Relevant bibliographies by topics / Actionable intelligence

Academic literature on the topic 'Actionable intelligence'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Actionable intelligence"

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McMahon, Eugene. "Actionable Intelligence." Journal of Visual Impairment & Blindness 104, no. 11 (November 2010): 732–33. http://dx.doi.org/10.1177/0145482x1010401110.

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Harkleroad, David. "Making intelligence analysis actionable." Competitive Intelligence Review 5, no. 2 (1994): 13–17. http://dx.doi.org/10.1002/cir.3880050205.

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Lustick, Ian S. "Geopolitical Forecasting and Actionable Intelligence." Survival 64, no. 1 (January 2, 2022): 51–56. http://dx.doi.org/10.1080/00396338.2022.2032959.

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Herring, Jan. "Producing actionable and effective intelligence." Competitive Intelligence Review 6, no. 1 (1995): 57–59. http://dx.doi.org/10.1002/cir.3880060111.

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Rao, R. "From unstructured data to actionable intelligence." IT Professional 5, no. 6 (November 2003): 29–35. http://dx.doi.org/10.1109/mitp.2003.1254966.

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Colineau, Nathalie, Cécile Paris, and Mingfang Wu. "Delivering actionable information." Revue d'intelligence artificielle 18, no. 4 (August 1, 2004): 549–76. http://dx.doi.org/10.3166/ria.18.549-576.

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Serketzis, Nikolaos, Vasilios Katos, Christos Ilioudis, Dimitrios Baltatzis, and George J. Pangalos. "Actionable threat intelligence for digital forensics readiness." Information & Computer Security 27, no. 2 (June 12, 2019): 273–91. http://dx.doi.org/10.1108/ics-09-2018-0110.

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PurposeThe purpose of this paper is to formulate a novel model for enhancing the effectiveness of existing digital forensic readiness (DFR) schemes by leveraging the capabilities of cyber threat information sharing.Design/methodology/approachThis paper uses a quantitative methodology to identify the most popular cyber threat intelligence (CTI) elements and introduces a lightweight approach to correlate those with potential forensic value, resulting in the quick and accurate triaging and identification of patterns of malicious activities.FindingsWhile threat intelligence exchange steadily becomes a common practice for the prevention or detection of security incidents, the proposed approach highlights its usefulness for the digital forensics (DF) domain.Originality/valueThe proposed model can help organizations to improve their DFR posture, and thus minimize the time and cost of cybercrime incidents.
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Gill, Karamjit S. "Actionable ethics." AI & SOCIETY 37, no. 1 (January 27, 2022): 1–7. http://dx.doi.org/10.1007/s00146-022-01387-1.

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Jain, Sarika, Sumit Sharma, Jorrit Milan Natterbrede, and Mohamed Hamada. "Rule-Based Actionable Intelligence for Disaster Situation Management." International Journal of Knowledge and Systems Science 11, no. 3 (July 2020): 17–32. http://dx.doi.org/10.4018/ijkss.2020070102.

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Managing natural disasters is a social responsibility as they might cause a gloomy impact on human life. Efficient and timely alert systems for public and actionable recommendations for decision makers may well decrease the number of casualties. Web semantics strengthen the description of web resources for exploiting them better and making them more meaningful for both human and machine. In this work, the authors propose a semantic rule-based approach for disaster situation management (DSM) to reach the next level of decision-making power and its architecture for providing actionable intelligence in the domain of the earthquake. The system itself is based on a data pre-processing layer, a computation layer, and the middle layer relies on an extensive rule base of experts' advice stored over time and a disaster ontology along with its inherent semantics. The rule-based reasoning approach uses this knowledge base in combination with the expert rule base, written in SWRL rules, to infer recommendations for the response to an earthquake.
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Tekin, Cem, and Mihaela van der Schaar. "Actionable intelligence and online learning for semantic computing." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630011. http://dx.doi.org/10.1142/s2425038416300111.

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As the world becomes more connected and instrumented, high dimensional, heterogeneous and time-varying data streams are collected and need to be analyzed on the fly to extract the actionable intelligence from the data streams and make timely decisions based on this knowledge. This requires that appropriate classifiers are invoked to process the incoming streams and find the relevant knowledge. Thus, a key challenge becomes choosing online, at run-time, which classifier should be deployed to make the best possible predictions on the incoming streams. In this paper, we survey a class of methods capable to perform online learning in stream-based semantic computing tasks: multi-armed bandits (MABs). Adopting MABs for stream mining poses, numerous new challenges requires many new innovations. Most importantly, the MABs will need to explicitly consider and track online the time-varying characteristics of the data streams and to learn fast what is the relevant information out of the vast, heterogeneous and possibly highly dimensional data streams. In this paper, we discuss contextual MAB methods, which use similarities in context (meta-data) information to make decisions, and discuss their advantages when applied to stream mining for semantic computing. These methods can be adapted to discover in real-time the relevant contexts guiding the stream mining decisions, and tract the best classifier in presence of concept drift. Moreover, we also discuss how stream mining of multiple data sources can be performed by deploying cooperative MAB solutions and ensemble learning. We conclude the paper by discussing the numerous other advantages of MABs that will benefit semantic computing applications.
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Dissertations / Theses on the topic "Actionable intelligence"

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Wang, Yun, and Yun Wang. "Mining Massive Spatiotemporal Data for Actionable Intelligence." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625611.

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The widespread adoption of the internet of things is producing a huge amount of spatiotemporal data. Global mobile data traffic is growing an average of 60 percent and has reached 7.2 exabytes per month in 2016. Realizing values from these data promises to improve understanding of human activities that will up opportunities to innovative applications and services. To this end, my dissertation focuses on analyzing massive spatiotemporal data to gather actionable intelligence using machine learning techniques. I strive to address the challenges in processing, representing and mining spatiotemporal data on a large-scale by embracing big data analytics. In particular, this dissertation consists of three essays of predictive modeling. The first essay proposes a novel model that captures spatial dependencies, temporal preferences, and social influence to predict when and where people will use certain kinds of services. In the second essay, I demonstrate the ability of network analysis and sequence learning to glean previously unavailable insights for student retention prediction, which fill the gap between student attrition theories and existing quantitative approaches. Using public transportation as a proxy, the third essay presents a deep-learning-based urban mobility model that collectively forecast short-term ridership between each pair of urban regions. Throughout this thesis, I show how big data infrastructure such as Hadoop MapReduce can amplify the capabilities to analyze spatiotemporal data in an efficient and economical way. All the proposed models are developed and evaluated on real-world datasets. In conjunction with prior knowledge in the application domains, the results can be converted into actionable intelligence that supports decision making in operation management.
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Ackerman, Marius. "Processes for unlocking actionable business intelligence in SA banking institutions." Diss., University of Pretoria, 2003. http://hdl.handle.net/2263/24040.

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Since much more than the implementation of IT solutions is frequently required to produce actionable intelligence output, the unlocking of actionable Business Intelligence (BI) for decision-making based on both internal and external information sources, is proving to be a real challenge for SA banking institutions. Although all the major banking institutions in South Africa produce and use BI in some form or the other, the concept is often not clearly defined, and not enough emphasis is placed on the use of recognized intelligence processes to provide intelligence output that is both accurate and actionable. The aim of this research was to determine whether SA banking institutions applied recognized intelligence processes, or components thereof, while conducing BI assignments. Whilst the typical four- or five-stage intelligence process, as discussed in BI literature, was commonly accepted by the banking institutions that participated in this research as a benchmark in conducting BI, the researcher established that these institutions placed more emphasis on executing some stages, whilst other stages were executed in an ad hoc manner. In this regard it was found that, while emphasis was placed on the collection stage of the BI process during the ‘analysis stage’, which is one of the most important stages of the process, no specific step-by-step procedure was followed. In the analysis stage of the process, emphasis was typically placed on the application of the specific methods of analysis. In the stages of the BI processes that deal with BI requirements definition and dissemination of BI products, formal BI processes were also found to be lacking. This prompted the researcher to suggest a practical step-by-step process for dealing with each stage of the BI process. In the final chapter of this dissertation, the researcher provides a summary of the key findings in relation to the research problem and identifies a number of areas in which further research should be conducted. Finally, it is important to note that BI will remain a critical business issue for SA banking institutions in their efforts to become more profitable, more customer centred, and ultimately more competitive in the face of dynamic and challenging market conditions. In this regard BI processes provide a critical framework for the conduct of BI assignments in SA banking institutions. he full text of this thesis/dissertation is not available online. Please contact us if you need access Since much more than the implementation of IT solutions is frequently required to produce actionable intelligence output, the unlocking of actionable Business Intelligence (BI) for decision-making based on both internal and external information sources, is proving to be a real challenge for SA banking institutions. Although all the major banking institutions in South Africa produce and use BI in some form or the other, the concept is often not clearly defined, and not enough emphasis is placed on the use of recognized intelligence processes to provide intelligence output that is both accurate and actionable. The aim of this research was to determine whether SA banking institutions applied recognized intelligence processes, or components thereof, while conducing BI assignments. Whilst the typical four- or five-stage intelligence process, as discussed in BI literature, was commonly accepted by the banking institutions that participated in this research as a benchmark in conducting BI, the researcher established that these institutions placed more emphasis on executing some stages, whilst other stages were executed in an ad hoc manner. In this regard it was found that, while emphasis was placed on the collection stage of the BI process during the ‘analysis stage’, which is one of the most important stages of the process, no specific step-by-step procedure was followed. In the analysis stage of the process, emphasis was typically placed on the application of the specific methods of analysis. In the stages of the BI processes that deal with BI requirements definition and dissemination of BI products, formal BI processes were also found to be lacking. This prompted the researcher to suggest a practical step-by-step process for dealing with each stage of the BI process. In the final chapter of this dissertation, the researcher provides a summary of the key findings in relation to the research problem and identifies a number of areas in which further research should be conducted. Finally, it is important to note that BI will remain a critical business issue for SA banking institutions in their efforts to become more profitable, more customer centred, and ultimately more competitive in the face of dynamic and challenging market conditions. In this regard BI processes provide a critical framework for the conduct of BI assignments in SA banking institutions.
Dissertation (MA)--University of Pretoria, 2003.
Information Science
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Sundland, Joseph J. Carroll Christopher J. "Transforming data and metadata into actionable intelligence and information within the maritime domain." Monterey, Calif. : Naval Postgraduate School, 2008. http://handle.dtic.mil/100.2/ADA483649.

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Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, June 2008. Thesis (M.S. in Systems Technology (Command, Control, and Communications (C3)))--Naval Postgraduate School, June 2008.
Thesis Advisor(s): MacKinnon, Douglas. "June 2008." Description based on title screen as viewed on August 25, 2008. M.S. in Information Technology Management awarded to Joseph J. Sundland, June 2008. M.S. in Systems Technology (Command, Control, and Communications (C3)) awarded to Christopher J. Carroll, June 2008. Includes bibliographical references (p. 93-96). Also available in print.
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Carroll, Christopher J. "Transforming data and metadata into actionable intelligence and information within the maritime domain." Thesis, Monterey, California. Naval Postgraduate School, 2008. http://hdl.handle.net/10945/4078.

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At present, the Navy is unable to provide a robust, routable network that provides real-time actionable intelligence between boarding operations and intelligence analysts. Actionable intelligence is the means of obtaining concrete knowledge that permits an individual to take action based on known information. The lack of a robust routable network creates a lag in operational responsiveness to potential threats identified within the Maritime Environment. In response to current shortfalls, improved Extended Maritime Interdiction Operations (EMIO) seeks to support the Secretary of the Navy's vision to streamline and improve operations and exploitation of boarding data. However, there has been no clear indication as to how the implementation of these technologies will affect command and control or current operations. This thesis examines the impact of improved EMIO technology designed to bridge together data with intelligence collected during EMIO and improve maritime domain decision making in terms of speed and quality and thus improve end user's situational awareness. We follow the construct of Business Process Reengineering (BPR) to frame our analysis and to provide focus in our data collection. We also examine the changes to the present EMIO process by developing and implementing an organizational simulation using POWER 2.0. Our results indicate that when improved Spiral-1 EMIO technologies, which significantly decrease the amount of time it takes to fuse collected boarding data into intelligence systems, are combined with a redesign of the EMIO organization, a qualitative improvement toward accomplishing the overall process can be achieved. The current process requires 35 hours. Yet, with the revised technological and proposed organizational changes, the same process can be achieved in 5 hours, thus achieving the Navy Secretary's vision to streamline and improve maritime operations.
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Uga, Brenda. "Towards Trustworthy AI : A proposed set of design guidelines for understandable, trustworthy and actionable AI." Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385392.

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Artificial intelligence is used today in both everyday applications and specialised expert systems. In situations where relying on the output of the AI brings about the risk of negative consequences, it becomes important to understand why the AI system has produced its output. Previous research in human-computer trust has identified trust antecedents that contribute to formation of trust in an AI artifact, understanding of the system being one of them. In the context of Pipedrive, a sales management system, this thesis investigates how can AI predictions be designed as understandable and trustworthy, and by extension which explanatory aspects provide guidance towards actions to take, and which presentation formats support for- mation of trust. Using a research-through design approach, multiple designs for displaying AI predictions are explored for Pipedrive, leading to a proposal for a set of design guidelines that support understandability, trustworthiness and actionability of AI. Both the designs and the guidelines have been iteratively developed in collaboration with users and design practitioners.
Artificiell intelligens används idag både i vardagliga applikationer och expertsystem. I situationer då förtroendet för utdata från AI innebär en risk för negativa konsekvenser blir det viktigt att förstå varför AI-systemet har producerat dess utdata. Tidigare forskning inom människa-datorförtroende har identifierat förtroendeföregångare som bidrar till att skapa förtroende för en AI-artefakt, varav förståelse för systemet är en av dem. Inom ramen för Pipedrive, ett säljhanteringssystem, utreder denna avhandling hur AI-förut-sägelser kan designas på ett förståeligt och pålitligt sätt, och i förlängningen vilka förklarande aspekter som kan ge vägledning gällande de åtgärder som ska vidtas, samt vilka presentationsformat som stödjer skapande av förtroende. Med hjälp av en metod för forskning genom design undersöks flera utföranden för att visa AI-förutsägelser för Pipedrive, vilket leder till ett förslag till en uppsättning riktlinjer för design som stödjer förståelse, pålitlighet och funktionsduglighet. Både design och riktlinjer har utvecklats iterativt i samarbete med användare och designutövare.
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Subramani, Sudha. "Extracting Actionable Knowledge from Domestic Violence Discourse on Social Media." Thesis, 2019. https://vuir.vu.edu.au/39603/.

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Respect for human rights is the cornerstone of strong communities, based on the principles of dignity, equality, and recognition of inherent value of each individual. Domestic Violence, ranging from physical abuse to emotional manipulation, is worldwide considered as the violation of the elementary rights to which all human beings are entitled to. As one might expect, the consequences for its victims are often severe, far-reaching, and long-lasting, causing major health, welfare, and economic burden. Domestic Violence is also one of the most prevailing forms of violence, and due to the social stigma surrounding the issue particularly challenging to address. With the emergence and expansion of Social Media, the substantial shift in the support-seeking and the support-provision pattern has been observed. The initial barriers in approaching healthcare professionals, i.e. personal reservations, or safety concerns, have been effectively addressed by virtual environments. Social Media platforms have quickly become crucial networking hubs for violence survivors as well as at-risk individuals to share experiences, raise concerns, offer advice, or express sympathy. As a result, the specialized support services groups have been established with the aim of pro-active reach-out to potential victims in time-critical situations. Given the high-volume, highvelocity and high-variety of Social Media data, the manual posts evaluation has not only become inefficient, but also unfeasible in the long-term. The conventional automated approaches reliant on pre-defined lexicons, and hand-crafted feature engineering proved limited in their classification performance capability when exposed to the challenging nature of Social Media discourse. At the same time, Deep Learning the state-of-the-art sub-field of Machine Learning has shown remarkable results on text classification tasks. Given its relative recency and algorithmical complexity, the implementation of Deep Learningbased models has been vastly under-utilised in practical applications. In particular, no prior work has addressed the problem of fine-grained user-generated content classification with Deep Learning in Domestic Violence domain. The study introduces novel 3-part framework aimed at (i) binary detection of critical situations; (ii) multi-class content categorization; and (ii) Abuse Types and Health Issues extraction from Social Media discourse. The classification performance of state-of-the-art models is improved through the domain-specific word embeddings development, capable of precise relationships between the words recognition. The prevalent patterns of abuse, and the associated health conditions are efficiently extracted to shed the light on violence scale and severity from directly affected individuals. The approach proposed marks a step forward towards effective prevention and mitigation of violence within the society.
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Books on the topic "Actionable intelligence"

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Fantuzzo, John, and Dennis P. Culhane, eds. Actionable Intelligence. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114.

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Irmeli, Hirvensalo, and Vaarnas Markko, eds. The handbook of market intelligence: Global best practice in turning market data into actionable insights. Hoboken, N.J: Wiley, 2011.

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Shane, Atchison, ed. Actionable web analytics: Using data to make smart business decisions. Indianapolis, IN: Wiley Publishing, 2007.

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Liebowitz, Jay, and Amanda Dawson. Actionable Intelligence in Healthcare. Auerbach Publishers, Incorporated, 2017.

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Actionable Intelligence in Healthcare. Taylor & Francis Group, 2017.

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Liebowitz, Jay, and Amanda Dawson. Actionable Intelligence in Healthcare. Auerbach Publishers, Incorporated, 2017.

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Liebowitz, Jay, and Amanda Dawson. Actionable Intelligence in Healthcare. Auerbach Publishers, Incorporated, 2017.

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Liebowitz, Jay, and Amanda Dawson. Actionable Intelligence in Healthcare. Auerbach Publishers, Incorporated, 2017.

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Burby, Jason, and Shane Atchison. Actionable Web Analytics. Wiley & Sons, Incorporated, John, 2007.

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Abellera, Rosendo. Oracle Business Intelligence with Machine Learning: Artificial Intelligence Techniques in OBIEE for Actionable BI. Apress, 2017.

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Book chapters on the topic "Actionable intelligence"

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Fantuzzo, John, Dennis Culhane, Heather Rouse, and Cassandra Henderson. "Introduction to the Actionable Intelligence Model." In Actionable Intelligence, 1–38. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114_1.

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Petrila, John. "Legal Issues in the Use of Electronic Data Systems for Social Science Research." In Actionable Intelligence, 39–75. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114_2.

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Rothbard, Aileen. "Quality Issues in the Use of Administrative Data Records." In Actionable Intelligence, 77–103. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114_3.

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Kumar, Prashant. "An Overview of Architectures and Techniques for Integrated Data Systems Implementation." In Actionable Intelligence, 105–23. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114_4.

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Stiles, Paul G., and Roger A. Boothroyd. "Ethical Use of Administrative Data for Research Purposes." In Actionable Intelligence, 125–55. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114_5.

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Zerbe, Richard O., and Tyler A. Scott. "Benefit-Cost Analysis and Integrated Data Systems." In Actionable Intelligence, 157–67. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114_6.

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Kitzmiller, Erika M., and T. C. Burnett. "The AISP Network: Three Organizational Models for Building, Using, and Sustaining Integrated Data Systems." In Actionable Intelligence, 169–90. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114_7.

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Kitzmiller, Erika M. "Demonstrating the Value of Integrated Data Systems: Data-Use Practice in Four AISP Network Sites." In Actionable Intelligence, 191–206. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114_8.

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Culhane, Dennis P., Whitney A. LeBoeuf, and T. C. Burnett. "Future Opportunities for Leveraging IDS and Evidence-Based Policy Making." In Actionable Intelligence, 207–21. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137475114_9.

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Tamez, Pamela A., and Mary B. Engler. "Empowering Clinician-Scientists in the Information Age of Omics and Data Science." In Actionable Intelligence in Healthcare, 1–18. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa PLC, 2017.: Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315208442-1.

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Conference papers on the topic "Actionable intelligence"

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Jovanović, Jelena, Shane Dawson, Srećko Joksimović, and George Siemens. "Supporting actionable intelligence." In LAK '20: 10th International Conference on Learning Analytics and Knowledge. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3375462.3375474.

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McKenzie, Amber, Rob Gillen, and Paul Logasa Bogen. "Redeye Text Analysis Workbench: Actionable intelligence from non-actionable data." In 2013 IEEE International Conference on Technologies for Homeland Security (HST). IEEE, 2013. http://dx.doi.org/10.1109/ths.2013.6699034.

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Dey, Lipika, Sk Mirajul Haque, and Nidhi Raj. "Mining Customer Feedbacks for Actionable Intelligence." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.196.

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Chaudhari, Upendra, Sarah Conrod, Alexander Faisman, Giridharan Iyengar, Dimitri Kanevsky, Mark Kogan, Ganesh Ramaswamy, and Paola Virga. "MAAI: Media analytics for actionable intelligence." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517977.

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McKay, Tim, Kate Miller, and Jared Tritz. "What to do with actionable intelligence." In the 2nd International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330601.2330627.

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Shin, Bongsik, Aaron Elkins, Lance Larson, Lance Cameron, and Marc Perez. "Actionable Intelligence-Oriented Cyber Threat Modeling Framework." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2022. http://dx.doi.org/10.24251/hicss.2022.820.

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Khosla, Deepak, and Suhas E. Chelian. "Bio-inspired method and system for actionable intelligence." In SPIE Defense, Security, and Sensing, edited by Stephen Mott, John F. Buford, Gabe Jakobson, and Michael J. Mendenhall. SPIE, 2009. http://dx.doi.org/10.1117/12.820505.

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"DELIVERING ACTIONABLE BUSINESS INTELLIGENCE THROUGH SERVICE-ORIENTED ARCHITECTURE." In 10th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001676701910194.

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Hunt, Christian O. "Leveraging STIX for Actionable Intelligence and Automated Response." In 2021 Resilience Week (RWS). IEEE, 2021. http://dx.doi.org/10.1109/rws52686.2021.9611811.

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Zhang, Chengqi. "Developing Actionable Trading Strategies for Trading Agents." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.381.

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Reports on the topic "Actionable intelligence"

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Armbruster, Thomas H. U.S. - Russian Nuclear Cooperation: Actionable Intelligence. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada427594.

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Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu, and Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42169.

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Managing the ever-growing volume and velocity of data across the battlefield is a critical problem for warfighters. Solving this problem will require a fundamental change in how battlefield analyses are performed. A new approach to making decisions on the battlefield will eliminate data transport delays by moving the analytical capabilities closer to data sources. Decision cycles depend on the speed at which data can be captured and converted to actionable information for decision making. Real-time situational awareness is achieved by locating computational assets at the tactical edge. Accelerating the tactical decision process leverages capabilities in three technology areas: (1) High-Performance Computing (HPC), (2) Machine Learning (ML), and (3) Internet of Things (IoT). Exploiting these areas can reduce network traffic and shorten the time required to transform data into actionable information. Faster decision cycles may revolutionize battlefield operations. Presented is an overview of an artificial intelligence (AI) system design for near-real-time analytics in a tactical operational environment executing on co-located, mobile HPC hardware. The report contains the following sections, (1) an introduction describing motivation, background, and state of technology, (2) descriptions of tactical decision process leveraging HPC problem definition and use case, and (3) HPC tactical data analytics framework design enabling data to decisions.
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You might also be interested in the extended bibliographies on the topic 'Actionable intelligence' for particular source types:
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