Relevant bibliographies by topics / Intelligent Workflows

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intelligent Workflows'

Author: Grafiati
Published: 7 June 2025
Last updated: 11 July 2025

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Journal articles on the topic "Intelligent Workflows"

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Buell, Catherine A., Yolanda Gil, William P. Seeley, and Ricky J. Sethi. "Intelligent workflows for visual stylometry." AI Matters 3, no. 4 (2018): 14–17. http://dx.doi.org/10.1145/3175502.3175507.

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Guan, Wen, Tadashi Maeno, Brian Paul Bockelman, et al. "An intelligent Data Delivery Service for and beyond the ATLAS experiment." EPJ Web of Conferences 251 (2021): 02007. http://dx.doi.org/10.1051/epjconf/202125102007.

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The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. iDDS has been designed to intelligently orchestrate workflow and data management systems, decoupling data pre-processing, delivery, and main processing in various workflows. It is an experiment-agnostic service around a workflow-oriented structure to work with existing and emerging use cases in ATLAS and other experiments. Here we will present the motivation for iDDS, its design schema and architecture, use cases and current status, and plans for the future.
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Lin, Shiyu. "Research on the Impact of Artificial Intelligence Applications on the Workflow of Advertising Agencies." Advances in Economics, Management and Political Sciences 117, no. 1 (2024): 106–10. http://dx.doi.org/10.54254/2754-1169/117/20242031.

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Abstract: At present, AI has gradually penetrated various industries, but there is little application in the advertising industry. This study aims to understand how the introduction of AI will affect advertising agencies' workflows and develop strategies for practical AI applications. Before this, much research has also discussed the impact of AI on marketing, but rarely from the perspective of the workflow of advertising agencies. This study analyzes the advertising agencies' workflows and how they will change after the introduction of AI by using a literature review approach. A review and summary of many current studies culminates in the impact of AI on workflows and recommended strategies. The results show that AI affects workflows in four areas: market research, creative attributes, creative production, and intelligent advertising. The study recommends that advertising agencies prioritize experimenting with AI in both market research and the discovery of creative attributes.
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Palanki, Vijaya Chaitanya. "AUGMENTING DATA SCIENCE WORKFLOWS: A COMPREHENSIVE ANALYSIS OF AI-DRIVEN PRODUCTIVITY ENHANCEMENTS." International Journal of Core Engineering & Management 5, no. 6 (2018): 89–95. https://doi.org/10.5281/zenodo.14064200.

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The rapid growth of artificial intelligence (AI) technologies has significantly impacted various fields, including data science. This paper explores how AI can enhance productivity in the data science workflow, from data collection and preprocessing to model development and deployment. We review key AI technologies such as automated machine learning, intelligent data cleaning, and AI-assisted coding; discussing their potential to streamline data science tasks and improve efficiency. Additionally, we examine the challenges and limitations of integrating AI into data science workflows, as well as future research directions. This comprehensive review aims to provide data scientists, researchers, and practitioners with insights into leveraging AI for increased productivity in data science projects.
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Mahaboob Subhani Shaik. "Impact of AI on Enterprise Cloud-Based Integrations and Automation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1393–401. https://doi.org/10.32628/cseit241061180.

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Artificial Intelligence has transformed enterprise cloud-based integrations and automation, revolutionizing how businesses manage data, workflows, and applications across distributed environments. This comprehensive article explores the impact of AI on enterprise systems, examining key areas, including intelligent data integration, automated workflow optimization, and enhanced security measures. The article delves into technical implementation considerations, discussing infrastructure requirements and integration architectures while highlighting the substantial business benefits in operational efficiency, cost optimization, and strategic advantages. Additionally, it addresses the critical challenges organizations face in technical and organizational dimensions when implementing AI solutions, providing insights into successful adoption strategies and future considerations for enterprise AI integration.
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Bala, Anju, and Inderveer Chana. "Intelligent failure prediction models for scientific workflows." Expert Systems with Applications 42, no. 3 (2015): 980–89. http://dx.doi.org/10.1016/j.eswa.2014.09.014.

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Favour Uche Ojika, Wilfred Oseremen Owobu, Olumese Anthony Abieba, Oluwafunmilayo Janet Esan, Bright Chibunna Ubamadu, and Andrew Ifesinachi Daraojimba. "AI-Enhanced Knowledge Management Systems: A Framework for Improving Enterprise Search and Workflow Automation through NLP and TensorFlow." Computer Science & IT Research Journal 6, no. 3 (2025): 201–30. https://doi.org/10.51594/csitrj.v6i3.1884.

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In the era of digital transformation, organizations are increasingly adopting artificial intelligence (AI) to enhance knowledge management systems (KMS) and gain a competitive edge. This paper proposes a novel framework for AI-enhanced knowledge management that leverages Natural Language Processing (NLP) and TensorFlow to improve enterprise search capabilities and workflow automation. Traditional KMS often struggle with unstructured data, inefficient information retrieval, and fragmented workflows, leading to reduced productivity and decision-making inefficiencies. By integrating advanced NLP algorithms with TensorFlow’s scalable machine learning capabilities, the proposed framework addresses these challenges through intelligent content classification, semantic search, and automated knowledge extraction. The framework begins with data ingestion from diverse sources, including emails, reports, and databases, which are processed using NLP techniques such as named entity recognition, sentiment analysis, and topic modeling. TensorFlow models are then employed to train and fine-tune neural networks for document classification and intent recognition, enabling contextual understanding and prioritization of enterprise content. The system supports a dynamic knowledge graph that interlinks related concepts, documents, and workflows, facilitating real-time, query-responsive search and content recommendation. Moreover, the framework incorporates workflow automation by integrating AI models that identify repetitive tasks and suggest optimized processes using predictive analytics. This reduces manual effort, enhances task routing, and supports intelligent alerts and decision support mechanisms. A case study in a mid-sized enterprise demonstrates a 35% improvement in knowledge retrieval time and a 28% reduction in workflow execution delays after implementation. The proposed AI-enhanced KMS offers a scalable, adaptive solution for managing organizational knowledge in real-time, thus supporting knowledge workers with timely, relevant, and context-aware insights. It emphasizes the role of NLP for linguistic comprehension and TensorFlow for deep learning-based model optimization, providing a robust foundation for future enterprise intelligence systems. The research contributes to the growing field of AI in enterprise settings, highlighting the potential of integrated technologies to redefine knowledge access and operational efficiency. Keywords: Artificial Intelligence, Knowledge Management Systems, Enterprise Search, Workflow Automation, Natural Language Processing, TensorFlow, Semantic Search, Knowledge Graph, Machine Learning, Information Retrieval.
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Venkateswara Reddi Cheruku. "AI-orchestrated workflow automation in cloud-based hospital information systems: Enhancing efficiency and patient outcomes." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1544–54. https://doi.org/10.30574/wjarr.2025.26.2.1763.

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This technical article explores the integration of artificial intelligence technologies into enterprise-grade Hospital Information Systems and Electronic Medical Record platforms to automate clinical and administrative workflows. As healthcare organizations face increasing pressure to improve operational efficiency while enhancing patient care quality, AI-orchestrated workflow automation emerges as a transformative approach. The article examines the technical architecture, implementation challenges, and measurable benefits of these systems, highlighting successful deployments across various healthcare settings through detailed case studies. From intelligent triage to revenue cycle optimization, these AI-enabled systems demonstrate significant potential to reduce administrative burden, enhance clinical decision-making, and improve patient outcomes while addressing longstanding inefficiencies in healthcare delivery.
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Rama Krishna Debbadi and Obed Boateng. "Developing intelligent automation workflows in Microsoft power automate by embedding deep learning algorithms for real-time process adaptation." International Journal of Science and Research Archive 14, no. 2 (2025): 802–20. https://doi.org/10.30574/ijsra.2025.14.2.0449.

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The advent of intelligent automation has revolutionized business processes by integrating artificial intelligence (AI) with robotic process automation (RPA) to enable adaptive, efficient, and data-driven decision-making. Microsoft Power Automate, a widely used low-code automation platform, offers a powerful environment for developing intelligent workflows. However, traditional automation lacks dynamic decision-making capabilities, which can be significantly enhanced by embedding deep learning algorithms. This integration enables real-time process adaptation, allowing workflows to learn from historical data, predict outcomes, and make proactive adjustments without human intervention. This study explores the impact of embedding deep learning models within Power Automate workflows to enhance real-time process adaptability. By leveraging Azure Machine Learning and AI Builder, businesses can deploy deep neural networks for tasks such as anomaly detection, demand forecasting, sentiment analysis, and intelligent document processing. The research presents real-world applications across industries, including predictive maintenance in manufacturing, customer sentiment-driven automation in retail, and fraud detection in financial services. Challenges such as model deployment complexities, latency in real-time inference, and the need for seamless integration between AI services and Power Automate are also analyzed. Strategies for overcoming these challenges, such as optimizing model performance, leveraging cloud-based AI services, and ensuring scalable automation architectures, are proposed. The findings suggest that embedding deep learning models into Microsoft Power Automate can drive significant improvements in process efficiency, decision accuracy, and operational resilience, ultimately enabling businesses to achieve higher levels of automation intelligence and competitiveness.
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Rama Krishna Debbadi and Obed Boateng. "Enhancing cognitive automation capabilities with reinforcement learning techniques in robotic process automation using UiPath and automation anywhere." International Journal of Science and Research Archive 14, no. 2 (2025): 733–52. https://doi.org/10.30574/ijsra.2025.14.2.0450.

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Cognitive automation represents the next frontier in Robotic Process Automation (RPA), enabling systems to learn, adapt, and optimize decision-making processes dynamically. Traditional RPA platforms, such as UiPath and Automation Anywhere, excel in automating rule-based tasks but lack the ability to handle complex, evolving scenarios that require adaptive intelligence. Integrating reinforcement learning (RL) techniques into RPA workflows offers a transformative approach to enhancing cognitive automation capabilities. RL enables bots to make intelligent, data-driven decisions by learning from their environment, optimizing workflows, and improving operational efficiency over time. This study explores the integration of RL algorithms within UiPath and Automation Anywhere to develop self-learning automation systems capable of handling non-deterministic processes. Key applications include intelligent exception handling, dynamic process optimization, and adaptive customer service automation. By leveraging RL-based decision models, RPA bots can continuously improve their performance, reduce error rates, and optimize workflows beyond predefined rules. The research also examines challenges such as computational complexity, model interpretability, and integration barriers within enterprise automation environments. Solutions such as cloud-based reinforcement learning frameworks, hybrid AI-RPA architectures, and explainable AI techniques are proposed to mitigate these challenges. The findings indicate that reinforcement learning can significantly enhance cognitive automation in RPA, enabling businesses to achieve higher levels of efficiency, adaptability, and intelligent decision-making.
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Dissertations / Theses on the topic "Intelligent Workflows"

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Obeso, Duque Aleksandra. "Performance Prediction for Enabling Intelligent Resource Management on Big Data Processing Workflows." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372178.

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Mobile cloud computing offers an augmented infrastructure that allows resource-constrained devices to use remote computational resources as an enabler for highly intensive computation, thus improving end users experience. Being able to efficiently manage cloud elasticity represents a big challenge for dynamic resource scaling on-demand. In this sense, the development of intelligent tools that could ease the understanding of the behavior of a highly dynamic system and to detect resource bottlenecks given certain service level constrains represents an interesting case of study. In this project, a comparative study has been carried out for different distributed services taking into account the tools that are available for load generation, benchmarking and sensing of key performance indicators. Based on that, the big data processing framework Hadoop Mapreduce, has been deployed as a virtualized service on top of a distributed environment. Experiments for different cluster setups using different benchmarks have been conducted on this testbed in order to collect traces for both resource usage statistics at the infrastructure level and performance metrics at the platform level. Different machine learning approaches have been applied on the collected traces, thus generating prediction and classification models whose performance is then evaluated and compared. The highly accurate results, namely a Normalized Mean Absolute Error below 10.3% for the regressor and an accuracy score above 99.9% for the classifier, show the feasibility of the prediction models generated for service performance prediction and resource bottleneck detection that could be further used to trigger auto-scaling processes on cloud environments under dynamic loads in order to fulfill service level requirements.
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Fourli-Kartsouni, Florendia. "Intelligent workflow support for context sensitive business process modelling." Saarbrücken VDM Verlag Dr. Müller, 2004. http://d-nb.info/99121773X/04.

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Cheung, Yee Chung. "Compliance flow : an intelligent workflow management system to support engineering processes." Thesis, Loughborough University, 2003. https://dspace.lboro.ac.uk/2134/35616.

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This work is about extending the scope of current workflow management systems to support engineering processes. On the one hand engineering processes are relatively dynamic, and on the other their specification and performance are constrained by industry standards and guidelines for the sake of product acceptability, such as IEC 61508 for safety and ISO 9001 for quality. A number of technologies have been proposed to increase the adaptability of current workflow systems to deal with dynamic situations. A primary concern is how to support open-ended processes that cannot be completely specified in detail prior to their execution. A survey of adaptive workflow systems is given and the enabling technologies are discussed. Engineering processes are studied and their characteristics are identified and discussed. Current workflow systems have been successfully used in managing "administrative" processes for some time, but they lack the flexibility to support dynamic, unpredictable, collaborative, and highly interdependent engineering processes.
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Guo, Hanwen. "Workflow resource pattern simulation and visualization." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/65502/1/Hanwen_Guo_Thesis.pdf.

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This thesis addresses the process simulation and validation in Business Process Management. It proposes that the hybrid Multi Agent System (MAS) / 3D Virtual World approach is a valid method for better simulating the behaviour of human resources in business processes, supporting a wide range of rich visualization applications that can facilitate communication between business analysts and stakeholders. It is expected that the findings of this thesis may be fruitfully extended from BPM to other application domains, such as social simulation in video games and computer-based training animations.
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Romeo, Marco. "Automated processes and intelligent tools in CG media production." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/373915.

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La producció moderna dels mitjans descansa de forma important sobre continguts generats per ordinador, tals com animacions 3D, i efectes visuals digitals. Aquests actius complexos poblen videojocs, pel•lícules, televisió, dispositius mòbils i internet, però crear-los encara és una feina complexa, que requereix molta intervenció humana, té un flux de treball complex i no estàndard, procliu als errors, i demana un esforç important. Aquesta tesi contribueix en dues àrees de contingut generat per ordinador: la investigació inicial es dirigeix a l’automatització de “rigging”, de l’expressió facial emocional i de moviments expressius, i de la generació semiautomàtica de “clips”; a la investigació més recent, basada en un treball extens amb la indústria, la tesi defineix un flux genèric de producció de mitjans generats per ordinador, inclosos processos de producció i un model de “pipeline”, i, a partir d’aquest flux, es mostren i es discuteixen unes aproximacions a l’automatització dels passos més crítics des d’una perspectiva tant industrial com acadèmica. En resum, la tesi contribueix amb un conjunt d’algorismes i eines “familiaritzats” amb el “pipeline”, que ajuden l’usuari en el procés de producció, és a dir, eines intel•ligents.<br>Modern media production heavily relies on computer generated content, such as 3D animations and digital visual effects. Those complex assets populate videogames, films, television, mobile devices and the Internet, but their creation is still a complex task requiring a lot of human intervention, and with non standard complex workflows, which are prone to errors, and demand more production effort. The thesis contributes in two main areas of computer generated content: the oldest research is geared towards the automation of rigging, of emotional facial expression and emotional movements; and towards semi-automated generation of clips; in the most recent research, based on extensive work with the industry, the thesis defines a generic computer generated media production workflow, including production processes and a sample pipeline, and starting from this workflow, approaches to automate the most critical steps are shown and discussed with both scientific and industry eyes. In summary, this thesis contributes with a series of algorithms and tools that are “aware” of the pipeline, and assist the user in the production process, thus: intelligent tools.<br>La producción moderna de los medios descansa de forma importante en contenidos generados por ordenador, tales como animaciones 3D, y efectos visuales digitales. Estos activos complejos pueblan los videojuegos, películas, televisión, dispositivos móviles e internet, pero crearlos es aún una faena compleja, que demanda mucha intervención humana, implica un flujo de trabajo complejo no estándar, proclive a errores, y exige un esfuerzo importante. Esta tesi contribuye en dos áreas de contenido generado mediante ordenador: la investigación inicial se dirige a la automatización del “rigging”, de la expresión facial emocional i de movimientos expresivos, i de la generación semiautomática de “clips”; en la investigación más reciente, basada en un trabajo extenso en la industria, la tesis define un flujo genérico de producción de medios generados mediante ordenador, incluyendo procesos de producción y un modelo de “pipeline”, y, a partir de este flujo, se muestran y discuten unas aproximaciones a la automatización de los pasos más críticos desde una perspectiva tanto industrial como académica. En resumen, la tesis contribuye con un conjunto de algoritmos y herramientas “familiarizados” con el “pipeline”, que ayudan al usuario en el proceso de producción, es decir, unas herramientas inteligentes.
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Hachicha, Rim. "Modélisation et analyse de la flexibilité dans les systèmes workflow." Paris, CNAM, 2007. http://www.theses.fr/2007CNAM0565.

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Cette thèse est consacrée à la modélisation formelle et la gestion des systèmes workflows. Nous nous intéressons à apporter une solution à l'un des principaux problèmes des systèmes workflows est celui de la flexibilité : les modèles aussi bien que les systèmes actuels ne sont pas suffisamment flexibles et adaptables. Dans ce but, nous proposons un modèle de tâches et d'acteurs précisant les relations formelles entre les tâches workflow et les acteurs et permettant une assignation flexible des acteurs ux activités workflow. L'allocation des tâches workflows est fondée sur le concept de distance acteur/tâche et sur le processus de formation de coalitions d'agents. Le modèle permet de vérifier l'interchangeabilité des acteurs et la cohérence des tâches workflows suite à l'évolution de l'environnement. Nous proposons une architecture orientée agent distribuée intégrant le modèle formel et permettant de réaliser les fonctionnalités requises par les systèmes workflows. Cette architecture est capable de s'adapter de façon réactive aux changements tout en assurant la réutilisabilité du système Workflow. Nous avons implémenté le modèle sur la plate forme JADE en utilisant le système expert JESS et nous l'avons validé sur une réelle application<br>This thesis is devoted to formal modeling and management of workflow system. We are interested to bring a solution to the one of the principal problems of workflow systems is that of flexibility : the models as well as the current systems are not sufficiently flexible and adaptable. For these requirements, we propose a task model and actor model specifying the formal relations between workflow tasks and actors and allowing a flexible assignment of actors to workflow activities. The workflow task allocation is based on the concept of actor/task distance and agent coalition formation process. The model allows checking the interchangeability of the actors and the coherence of the workflows tasks following the evolution of the environment. We propose a distributed agent architecture integrating the formal model and permitting to carry out the functionalities required by the workflow system. This architecture is adaptable, reactive and ensures the reusability of the workflow system. We implemented the proposed model on JADE agent platform using expert system JESS and we validated our model on a real application
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Reul, Christian [Verfasser], Frank [Gutachter] Puppe, and Marcus [Gutachter] Liwicki. "An Intelligent Semi-Automatic Workflow for Optical Character Recognition of Historical Printings / Christian Reul ; Gutachter: Frank Puppe, Marcus Liwicki." Würzburg : Universität Würzburg, 2020. http://d-nb.info/1215500882/34.

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Lopes, Robson da Silva. "Planejamento instrucional adaptativo usando Workflow e planejamento genético." Universidade Federal de Uberlândia, 2009. https://repositorio.ufu.br/handle/123456789/12483.

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Fundação de Amparo a Pesquisa do Estado de Minas Gerais<br>The course in distance learning can be customized taking in to account the specific characteristic of each student. This means that the learning environment has adaptativity. So, in order to develop adaptative systems of distance learning, it is necessary to consider the problems: instructional planning and the student model. The first problem allows to generate a specific sequence of content for each student and the second one provides the necessary information to cope with adaptativity. Planning techniques in Artificial Intelligence have been successfully used for determining the sequence of instructional actions. The Workflow technology has been used for the management of these systems. Then, this work presents an adaptative instructional planning and a student model based on Taxonomy of Educational Objectives and learning styles.<br>Muitos sistemas de educação à distância não levam em consideração características particulares do estudante, utilizam as mesmas estratégias pedagógicas e seqüencias de conteúdo para todos os estudante. No entanto, ambiente de educação à distância devem introduzir adaptatividade como uma das suas principais características. Para que isto seja possível, deve-se considerar os problemas de planejamento instrucional e definição modelo do estudante. O primeiro problema permite gerar uma sequência de conteúdo específica para cada estudante e o segundo problema prove informações necessárias para lidar com adaptatividade. Técnicas de planejamento em Inteligência Artificial têm sido utilizada para determinar a sequência de ações instrucionais. A tecnologia de Workflow têm sido utilizada para gerenciar estes sistemas. Por tanto, este trabalho apresentar um sequenciador de conteúdo adaptativo, que utiliza algoritmos genéticos e um modelo do estudante baseado taxionomia do objetivos educacionais e em estilos de aprendizagem.<br>Mestre em Ciência da Computação
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Rabenius, Michaela. "Deep Learning-based Lung Triage for Streamlining the Workflow of Radiologists." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160537.

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The usage of deep learning algorithms such as Convolutional Neural Networks within the field of medical imaging has grown in popularity over the past few years. In particular, these types of algorithms have been used to detect abnormalities in chest x-rays, one of the most commonly performed type of radiographic examination. To try and improve the workflow of radiologists, this thesis investigated the possibility of using convolutional neural networks to create a lung triage to sort a bulk of chest x-ray images based on a degree of disease, where sick lungs should be prioritized before healthy lungs. The results from using a binary relevance approach to train multiple classifiers for different observations commonly found in chest x-rays shows that several models fail to learn how to classify x-ray images, most likely due to insufficient and/or imbalanced data. Using a binary relevance approach to create a triage is feasible but inflexible due to having to handle multiple models simultaneously. In future work it would therefore be interesting to further investigate other approaches, such as a single binary classification model or a multi-label classification model.
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Pla, Planas Albert. "Multi-attribute auctions: application to workflow management systems." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/134731.

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Resource and task allocation for workflows poses an allocation problem in which several attributes may be involved (economic cost, delivery time, CO2 emissions...), therefore, it must be treated from a multi-criteria perspective so that all of the attributes are taken into account when deciding the optimal assignments. Auction mechanisms offer the chance to allocate resources and services in a competitive market environment whilst optimizing outcomes for all of the participants. In this thesis, we propose the use of multi-attribute auctions for allocating resources to workflows occurring in dynamic environments where task performance is uncertain. To this end, we present an auction mechanism for allocationg multi-attribute tasks and resources in the workflow domain (PUMAA), a framework for customizing the outcomes of the auctions depending on the domain particularities (FMAAC) and a multi-dimensional fairness mechanism for favouring egalitarian allocations<br>L'assignació de tasques i recursos en fluxos de treball planteja un problema en el qual poden intervenir diferents atributs (costs econòmics, terminis d'entrega, emissions de CO2, etc.). Conseqüentment, per tal de tenir en compte tots els elements involucrats en l'assignació de recursos i aconseguir-ne una d'òptima, cal enfocar el problema des d'un prisma multicritèria. Els mecanismes de subhasta ofereixen la possibilitat d'assignar recursos i serveis en entorns competitius al mateix temps que s'optimitzen els beneficis per a tots els participants. En aquesta tesi es proposa emprar subhastes multi-atribut per a l'assignació de recursos en fluxos de treball que es desenvolupen, concurrentment, en entorns dinàmics on el desenvolupament de tasques presenta un alt grau d'incertesa.Amb aquest objectiu, presentem un mecanisme de subhastes multi-attribute per assignar recursos i tasques (PUMAA), un marc per a personalitzar les assignacions resultants dels mecanismes de buasta (FMAAC) i un mecanisme de fairness multi-criteria per afovirir assignacions igualitàries en lloc d'utilitàries
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Books on the topic "Intelligent Workflows"

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Cichocki, Andrzej. Workflow and Process Automation: Concepts and Technology. Springer US, 1998.

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Developing workflows and automation packages for IBM Tivoli Intelligent Orchestrator V3.1. 2nd ed. IBM, International Technical Support Organization, 2006.

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Developing Workflows and Automation Packages for IBM Tivoli Intelligent Orchestrator V3.1. Vervante, 2006.

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Redbooks, IBM. Developing Workflows And Automation Packages for IBM Tivoli Intelligent Thinkdynamic Orchestrator. Ibm, 2004.

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Trinh, Kevin, Guilhem Molines, and Suzette Samoojh. Intelligent Automation with IBM Cloud Pak for Business Automation: A Practical Guide to Automating Enterprise Business Workflows to Deliver Intelligent Solutions. Packt Publishing, Limited, 2022.

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Suarez-Battan. Collaborative Intelligence: 20 Digital Workflows F or Visual Collaboration. Wiley & Sons, Limited, John, 2023.

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Buller, Torsten. Einsatz von Workflow-Systemen mit intelligentem Dokumenten-Management. diplom.de, 2001.

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2010 BPM and Workflow Handbook, Spotlight on Business Intelligence. Future Strategies Inc, 2010.

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Scientific Workflows: Programming, Optimization, and Synthesis with ASKALON and AWDL. Springer London, Limited, 2012.

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Fahringer, Thomas, and Jun Qin. Scientific Workflows: Programming, Optimization, and Synthesis with ASKALON and AWDL. Springer, 2012.

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Book chapters on the topic "Intelligent Workflows"

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Ma, Jun, Erin Shaw, and Jihie Kim. "Computational Workflows for Assessing Student Learning." In Intelligent Tutoring Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13437-1_19.

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Jain, Aditi, and Raj Kumari. "A Review on Comparison of Workflow Scheduling Algorithms with Scientific Workflows." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2750-5_63.

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Dąbrowski, Marcin, Michał Drabik, Mariusz Trzaska, and Kazimierz Subieta. "Prototype of Object-Oriented Declarative Workflows." In Intelligent Information and Database Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20039-7_5.

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El Kandoussi, Asmaa, and Hanan El Bakkali. "Automated Security Driven Solution for Inter-Organizational Workflows." In Hybrid Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_35.

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Jander, Kai, Lars Braubach, and W. Lamersdorf. "Distributed Event Processing for Goal-Oriented Workflows." In Intelligent Distributed Computing VIII. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10422-5_7.

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Balamurugan, S., and S. Saraswathi. "Energy-Aware Workflow Scheduling Algorithm for the Deployment of Scientific Workflows in Cloud." In Smart Intelligent Computing and Applications. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1921-1_15.

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Afanasyev, Alexander, Nikolay Voit, Oksana Timofeeva, and Vyacheslav Epifanov. "Analysis and Control of Hybrid Diagrammatical Workflows." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68321-8_13.

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Knapp, JD, Matias Elo, James Shaeffer, and Paul G. Flikkema. "Towards Intelligent Closed-Loop Workflows for Ecological Research." In Dynamic Data-Driven Environmental Systems Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25138-7_10.

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Mihăescu, M. C., P. S. Popescu, and M. L. Mocanu. "Building Data Analysis Workflows that Provide Personalized Recommendations for Students." In Intelligent Systems Reference Library. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96644-7_3.

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Cima, Vojtech, Stanislav Böhm, Jan Martinovič, Jiří Dvorský, Thomas J. Ashby, and Vladimir Chupakhin. "HyperLoom Possibilities for Executing Scientific Workflows on the Cloud." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61566-0_36.

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Conference papers on the topic "Intelligent Workflows"

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Wang, Linhui, Liying Li, Yue Zeng, Peijin Cong, and Junlong Zhou. "ERSCHED: An Efficient and Reliable Scheduler for Workflows in Multi-Edge Systems." In 2024 4th International Conference on Intelligent Technology and Embedded Systems (ICITES). IEEE, 2024. https://doi.org/10.1109/icites62688.2024.10777403.

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Ghattas, Mohammad, Mohammad Abu-Salh, Suhail Odeh, and Osama Ghneem. "Enhancing Software Engineering Education through an Authentic Web-Based Simulation of Companies Workflows." In 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 2024. http://dx.doi.org/10.1109/coins61597.2024.10622145.

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Ciufudean, Calin, Constantin Filote, and Dumitru Amarandei. "Intelligent control of dependability workflows." In 2008 IEEE International Symposium on Industrial Electronics (ISIE 2008). IEEE, 2008. http://dx.doi.org/10.1109/isie.2008.4677150.

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Hartmann, Melanie, Marcus Stander, and Victoria Uren. "Adapting Workflows to Intelligent Environments." In 2011 7th International Conference on Intelligent Environments (IE). IEEE, 2011. http://dx.doi.org/10.1109/ie.2011.37.

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Wessling, Stefan, Anne Bartetzko, Jianyong Pei, and Thomas Dahl. "Automation in Wellbore Stability Workflows." In SPE Intelligent Energy International. Society of Petroleum Engineers, 2012. http://dx.doi.org/10.2118/149766-ms.

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Shealy, Benjamin T., F. Alex Feltus, and Melissa C. Smith. "Intelligent Resource Provisioning for Scientific Workflows and HPC." In 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS). IEEE, 2021. http://dx.doi.org/10.1109/works54523.2021.00007.

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Fiannaca, Antonino, Salvatore Gaglio, Massimo La Rosa, Riccardo Rizzo, and Alfonso Urso. "An Intelligent System for Building Bioinformatics Workflows." In 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS). IEEE, 2012. http://dx.doi.org/10.1109/cisis.2012.141.

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Doukas, Charalampos, Aristotle Chatziioannou, and Ilias Maglogiannis. "Intelligent planning of biomedical image mining workflows." In 2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010). IEEE, 2010. http://dx.doi.org/10.1109/itab.2010.5687677.

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Goumopoulos, Christos, Ioannis Calemis, and Achilles Kameas. "Deployment of Adaptive Workflows in Intelligent Environments." In 2010 6th International Conference on Intelligent Environments (IE). IEEE, 2010. http://dx.doi.org/10.1109/ie.2010.43.

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Sardis, Emmanuel, Vasileios Anagnostopoulos, and Theodora Varvarigou. "Multi-agent Based Surveillance of Workflows." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.172.

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Reports on the topic "Intelligent Workflows"

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Hurley, M. B., and P. B. Jones. Performance Assessment of Intelligence, Surveillance, and Reconnaissance (ISR) Enterprise Workflows. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada462746.

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Zhu, Michelle M., and Chase Q. Wu. A Workflow-based Intelligent Network Data Movement Advisor with End-to-end Performance Optimization. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1104532.

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Perdigão, Rui A. P., and Julia Hall. Empowering Next-Generation Synergies among Models and Data with Information Physical Quantum Technological Intelligence. Synergistic Manifolds, 2024. https://doi.org/10.46337/241209.

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We present and deploy our methodological and technological framework of Information Physical Quantum Technological Intelligence (IPQuTI), to empower next-generation mathematically robust, physically consistent, computationally efficient and operationally scalable synergies among models and data across multisectoral theoretical and applied workflows. Going beyond digital computing platforms, IPQuTI encompasses a richer basis alphabet of fundamental quantum states (information building blocks) and a high-order set of superposition and entanglement functionals (grammar) beyond the state of the art in quantum information itself. These are foundational core underneath our increased ability to encode, analyze, generate and simulate a broader physical language: one that is able to seamlessly treat complex high-dimensional data and model structures, interactions and operations with faster computational speed, higher robustness, physical consistency, information fidelity, spatiotemporal resolution and lead. For example, in the setting of ensemble operations, IPQuTI encapsulates an entire spatiotemporal system of events into a block operation as a coherent universe. Turning lengthy intense computational churning of approximate equations and massive datasets into a post-quantum spatiotemporal pulse of rich spatiotemporal diversity, spanning deterministic and stochastic synergies among models and data into a unified solution. With nonlinear geophysical applications in mind, IPQuTI is explored in key contexts of Data Assimilation, Data Fusion, Machine Learning, Predictability Investigation and Uncertainty Quantification. Firstly, to further optimize gold-standard state-of-art (SoA) solutions, providing them with a new efficient and robust platform to operate. Second, to overcome known SoA challenges, thereby contributing towards their methodological and practical upgrade. Third, to unveil novel features adding methodological and applied value to these areas, including handling sparse records, perturbations and extremes.
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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Muelaner, Jody Emlyn. Generative Design in Aerospace and Automotive Structures. SAE International, 2024. http://dx.doi.org/10.4271/epr2024016.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Semi-automated computational design methods involving physics-based simulation, optimization, machine learning, and generative artificial intelligence (AI) already allow greatly enhanced performance alongside reduced cost in both design and manufacturing. As we progress, developments in user interfaces, AI integration, and automation of workflows will increasingly reduce the human inputs required to achieve this. With this, engineering teams must change their mindset from designing products to specifying requirements, focusing their efforts on testing and analysis to provide accurate specifications.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;Generative Design in Aerospace and Automotive Structures&lt;/b&gt; discusses generative design in its broadest sense, including the challenges and recommendations regarding multi-stage optimizations.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt;Click here to access the full SAE EDGE&lt;/a&gt;&lt;sup&gt;TM&lt;/sup&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt; Research Report portfolio.&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;
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Pasupuleti, Murali Krishna. Decentralized Creativity: AI-Infused Blockchain for Secure and Transparent Digital Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi125.

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Abstract The convergence of artificial intelligence (AI) and blockchain technology is transforming the creative economy by enabling secure, transparent, and decentralized innovation in digital content creation, intellectual property management, and monetization. Traditional creative industries are often constrained by centralized platforms, opaque copyright enforcement, and unfair revenue distribution, which limit the autonomy and financial benefits of creators. By leveraging blockchain’s immutable ledger, smart contracts, and non-fungible tokens (NFTs), digital assets can be authenticated, tokenized, and securely traded, ensuring ownership verification and automated royalty distribution. Simultaneously, AI-driven tools such as generative adversarial networks (GANs), neural networks, and natural language processing (NLP) models facilitate content generation, curation, and adaptive recommendations, enhancing creative workflows and fostering new artistic possibilities. This research report explores the synergies between AI and blockchain in the decentralized creative economy, analyzing their impact on digital rights protection, NFT marketplaces, decentralized publishing, AI-assisted music composition, and smart licensing models. Furthermore, it examines regulatory challenges, ethical considerations, and scalability limitations that need to be addressed for mainstream adoption. By integrating AI-powered automation with blockchain’s decentralized infrastructure, this study outlines a sustainable roadmap for secure, fair, and transparent digital creativity in the Web3 era. Keywords AI-powered creativity, blockchain-based digital ownership, decentralized innovation, generative AI, smart contracts, non-fungible tokens (NFTs), digital content authentication, AI-driven content generation, decentralized autonomous organizations (DAOs), intellectual property management, AI in art and music, Web3 creativity, tokenized digital assets, secure content monetization, ethical AI in blockchain, AI-assisted copyright protection, decentralized publishing, AI-powered music composition, blockchain scalability, AI for digital rights management.
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Greinert, Jens. Mine Monitoring in the German Baltic Sea 2020; Dumped munition monitoring AL548, 03rd – 16th November 2020, Kiel (Germany) – Kiel (Germany) „MineMoni-II 2020“. GEOMAR Helmholtz Centre for Ocean Research Kiel, 2021. http://dx.doi.org/10.3289/cr_al548.

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ALKOR cruise AL548 took place as part of the EMFF (European Maritime and Fisheries Fund)-funded project BASTA (Boost Applied munition detection through Smart data inTegration and AI workflows; https://www.basta-munition.eu) and as continuation of the munition monitoring started within the BMBF-funded project UDEMM (Environmental Monitoring for the Delaboration of Munition in the Sea; https://udemm.geomar.de/). In October 2018, a first cruise (POS530 MineMoni2018) was conducted, to gather data for a broad baseline study in the German Baltic Sea. Results show a moderate contamination level on regional and coastal scale, but indicate higher levels for specific local areas. Within UDEMM, expertise was developed to detect, exactly locate and monitor munition (e.g. torpedoes, sea mines, ground mines) on the seafloor using optical and hydroacoustic means. In addition, chemical analyses of dissolved contaminants in the water and sediments was performed. Data acquired during this cruise are used in BASTA, which aims for enhanced munition detection via AUV-based artificial intelligence applied on multi-sensor datasets. At the same time, the project ExPloTect (Ex-situ, near-real-time exPlosive compound deTection in seawater) (also EMFF-funded) addresses the need for an innovative approach to detect explosive compounds in seawater. A prototype system was used and successfully tested for the first time during this cruise. The main focus was placed onto the two already known dumpsites Kolberger Heide and Lübeck Bight. Additionally, new areas Falshöft (Schleswig-Holstein) and Cadet Channel, Trollegrund and Großklützhöved (Mecklenburg-Vorpommern) were explored. In each area high-resolution multibeam mapping was performed and contact lists, indicating potential munition objects were produced on board. AUV surveys were conducted to ground-truth possible contacts via detailed photograph and magnetometer mapping. This was complemented with towed video (TV)-CTD profiles. The transits to and between those sites were planned along former constraint routes during WWII. These routes were main targets of the British Air Force and mines and bombs can be expected along these ways. During transits water samples were taken with on a CTD- (conductivity, temperature, depth) rosette-mounted Niskin bottles in regular distances, in order to obtain a comprehensive understanding munition compounds (inter alia trinitrotoluene (TNT)) measurements across the German Baltic Sea.
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