies with that of the Dark Triad model of personality mapping. Each computer deviant behavior is identified by its distinct dimensions of cyber-criminal behavior (e.g., unethical hacking, cyberbullying, cyberstalking, and identity theft) and analyzed against the Dark Triad personality factors (i.e., narcissism, Machiavellianism, and psychopathy). The goal of this study is to explore whether there are significant relationships among the Dark Triad personality traits and specific cyber-criminal behaviors within social network sites (SNSs). The study targets offensive security engineers and computer deviants from specific hacker conferences and from websites that discuss or promote computer deviant behavior (e.g., hacking). Additional sampling is taken from a general population of SNS users. Using a snowball sampling method, 235 subjects completed an anonymous, self-report survey that includes items measuring computer deviance, personality traits, and demographics. Results yield that there was no significant relationship between Dark Triad and cyber-criminal behaviors defined in the perceived hypotheses. The final chapter of the study summarizes the results and discusses the mechanisms potentially underlying the findings. In the context of achieving the latter objective, exploratory analyses are incorporated and partly relied upon. It also includes a discussion concerning the implications of the findings in terms of providing theoretical insights on the Dark Triad traits and cyber-criminal behaviors more generally

    Systemic Social Media Regulation

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    Social media platforms are motivated by profit, corporate image, long-term viability, good citizenship, and a desire for friendly legal environments. These managerial interests stand in contrast to the gubernatorial interests of the state, which include the promotion of free speech, the development of e-commerce, various counter terrorism initiatives, and the discouragement of hate speech. Inasmuch as managerial and gubernatorial interests overlap, a self-regulation model of platform governance should prevail. Inasmuch as they diverge, regulation is desirable when its benefits exceed its costs. An assessment of the benefits and costs of social media regulation should account for how social facts, norms, and falsehoods proliferate. This Article sketches a basic economic model. What emerges from the analysis is that the quality of discourse cannot be controlled through suppression of content, or even disclosure of source. A better approach is to modify, in a manner conducive to discursive excellence, the structure of the forum. Optimal platform architecture should aim to reduce the systemic externalities generated by the social interactions that they enable, including the social costs of unlawful interference in elections and the proliferation of hate speech. Simultaneously, a systemic approach to social media regulation implies fewer controls on user behavior and content creation, and attendant First Amendment complications. Several examples are explored, including algorithmic newsfeeds, online advertising, and invited campus speakers
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