Статті в журналах: "Actionable intelligence"

1

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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Havlík, Martin. "Actionable Intelligence – Supporting Instrument for Commander’s Decision-making Process." Vojenské rozhledy 25, no. 1 (February 15, 2016): 61–72. http://dx.doi.org/10.3849/1210-3292.25.2016.01.061-072.

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Linkov, Igor, Stephanie Galaitsi, Benjamin D. Trump, Jeffrey M. Keisler, and Alexander Kott. "Cybertrust: From Explainable to Actionable and Interpretable Artificial Intelligence." Computer 53, no. 9 (September 2020): 91–96. http://dx.doi.org/10.1109/mc.2020.2993623.

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Chiba, Daiki, Mitsuaki Akiyama, Takeshi Yagi, Kunio Hato, Tatsuya Mori, and Shigeki Goto. "DomainChroma: Building actionable threat intelligence from malicious domain names." Computers & Security 77 (August 2018): 138–61. http://dx.doi.org/10.1016/j.cose.2018.03.013.

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Srivastava, Jaideep, and Robert Cooley. "Web Business Intelligence: Mining the Web for Actionable Knowledge." INFORMS Journal on Computing 15, no. 2 (May 2003): 191–207. http://dx.doi.org/10.1287/ijoc.15.2.191.14447.

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Dokman, Tomislav. "Defining the term "intelligence" - insight into existing intelligence knowledge." Informatologia 52, no. 3-4 (December 31, 2019): 194–205. http://dx.doi.org/10.32914/i.52.3-4.7.

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For more than half a century of Intelligence Studies, this field has been characterized by the problem of lack of uniform definition of the term intelligence, a contentious place in the corpus of existing knowledge. The determinant of this is the existence of different types of intelligence, that is, the term is related to the intelligence product or information, the process/cycle in which information is collected, processed, analyzed and disseminated, and to the intelligence producing organization. Furthermore, it is a broad concept that initially developed and presented itself throughout history as exclusive state property, only later to become an equally represented term in other fields, more specifically business, science, sports, etc. Defining the term "intelligence" is important not only for the sake of development of intelligence theory and scientific discipline, but also because of the practical part of "intelligence" which is an essential feature of every state as it provides support for state decision-making process and defining policies in the national security spectrum. The paper analyzes 35 scientific, expert and institutional definitions of the term intelligence using quantitative and qualitative content analysis. Qualitative content analysis identified 15 key elements. The quantitative analysis found that the most represented element was "information", followed by "end user/decision maker", followed by "actionable character", "foreign countries" and "knowledge". Based on the elements extracted, a new definition is presented. Intelligence is characterized by actionable knowledge of foreign/other countries that is disseminated towards end users, i.e. decision makers, in the form of information.

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Gonçalves, Leandro Rodrigues, and Fernando Carvalho De Almeida. "How Technology Intelligence is Applied In Different Contexts?" International Journal of Innovation 7, no. 1 (January 2, 2019): 104–18. http://dx.doi.org/10.5585/iji.v7i1.271.

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Technology Intelligence is one of the many ways of applying Competitive Intelligence. As CI, TI intends to detect and process weak signals in order to identify opportunities and threats and provide actionable information. There is still a gap in reported cases of companies actually applying Technology Intelligence. This article intends to answer the research question: How companies build an actionable technology intelligence project? Case Study and Action-research approaches were applied for this research. The article describes two application cases: a research institute with a petrochemical industry as a client; and a private petrochemical industry. Companies seem to not know how to deal with Technology Intelligence. When outsourcing, they are more willing to pay for an extremely comprehensive project that not necessarily needs to be so deep and complex. When doing it internally, decision makers are not willing to wait and give the TI analysts resources to conduct a project in the right deepness and complexity. It seems like a “goldilocks problem” applied to Technology Intelligence.

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Morales, Cecilia G., Nicholas Gisolfi, Robert Edman, James K. Milller, and Artur Dubrawski. "Actionable Model-Centric Explanations (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13019–20. http://dx.doi.org/10.1609/aaai.v36i11.21646.

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We recommend using a model-centric, Boolean Satisfiability (SAT) formalism to obtain useful explanations of trained model behavior, different and complementary to what can be gleaned from LIME and SHAP, popular data-centric explanation tools in Artificial Intelligence (AI).We compare and contrast these methods, and show that data-centric methods may yield brittle explanations of limited practical utility.The model-centric framework, however, can offer actionable insights into risks of using AI models in practice. For critical applications of AI, split-second decision making is best informed by robust explanations that are invariant to properties of data, the capability offered by model-centric frameworks.

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DERKSEN, KEVIN MICHAEL. "Commentary: The Logistics of Actionable Intelligence Leading to 9/11." Studies in Conflict & Terrorism 28, no. 3 (May 2005): 253–68. http://dx.doi.org/10.1080/10576100590928133.

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Cao, Longbing, and Tony He. "Developing actionable trading agents." Knowledge and Information Systems 18, no. 2 (October 10, 2008): 183–98. http://dx.doi.org/10.1007/s10115-008-0170-2.

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Kim, Miyoung, Jane Snowdon, S. Dilhan Weeraratne, Winnie Felix, Lionel Lim, Irene Dankwa-Mullan, Young Kyung Lee, et al. "Clinical insights for hematological malignancies from an artificial intelligence decision-support tool." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e13023-e13023. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e13023.

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e13023 Background: Next generation sequencing (NGS) in hematological tumors is increasingly shaping clinical treatment decisions at the point of care. While the impact of NGS panels in solid tumors is largely therapeutic, targeted sequencing in hematological tumors can additionally provide diagnostic and prognostic insights. Additional data generated in hematological tumor sequencing makes manual interpretation and annotation of variants tedious and non-scalable. In this study we compared hematological tumor variant interpretation using an artificial intelligence decision-support system, Watsonä for Genomics (WfG), with expert guided manual curation. Methods: Patients with hematological tumors at Hallym University, College of Medicine between December 2017 and December 2018, were sequenced using the 54 gene Illumina TruSight Myeloid Panel. WfG interpreted and annotated all patients’ sequencing results, a subset of which were assessed manually to ascertain concordance. Results: 54 South Korean patients with hematological malignancies were analyzed (23 Acute Myeloid Leukemia, 12 myeloproliferative neoplasm, 5 myelodysplastic syndrome, 5 multiple myeloma and 9 others). Comparison of manual and WfG interpretation of 10 randomly selected cases yielded 90% (9/10) concordance and identification of 9 clinically actionable variants (33%) not found in manual interpretation. In total, WfG identified that 71% (38/54) of all cases had at least one clinically actionable therapeutic alteration (a variant targeted by a US FDA approved drug, off-label drug, or clinical trial). 33% (18/54) of cases had genes that were targeted by a US FDA approved therapy including JAK2, IDH1, IDH2, and FLT3. In cases without therapeutic alterations, WfG identified diagnostic or prognostic insights in an additional 20% (11/54) of patients. 9% (5/54) had no clinically actionable information. Conclusions: WfG variant interpretation correlated well with manually curated expert opinion and identified clinically actionable insights missed by manual interpretation. WfG has obviated the need for labor-intensive manual curation of clinical trials and therapy, enabling our center to exponentially scale our NGS operations.

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van Haastrecht, Max, Guy Golpur, Gilad Tzismadia, Rolan Kab, Cristian Priboi, Dumitru David, Adrian Răcătăian, et al. "A Shared Cyber Threat Intelligence Solution for SMEs." Electronics 10, no. 23 (November 24, 2021): 2913. http://dx.doi.org/10.3390/electronics10232913.

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Small- and medium-sized enterprises (SMEs) frequently experience cyberattacks, but often do not have the means to counter these attacks. Therefore, cybersecurity researchers and practitioners need to aid SMEs in their defence against cyber threats. Research has shown that SMEs require solutions that are automated and adapted to their context. In recent years, we have seen a surge in initiatives to share cyber threat intelligence (CTI) to improve collective cybersecurity resilience. Shared CTI has the potential to answer the SME call for automated and adaptable solutions. Sadly, as we demonstrate in this paper, current shared intelligence approaches scarcely address SME needs. We must investigate how shared CTI can be used to improve SME cybersecurity resilience. In this paper, we tackle this challenge using a systematic review to discover current state-of-the-art approaches to using shared CTI. We find that threat intelligence sharing platforms such as MISP have the potential to address SME needs, provided that the shared intelligence is turned into actionable insights. Based on this observation, we developed a prototype application that processes MISP data automatically, prioritises cybersecurity threats for SMEs, and provides SMEs with actionable recommendations tailored to their context. Subsequent evaluations in operational environments will help to improve our application, such that SMEs are enabled to thwart cyberattacks in future.

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Albors-Garrigos, Jose, Jose Carlos Ramos-Carrasco, and Angel Peiro-Signes. "Actional Intelligence, a Key Element for Actioning Knowledge. A Field Study Analysis." Journal of Information & Knowledge Management 15, no. 01 (March 2016): 1650006. http://dx.doi.org/10.1142/s0219649216500064.

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This paper develops a framework based on a new construct, actional intelligence, and highlights the key elements involved: individual and organisational value alignment and open learning attitudes. It analyses the relationships between them and the individual performance in the organisation. The model is tested using Smart partial least squares (PLS) with a sample of 175 employees in a large organisation belonging to the cosmetics sector. This research also analyses whether these constructs contribute to the organisational performance of individuals. This paper contributes to developing the seminal ideas of actionable knowledge proposed by Argyris and other academics. It concludes with practical implications that justify the use of knowledge management (KM) by managers in organisations. On the other hand, it provides a better understanding of the KM process and its systematisation. It covers the research gap related to actioning of knowledge of previous methods and signifies a more practical and understandable approach to KM and a closer perspective to business.

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Bernhardt, Douglas C. "‘I want it fast, factual, actionable’—tailoring competitive intelligence to executives' needs." Long Range Planning 27, no. 1 (February 1994): 12–24. http://dx.doi.org/10.1016/0024-6301(94)90003-5.

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Mendez Mena, Diego, and Baijian Yang. "Decentralized Actionable Cyber Threat Intelligence for Networks and the Internet of Things." IoT 2, no. 1 (December 30, 2020): 1–16. http://dx.doi.org/10.3390/iot2010001.

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Security presents itself as one of the biggest threats to the enabling and the deployment of the Internet of Things (IoT). Security challenges are evident in light of recent cybersecurity attacks that targeted major internet service providers and crippled a significant portion of the entire Internet by taking advantage of faulty and ill-protected embedded devices. Many of these devices reside at home networks with user-administrators who are not familiar with network security best practices, making them easy targets for the attackers. Therefore, security solutions are needed to navigate the insecure and untrusted public networks by automating protections through affordable and accessible first-hand network information sharing. This paper proposes and implements a proof of concept (PoC) to secure Internet Service Providers (ISPs), home networks, and home-based IoT devices using blockchain technologies. The results obtained support the idea of a distributed cyber threat intelligence data sharing network capable of protecting various stakeholders.

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Pal, Adrian. "Failures of Intell Services." International conference KNOWLEDGE-BASED ORGANIZATION 22, no. 1 (June 1, 2016): 105–8. http://dx.doi.org/10.1515/kbo-2016-0019.

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Abstract The main purpose of this article is to bring more light - further information and a better understanding – on the reasons for the failure of the intelligence services to combat terrorist attacks. Starting with September 11th 2001 in the USA, continuing with the attack on 11th March 2004 in Madrid, and the London one on 7th July 2005, and the latest events – the bombing of the Paris and Istanbul, all this made me pay particular attention to the reasons for the intelligence services not to fall within the limits of early warning, which often lead to real catastrophes. The article focuses on drawing, as close to reality as possible, the parallel between failures of intelligence and terrorist attacks. Therefore, I have focused on the connection between actionable intelligence and terrorist attack on September 11th, 2001 in the USA. The concept ‘actionable intelligence’ involves the transformation of information into real action, one meant to launch a protective barrier, and go on the offensive. Starting from this premise, and in parallel with the attack on September 11th, 2001, we can say that intell services failed to prevent and stop this event. ‘Balance bombing’ meant the first piece of the terrorist attacks puzzle, which has tended towards a broadening of horizon given the increasing number of attacks occurred so far. Thus, the article briefly reflects an assessment of the grounds for which the intelligence services continue to fail in fighting terrorism.

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Gao, Cong, and Yiyu Yao. "Actionable strategies in three-way decisions." Knowledge-Based Systems 133 (October 2017): 141–55. http://dx.doi.org/10.1016/j.knosys.2017.07.001.

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Roschuni, Celeste, Elizabeth Goodman, and Alice M. Agogino. "Communicating actionable user research for human-centered design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, no. 2 (April 18, 2013): 143–54. http://dx.doi.org/10.1017/s0890060413000048.

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AbstractIn human-centered design, user research drives design decisions by providing an understanding of end users. In practice, different people, teams, or even companies manage each step of the design process, making communication of user research results a critical activity. Based on an empirical study of current methods used by experts, this paper presents strategies for effectively communicating user research findings across organizational or corporate boundaries. To build researcher–client relationships, understand both user and client needs, and overcome institutional inertia, this paper proposes viewing user research clients asusersof user research outcomes. This reframing of the crafting of communication across boundaries as a parallel internal human-centered design process we refer to as adouble ethnography.

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Chemchem, Amine, François Alin, and Michael Krajecki. "Improving the Cognitive Agent Intelligence by Deep Knowledge Classification." International Journal of Computational Intelligence and Applications 18, no. 01 (March 2019): 1950005. http://dx.doi.org/10.1142/s1469026819500056.

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In this paper, a new idea is developed for improving the agent intelligence. In fact with the presented convolutional neural network (CNN) approach for knowledge classification, the agent will be able to manage its knowledge. This new concept allows the agent to select only the actionable rule class, instead of trying to infer its whole rule base exhaustively. In addition, through this research, we developed a comparative study between the proposed CNN approach and the classical classification approaches. As foreseeable the deep learning method outperforms the others in term of classification accuracy.

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Qiang Yang, C. A. Knoblock, and Xindong Wu. "Guest Editors' Introduction: Mining Actionable Knowledge on the Web." IEEE Intelligent Systems 19, no. 6 (November 2004): 30–31. http://dx.doi.org/10.1109/mis.2004.64.

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Johnsen, Hans Chr Garmann, Roger Normann, and Jens Kristian Fosse. "Reflexive democracy: creating actionable knowledge through regional development coalitions." AI & SOCIETY 19, no. 4 (May 21, 2005): 442–63. http://dx.doi.org/10.1007/s00146-005-0326-5.

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Mukherjee, Souvik, Ronald S. Bell, William N. Barkhouse, Santi Adavani, Peter G. Lelièvre, and Colin G. Farquharson. "High-resolution imaging of subsurface infrastructure using deep learning artificial intelligence on drone magnetometry." Leading Edge 41, no. 7 (July 2022): 462–71. http://dx.doi.org/10.1190/tle41070462.1.

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The use of drones fo r geophysical data acquisition and artificial intelligence (AI) for geophysical data processing, imaging, and interpretation are active focus areas in current industry and academic applications. Unlocking their cumulative potential in single-focus applications can have a transformative impact, possibly leading to dramatic cost reductions in key use cases and new application areas for enhanced actionable business intelligence. We present field study results from Texas and California that show the potential for imaging pipelines and other subsurface infrastructure by using AI-based methods on high-resolution aboveground magnetic data. The superior resolution and interpretability over conventional geophysical inversion is demonstrated. The method has the potential to provide actionable intelligence in several business-use cases for detecting and characterizing pipelines, crossing zones for multiple pipes, etc. at dramatically reduced costs. The advanced algorithms and workflows used resulted in a 100-fold increase in efficiency and delivered results in two days compared to what could take several months using generally available open-source deep learning AI workflows and software. Future direction of development is to validate against excavation-/drill-bit-/inline-tool-based ground truth and further extend and develop this process to deliver near real-time results. The techniques used are general and can be applied to other geophysical data including seismic, electromagnetic, and gravity at various scales and resolution.

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Et.al, Sandhya Sukhabogi. "A Theoretical review on the importance of Threat Intelligence Sharing & The challenges intricated." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 3950–56. http://dx.doi.org/10.17762/turcomat.v12i3.1684.

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Cyber Threat Intelligence (CTI) is the emerging strategy of cyber defense which helps organizations to combat the latest and more sophisticated cyber threats. Gathering this threat information, analyzing and communicating it between the security teams is very difficult and challenging because of the heterogeneous aspects involved. The necessity of sharing the intelligence related data collected by organizations is increasing day by day to counter the ever changing and highly dynamic threat landscape. In this paper an attempt is made to understand CTI concept and how it is collected and analyzed to form useful actionable intelligence are observed. The importance of Threat intelligence sharing, and various standards working in the area of TIS are also mentioned. Finally the primary challenges in TIS are given a light in a broad view

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Hazarika, Indrajit. "Artificial intelligence: opportunities and implications for the health workforce." International Health 12, no. 4 (April 17, 2020): 241–45. http://dx.doi.org/10.1093/inthealth/ihaa007.

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Abstract Healthcare involves cyclic data processing to derive meaningful, actionable decisions. Rapid increases in clinical data have added to the occupational stress of healthcare workers, affecting their ability to provide quality and effective services. Health systems have to radically rethink strategies to ensure that staff are satisfied and actively supported in their jobs. Artificial intelligence (AI) has the potential to augment provider performance. This article reviews the available literature to identify AI opportunities that can potentially transform the role of healthcare providers. To leverage AI’s full potential, policymakers, industry, healthcare providers and patients have to address a new set of challenges. Optimizing the benefits of AI will require a balanced approach that enhances accountability and transparency while facilitating innovation.

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Kalanat, Nasrin, and Eynollah Khanjari. "Extracting actionable knowledge from social networks with node attributes." Expert Systems with Applications 152 (August 2020): 113382. http://dx.doi.org/10.1016/j.eswa.2020.113382.

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Wagner, Thomas D., Esther Palomar, Khaled Mahbub, and Ali E. Abdallah. "A Novel Trust Taxonomy for Shared Cyber Threat Intelligence." Security and Communication Networks 2018 (June 5, 2018): 1–11. http://dx.doi.org/10.1155/2018/9634507.

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Cyber threat intelligence sharing has become a focal point for many organizations to improve resilience against cyberattacks. The objective lies in sharing relevant information achieved through automating as many processes as possible without losing control or compromising security. The intelligence may be crowdsourced from decentralized stakeholders to collect and enrich existing information. Trust is an attribute of actionable cyber threat intelligence that has to be established between stakeholders. Sharing information about vulnerabilities requires a high level of trust because of the sensitive information. Some threat intelligence platforms/providers support trust establishment through internal vetting processes; others rely on stakeholders to manually build up trust. The latter may reduce the amount of intelligence sources. This work presents a novel trust taxonomy to establish a trusted threat sharing environment. 30 popular threat intelligence platforms/providers were analyzed and compared regarding trust functionalities. Trust taxonomies were analyzed and compared. Illustrative case studies were developed and analyzed applying our trust taxonomy.

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Tran, Nam K., Samer Albahra, Larissa May, Sarah Waldman, Scott Crabtree, Scott Bainbridge, and Hooman Rashidi. "Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing." Clinical Chemistry 68, no. 1 (December 30, 2021): 125–33. http://dx.doi.org/10.1093/clinchem/hvab239.

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Abstract Background Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available. Content In this review we: (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease testing, and (d) discuss the future evolution AI/ML for infectious disease testing in both laboratory and point-of-care applications. Summary The review provides an important educational overview of AI/ML technique in the context of infectious disease testing. This includes supervised ML approaches, which are frequently used in laboratory medicine applications including infectious diseases, such as COVID-19, sepsis, hepatitis, malaria, meningitis, Lyme disease, and tuberculosis. We also apply the concept of “data fusion” describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge.

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CAO, LONGBING, and CHENGQI ZHANG. "THE EVOLUTION OF KDD: TOWARDS DOMAIN-DRIVEN DATA MINING." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 04 (June 2007): 677–92. http://dx.doi.org/10.1142/s0218001407005612.

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Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information, in which domain intelligence may not be necessary in targeting the demonstration of an algorithm. Often knowledge discovered is not generally interesting to business needs. Comparably, real-world applications rely on knowledge for taking effective actions. In retrospect of the evolution of KDD, this paper briefly introduces domain-driven data mining to complement traditional KDD. Domain intelligence is highlighted towards actionable knowledge discovery, which involves aspects such as domain knowledge, people, environment and evaluation. We illustrate it through mining activity patterns in social security data.

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Zeng, Jia, and Md Abu Shufean. "Molecular-based precision oncology clinical decision making augmented by artificial intelligence." Emerging Topics in Life Sciences 5, no. 6 (December 7, 2021): 757–64. http://dx.doi.org/10.1042/etls20210220.

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The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians’ decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.

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Rashid, Mudassir M., Mohammad Reza Askari, Canyu Chen, Yueqing Liang, Kai Shu, and Ali Cinar. "Artificial Intelligence Algorithms for Treatment of Diabetes." Algorithms 15, no. 9 (August 26, 2022): 299. http://dx.doi.org/10.3390/a15090299.

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Artificial intelligence (AI) algorithms can provide actionable insights for clinical decision-making and managing chronic diseases. The treatment and management of complex chronic diseases, such as diabetes, stands to benefit from novel AI algorithms analyzing the frequent real-time streaming data and the occasional medical diagnostics and laboratory test results reported in electronic health records (EHR). Novel algorithms are needed to develop trustworthy, responsible, reliable, and robust AI techniques that can handle the imperfect and imbalanced data of EHRs and inconsistencies or discrepancies with free-living self-reported information. The challenges and applications of AI for two problems in the healthcare domain were explored in this work. First, we introduced novel AI algorithms for EHRs designed to be fair and unbiased while accommodating privacy concerns in predicting treatments and outcomes. Then, we studied the innovative approach of using machine learning to improve automated insulin delivery systems through analyzing real-time information from wearable devices and historical data to identify informative trends and patterns in free-living data. Application examples in the treatment of diabetes demonstrate the benefits of AI tools for medical and health informatics.

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Berger, Nathan A. "Actionable intelligence provided by pancreatic cancer genomic landscape: are targets for curative therapy on the map?" Translational Cancer Research 5, S2 (August 2016): S243—S247. http://dx.doi.org/10.21037/tcr.2016.08.07.

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Arifa, Bithi, and Kumar Suker. "Geography Information System (GIS) and Geography Teaching Material." Sumatra Journal of Disaster, Geography and Geography Education 2, no. 1 (June 6, 2018): 124. http://dx.doi.org/10.24036/sjdgge.v2i1.141.

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GIS technology applies geographic science with tools for understanding and collaboration. It helps people reach a common goal: to gain actionable intelligence from all types of data. GIS integrates many different kinds of data layers using spatial location. Most data has a geographic component. GIS data includes imagery, features, and basemaps linked to spreadsheets and tables. Spatial analysis lets you evaluate suitability and capability, estimate and predict, interpret and understand, and much more, lending new perspectives to your insight and decision-making.

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Hasan, Md Mahadi, Muhammad Usama Islam, Muhammad Jafar Sadeq, Wai-Keung Fung, and Jasim Uddin. "Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment." Sensors 23, no. 1 (January 3, 2023): 527. http://dx.doi.org/10.3390/s23010527.

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Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence’s role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients’ mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.

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Cao, Longbing. "Beyond AutoML: Mindful and Actionable AI and AutoAI With Mind and Action." IEEE Intelligent Systems 37, no. 5 (September 1, 2022): 6–18. http://dx.doi.org/10.1109/mis.2022.3207860.

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44

Williams, Anna Marie, Yong Liu, Kevin R. Regner, Fabrice Jotterand, Pengyuan Liu, and Mingyu Liang. "Artificial intelligence, physiological genomics, and precision medicine." Physiological Genomics 50, no. 4 (April 1, 2018): 237–43. http://dx.doi.org/10.1152/physiolgenomics.00119.2017.

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Big data are a major driver in the development of precision medicine. Efficient analysis methods are needed to transform big data into clinically-actionable knowledge. To accomplish this, many researchers are turning toward machine learning (ML), an approach of artificial intelligence (AI) that utilizes modern algorithms to give computers the ability to learn. Much of the effort to advance ML for precision medicine has been focused on the development and implementation of algorithms and the generation of ever larger quantities of genomic sequence data and electronic health records. However, relevance and accuracy of the data are as important as quantity of data in the advancement of ML for precision medicine. For common diseases, physiological genomic readouts in disease-applicable tissues may be an effective surrogate to measure the effect of genetic and environmental factors and their interactions that underlie disease development and progression. Disease-applicable tissue may be difficult to obtain, but there are important exceptions such as kidney needle biopsy specimens. As AI continues to advance, new analytical approaches, including those that go beyond data correlation, need to be developed and ethical issues of AI need to be addressed. Physiological genomic readouts in disease-relevant tissues, combined with advanced AI, can be a powerful approach for precision medicine for common diseases.

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Jansaenroj, Krit. "ATTITUDE OF MILLENNIALS AND GENERATION Z TOWARDS ARTIFICIAL INTELLIGENCE IN SURGERY." International Journal of Advanced Research 10, no. 7 (July 31, 2022): 921–26. http://dx.doi.org/10.21474/ijar01/15114.

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Because of its increasing ability to turn ambiguity and complexity in data into actionable-though imperfect-clinical choices or suggestions, artificial intelligence (AI) has the potential to change health care practices. Trust is the only mechanism that influences physicians use and adoption of AI in the growing interaction between humans and AI. Trust is a psychological process that enables people to deal with ambiguity in what they know and do not know. The purpose of this online survey was to determine the relationship between age groups, familiarity, and trustworthiness present towards AI through the question of whether particular participants would prefer a human or an AI surgeon if they had to undergo a surgery.The results showed that age groups and trustworthiness are not correlated, due to a variety of factors, and,also, familiarity is not correlated with age group.

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Dawood, Zubeida, and Carien Van 't Wout. "An Ontology Towards Predicting Terrorism Events." International Journal of Cyber Warfare and Terrorism 12, no. 1 (January 1, 2022): 1–13. http://dx.doi.org/10.4018/ijcwt.311421.

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Although there is an increasing amount of information for counter-terrorism operations freely available online, it is a complex process to extract relevant information and to detect useful patterns in the data in order for intelligence functionaries to identify threats and to predict possible terror attacks. Automation is required for intelligent decision-making. To assist with this, in this paper, the researchers propose an ontology-based data access system for counter-terrorism. The system will enable intelligence analysts to perform specialised semantic searches about terrorist events or groups for analysis using an ontology. In this paper, the researchers present the ontology that was created by following an existing methodology for ontology development, and an ontology-based data access system together with all the components used in development (i.e., databases, web-scraper tools, ontology-based data access software, and data sources). Lastly, the ontology is demonstrated by means of use cases with example queries for generating actionable intelligence for operations.

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Summers, Lucia, and D. Kim Rossmo. "Offender interviews: implications for intelligence-led policing." Policing: An International Journal 42, no. 1 (February 11, 2019): 31–42. http://dx.doi.org/10.1108/pijpsm-07-2018-0096.

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PurposeIntelligence-led policing (ILP) involves the analysis of data to inform the development and implementation of strategic actions aimed at more efficiently reducing crime. The purpose of this paper is to examine how chronic acquisitive offenders – a focus of ILP – respond to police patrol, and how this knowledge can be turned into actionable strategies to reduce crime.Design/methodology/approachInterviews were conducted with 137 chronic offenders who had multiple convictions for burglary, robbery and/or vehicle crime. The interviews involved the collection of both qualitative and quantitative data, including responses to situational crime vignettes.FindingsWhen encountering police patrols, criminals were initially more likely to displace (e.g. committing crime elsewhere and/or later in the day) than to desist from offending. Some of the conditions under which police patrol was most effective were identified, including offenders’ fear of being recognized by officers. Repeated thwarted crime attempts appeared to be most impactful, with even the most chronic offenders becoming “worn down.”Practical implicationsThe profiles of top offenders should be systematically disseminated to front line officers to augment the effectiveness of police patrol and minimize the possibility of crime displacement.Originality/valueOffender interviews are a valuable source of information but they have been underutilized within an ILP framework. This research illustrates how offender interview research can inform and support the role of police in preventing crime.

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Kalanat, Nasrin, Alireza Khanshan, and Eynollah Khanjari. "Actionable knowledge discovery from social networks using causal structures of structural features." Journal of Intelligent & Fuzzy Systems 39, no. 1 (July 17, 2020): 489–501. http://dx.doi.org/10.3233/jifs-191519.

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Ranganathan, Jaishree, Allen S. Irudayaraj, Arunkumar Bagavathi, and Angelina A. Tzacheva. "Actionable pattern discovery for Sentiment Analysis on Twitter Data in clustered environment." Journal of Intelligent & Fuzzy Systems 34, no. 5 (May 24, 2018): 2849–63. http://dx.doi.org/10.3233/jifs-169472.

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50

Lamsal, Rabindra, and T. V. Vijay Kumar. "Twitter-Based Disaster Response Using Recurrent Nets." International Journal of Sociotechnology and Knowledge Development 13, no. 3 (July 2021): 133–50. http://dx.doi.org/10.4018/ijskd.2021070108.

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Twitter has become the major source of data for the research community working on the social computing domain. The microblogging site receives millions of tweets every day on its platform. Earlier studies have shown that during any disaster, the frequency of tweets specific to an event grows exponentially, and these tweets, if monitored, processed, and analyzed, can contain actionable information relating to the event. However, during disasters, the number of tweets can be in the hundreds of thousands thereby necessitating the design of a semi-automated artificial intelligence-based system that can extract actionable information based on which steps can be taken for effective disaster response. This paper proposes a Twitter-based disaster response system that uses recurrent nets for training a classifier on a disaster specific tweets dataset. The proposed system would enable timely dissemination of information to various stakeholders so that timely response and proactive measures can be taken in order to reduce the severe consequences of disasters. Experimental results show that the recurrent nets outperform the traditional machine learning algorithms with regard to accuracy in classifying disaster-specific tweets.

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