Sökning: "Panagiotis Papapetrou"

Visar resultat 1 - 5 av 6 avhandlingar innehållade orden Panagiotis Papapetrou.

  1. 1. Stochastic Modeling and Management of an Emergency Call Center : A Case Study at the Swedish Emergency CallCenter Provider, SOS Alarm Sverige AB

    Författare :Klas Gustavsson; Leif Olsson; Panagiotis Papapetrou; Mittuniversitetet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Call Center Management; Burst Modeling; Stochastic Resources; Skills-Based Routing; Discrete-Event-Simulation;

    Sammanfattning : A key task of managing an inbound call center is in estimating its performance and consequently plan its capacity, which can be considered a complex task since several system variables are stochastic. These issues are highly crucial for certain time-sensitive services, such as emergency call services. LÄS MER

  3. 2. Advancing Automation in Digital Forensic Investigations

    Författare :Irvin Homem; Panagiotis Papapetrou; Fredrik Blix; Indre Žliobaitė; Stockholms universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Digital Forensics; Machine Learning; Computer Forensics; Network Forensics; Predictive Modelling; Distributed Systems; Mobile Devices; Mobile Forensics; Memory Forensics; Android; Semantic Web; Hypervisors; Virtualization; Remote Acquisition; Evidence Analysis; Correlation; P2P; Bittorrent; Computer and Systems Sciences; data- och systemvetenskap;

    Sammanfattning : Digital Forensics is used to aid traditional preventive security mechanisms when they fail to curtail sophisticated and stealthy cybercrime events. The Digital Forensic Investigation process is largely manual in nature, or at best quasi-automated, requiring a highly skilled labour force and involving a sizeable time investment. LÄS MER

  4. 3. Towards Automation in Digital Investigations : Seeking Efficiency in Digital Forensics in Mobile and Cloud Environments

    Författare :Irvin Homem; Theo Kanter; Panagiotis Papapetrou; Rahim Rahmani; Fredrik Björck; Stockholms universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Computer forensics; network forensics; mobile devices; mobile forensics; cloud computing; semantic web; hypervisors; virtualization; remote acquisition; automation; evidence analysis; correlation; P2P; bittorrent; datalogi; Computer Science; informationssäkerhet; Information Systems Security;

    Sammanfattning : Cybercrime and related malicious activity in our increasingly digital world has become more prevalent and sophisticated, evading traditional security mechanisms. Digital forensics has been proposed to help investigate, understand and eventually mitigate such attacks. LÄS MER

  5. 4. Z-Series : Mining and learning from complex sequential data

    Författare :Zed Lee; Panagiotis Papapetrou; Tony Lindgren; Toon Calders; Stockholms universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; data- och systemvetenskap; Computer and Systems Sciences;

    Sammanfattning : The amount and complexity of sequential data collected across various domains have grown rapidly, posing significant challenges for extracting useful knowledge from such data sources. The complexity arises from diverse sequence representations with varying granularities, such as multivariate time series, histogram snapshots, and heterogeneous health records, which often describe a single data instance with multiple sequences. LÄS MER

  7. 5. Learning from Complex Medical Data Sources

    Författare :Jonathan Rebane; Panagiotis Papapetrou; Isak Samsten; Myra Spiliopoulou; Stockholms universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Data Science; Healthcare; Complex Data; Explainable AI; Deep Learning; data- och systemvetenskap; Computer and Systems Sciences;

    Sammanfattning : Large, varied, and time-evolving data sources can be observed across many domains and present a unique challenge for classification problems, in which traditional machine learning approaches must be adapted to accommodate for the complex nature of such data. Across most domains, there is also a need for machine learning models that are both well-performing and interpretable, to help provide explanations of a model's decisions that stakeholders can trust and take appropriate actions with. LÄS MER