Relevant bibliographies by topics / Decision implementation

Contents

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

Academic literature on the topic 'Decision implementation'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Decision implementation"

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Parlindungan and HariSupriadi. "Implementation Decision Tree Algorithm for Ecommerce Website." International Journal of Psychosocial Rehabilitation 24, no. 02 (2020): 3611–14. http://dx.doi.org/10.37200/ijpr/v24i2/pr200682.

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STRUHARIK, RASTISLAV J. R., and LADISLAV A. NOVAK. "HARDWARE IMPLEMENTATION OF DECISION TREE ENSEMBLES." Journal of Circuits, Systems and Computers 22, no. 05 (2013): 1350032. http://dx.doi.org/10.1142/s0218126613500321.

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In this paper, several hardware architectures for the realization of ensembles of axis-parallel, oblique and nonlinear decision trees (DTs) are presented. Hardware architectures for the implementation of a number of ensemble combination rules are also presented. These architectures are universal and can be used to combine predictions from any type of classifiers, such as decision trees, artificial neural networks (ANNs) and support vector machines (SVMs). Proposed architectures are suitable for the implementation using Field Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASIC). Experiment results obtained using 29 datasets from the standard UCI Machine Learning Repository database suggest that the FPGA implementations offer significant improvement in the classification time in comparison with the traditional software implementations. Greatest improvement can be achieved using the SP2-P architecture implemented on the FPGA achieving 416.53 times faster classification speed on average, compared with the software implementation. This result has been achieved on the FPGA working at 135.51 MHz on average, which is 33.21 times slower than the operating frequency of the general purpose computer on which the software implementation has been executed.
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STRUHARIK, RASTISLAV J. R., and LADISLAV A. NOVAK. "EVOLVING DECISION TREES IN HARDWARE." Journal of Circuits, Systems and Computers 18, no. 06 (2009): 1033–60. http://dx.doi.org/10.1142/s0218126609005526.

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This paper, according to the best of our knowledge, provides the very first solution to the hardware implementation of the complete decision tree inference algorithm. Evolving decision trees in hardware is motivated by a significant improvement in the evolution time compared to the time needed for software evolution and efficient use of decision trees in various embedded applications (robotic navigation systems, image processing systems, etc.), where run-time adaptive learning is of particular interest. Several architectures for the hardware evolution of single oblique or nonlinear decision trees and ensembles comprised from oblique or nonlinear decision trees are presented. Proposed architectures are suitable for the implementation using both Field Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASIC). Results of experiments obtained using 29 datasets from the standard UCI Machine Learning Repository database suggest that the FPGA implementations offer significant improvement in inference time when compared with the traditional software implementations. In the case of single decision tree evolution, FPGA implementation of H_DTS2 architecture has on average 26 times shorter inference time when compared to the software implementation, whereas FPGA implementation of H_DTE2 architecture has on average 693 times shorter inference time than the software implementation.
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Amarini, Indriati. "IMPLEMENTATION OF JUDICIAL ACTIVISM IN JUDGE’S DECISION." Jurnal Hukum dan Peradilan 8, no. 1 (2019): 21. http://dx.doi.org/10.25216/jhp.8.1.2019.21-38.

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The task of the judge in realizing justice is inseparable from the decisions that are made. A qualified judge's decision is obtained through the judge's thought process through a choice of judges that reflects judicial activism. The problem in this research is how judicial activism is used by judges in issuing decisions and how the implementation of judicial activism in decision making. Judicial Activism is the choice of decision making by judges in order to realize justice. Judicial activism in the Indonesian judicial system is regulated in Article 5 paragraph (1) of Law Number 48 of 2009 concerning Judicial Power. The law requires judges to explore the law and sense of justice that lives in society. The implementation of judicial activism is carried out by judges through means of legal discovery. Through means of legal discovery, judges play an active role in realizing justice as a law that lives in a dynamically developing society.
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Costeniuc, Marius, Michaela Schnetzer, and Luca Taschini. "Entry and Exit Decision Problem with Implementation Delay." Journal of Applied Probability 45, no. 04 (2008): 1039–59. http://dx.doi.org/10.1017/s0021900200004964.

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We study investment and disinvestment decisions in situations where there is a time lagd> 0 from the timetwhen the decision is taken to the timet+dwhen the decision is implemented. In this paper we apply the probabilistic approach to the combined entry and exit decisions under the Parisian implementation delay. In particular, we prove the independence between Parisian stopping times and a general Brownian motion with drift stopped at the stopping time. Relying on this result, we solve the constrained maximization problem, obtaining an analytic solution to the optimal ‘starting’ and ‘stopping’ levels. We compare our results with the instantaneous entry and exit situation, and show that an increase in the uncertainty of the underlying process hastens the decision to invest or disinvest, extending a result of Bar-Ilan and Strange (1996).
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Costeniuc, Marius, Michaela Schnetzer, and Luca Taschini. "Entry and Exit Decision Problem with Implementation Delay." Journal of Applied Probability 45, no. 4 (2008): 1039–59. http://dx.doi.org/10.1239/jap/1231340232.

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We study investment and disinvestment decisions in situations where there is a time lagd> 0 from the timetwhen the decision is taken to the timet+dwhen the decision is implemented. In this paper we apply the probabilistic approach to the combined entry and exit decisions under the Parisian implementation delay. In particular, we prove the independence between Parisian stopping times and a general Brownian motion with drift stopped at the stopping time. Relying on this result, we solve the constrained maximization problem, obtaining an analytic solution to the optimal ‘starting’ and ‘stopping’ levels. We compare our results with the instantaneous entry and exit situation, and show that an increase in the uncertainty of the underlying process hastens the decision to invest or disinvest, extending a result of Bar-Ilan and Strange (1996).
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Moustaid, Bouzekri, and Mohamed Fakir. "Implementation of Business Intelligence For Sales Management." IAES International Journal of Artificial Intelligence (IJ-AI) 5, no. 1 (2016): 22. http://dx.doi.org/10.11591/ijai.v5.i1.pp22-34.

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<p>Today's company operates in a socio-economic environment increasingly demanding. In such a context, it is obliged to adopt a competitive approach by exploiting at best the information that it possesses for developing appropriate action plans and taking effective decisions. The decision support systems provide to the enterprise the tools that help it for decision-making based on techniques and methodologies coming from domain of applied mathematics such as optimization, statistics and theory of the decision. The decision support systems are composed of various components such as data warehouses, ETL tools and reporting and analysis tools.</p>
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Ervin, Kaye E., Irene Blackberry, and Helen Haines. "Implementation of Shared Decision-Making in Australia." Asia Pacific Journal of Health Management 11, no. 2 (2016): 10–11. http://dx.doi.org/10.24083/apjhm.v11i2.177.

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Shared decision-making (SDM) is the process of clinicians and patients participating jointly in making healthcare decisions, having discussed evidence-based treatment options and the potential risks and benefits of each option, taking into consideration the patient’s individual preferences and values. SDM is ubiquitous in Australian healthcare policy. While there is good evidence for utilising SDM, clinicians’ knowledge of SDM, the current uptake, effectiveness and acceptability of SDM in Australia is largely unknown. The challenges perceived by clinicians to implementing SDM in clinical practice and potential moral, legal and ethical dilemmas require further debate and consideration.
 Abbreviations: SDM – Shared Decision-Making.
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Erokhina, O. V. "Implementation of Machine Learning Methods to solve Political Problems." Humanities and Social Sciences. Bulletin of the Financial University 10, no. 3 (2020): 67–73. http://dx.doi.org/10.26794/2226-7867-2020-10-3-67-73.

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The political reality described by non-linearity of processes and significant influence of informal mechanisms of interaction of decision-making subjects-political elites and leaders. One of the most promising areas that open up new opportunities for studying political problems is the synthesis of “traditional” methodology of political analysis and methods of artificial intelligence. The purpose of this work is to build a methodological model in the form of a “decision tree” and use it to analyze the process of making personnel decisions concerning the heads of the subjects of the Russian Federation. Chronological limits: 2000–2020 years. It is necessary to solve the following tasks to achieve such a goal. 1. Study the main trends and priorities of the Federal centre’s personnel policy in relation to regional heads in the period under review. 2. Identification of key factors affecting the political positions of the leaders of regions of the Russian Federation based on the analysis of data on the performance parameters of the regional executive power. 3. Generalization of data on personnel decisions made in 2000–2018 and the construction of a “decision tree” that provides grounds for predicting future personnel decisions following the selected algorithm. The article substantiates the adequacy of using the decision tree for analyzing and predicting political decisions on the example of the personnel policy of federal government structures in relation to the regions of the Russian Federation. Based on the results of the work, 13 formal and informal criteria for assessing the stability of political positions of governors with varying degrees of probability of their use in the process of making personnel decisions were formulated. The author proposes to use these criteria as attributes for training the decision tree. Due to the significant amount of data, the paper presents a fragment of the decision tree that clearly illustrates the possibility of using the C 4.5 algorithm to solve political problems.
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Simonian, Armen I., and Jason H. Lam. "Implementation of clinical decision support rules." American Journal of Health-System Pharmacy 73, no. 7 (2016): 436–39. http://dx.doi.org/10.2146/ajhp150122.

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Dissertations / Theses on the topic "Decision implementation"

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Danylenko, Antonina. "Decisions : Algebra and Implementation." Licentiate thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-16283.

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Processing decision information is a constitutive part in a number of applicationsin Computer Science fields. In general, decision information can be used to deduce the relationship between a certain context and a certain decision. Decision information is represented by a decision model that captures this information. Frequently used examples of decision models are decision tables and decision trees. The choice of an appropriate decision model has an impact on application performance in terms of memory consumption and execution time. High memory expenses can possibly occur due to redundancy in a decision model; and high execution time is often a consequence of an unsuitable decision model. Applications in different domains try to overcome these problems by introducing new data structures or algorithms for implementing decision models. These solutions are usually domain-specificand hard to transfer from one domain to another. Different application domains of Computer Science often process decision information in a similar way and, hence, have similar problems. We should thus be able to present a unifying approach that can be applicable in all application domains for capturing and manipulating decision information. Therefore, the goal of this thesis is (i) to suggest a general structure(Decision Algebra) which provides a common theoretical framework that captures decision information and defines operations (signatures) for storing, accessing, merging, approximating, and manipulating such information along with some general algebraic laws regardless of the used implementation. Our Decision Algebra allows defining different construction strategiesfor decision models and data structures that capture decision information as implementation variants, and it simplifies experimental comparisons between them. Additionally, this thesis presents (ii) an implementation of Decision Algebra capturing the information in a non-redundant way and performing the operations efficiently. In fact, we show that existing decision models that originated in the field of Data Mining and Machine Learning and variants thereof as exploited in special algorithms can be understood as alternative implementation variants of the Decision Algebra by varying the implementations of the Decision Algebra operations. Hence, this work (iii) will contribute to a classification of existing technology for processing decision information in different application domains of Computer Science.<br><p>A thesis for the Degree of Licentiate of Philosophy in Computer Science.</p>
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Schlumberger, Charles. "The implementation of the Yamoussoukro decision." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40822.

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This thesis examines progress made in liberalizing the economic regulation of air transport services in Africa through evaluating advancements made in the implementation of the Yamoussoukro Decision, and the effects observed in the sector. Despite the fact that the Yamoussoukro Decision was reached ten years ago, very little is known about the progress achieved by its implementation in various regions of Africa. While it is generally acknowledged that the formal application of the principles of the Decision to liberalize air services remains incomplete, the thesis outlines and establishes developments in various African markets that point toward a disconnection between the policy and legal framework and the operational realities of the sector. This disconnection, driven mainly by the governments of a small number of African countries who aim at protecting their weak or failing national carriers by refusing to liberalize their air transport markets irrespective of the obligations they have assumed under the Decision, has hindered full liberalization of the African air transport sector and effectively prevented African nations from taking full advantage of the positive economic impacts of air transportation. This thesis reviews these economic aspects, as well as the potential economic impact of full liberalization of African air transport. This thesis demonstrates that, although at the operational level significant progress has been achieved in the liberalization of air services through the signing of numerous bilateral agreements, policy implementation remains incomplete or stagnant in many regions of the continent, thereby hindering the full deployment of the economic potential of Africa. The purpose of this research is to recommend a set of policy measures for African governments in order to move towards full application of the Yamoussoukro Decision to liberalize air services.<br>La présente thèse traite des progrès de la libéralisation des services de transport aérien en Afrique. Elle s’appuie pour cela sur une évaluation des avancées réalisées dans la mise en œuvre de la Décision de Yamoussoukro ainsi que des effets observés dans le secteur.La Décision de Yamoussoukro libéralisant les services aériens en Afrique date d’il y a déjà dix ans, et pourtant l’état d’avancement de sa mise en œuvre dans les différentes régions est souvent mal connu. Alors qu’il est communément admis que l’application formelle des principes énoncés dans l’accord reste incomplète, la thèse repère nombre de développements intervenus sur différents marchés africains et tendant à mettre en lumière le décalage entre les réalités pratiques du secteur et la réalisation du cadre politique et juridique prévu par la Décision. Bien que ce décalage soit surtout le fait d’un petit nombre de gouvernements soucieux de protéger leurs transporteurs défaillants, il fait obstacle à une libéralisation complète, empêchant ainsi l’Afrique de profiter à plein des impacts positifs du transport aérien. La thèse passe en revue les implications économiques à attendre d’une libéralisation complète. Le but de cette recherche est d’identifier un ensemble de mesures à recommander aux gouvernements africains pour faire progresser la libéralisation effective des services aériens.La thèse démontre par ailleurs que la libéralisation des services aériens a connu des progrès substantiels au plan pratique grâce à la signature de nombreux accords bilatéraux, alors même que la mise en œuvre de la politique de libéralisation reste à la traîne dans plusieurs régions du continent, obérant ainsi le plein déploiement du potentiel économique du transport aérien pour l’Afrique.
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Beck, Jason. "Implementation and Experimentation with C4.5 Decision Trees." Honors in the Major Thesis, University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1157.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.<br>Bachelors<br>Engineering and Computer Science<br>Computer Engineering
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LAPEBIE, GALLIANO CHRISTINE. "Implementation et optimisation de strategies de decision." Rennes 1, 1995. http://www.theses.fr/1995REN10131.

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Nous modelisons le choix multiattribut en decision individuelle par un processus base sur des strategies de decision, mises a jour par des psychologues en situation reelle, et tenant compte des capacites limitees (memoire, calculs,) de l'etre humain, et ce, afin d'implementer des systemes d'aide a la decision reposant sur ce principe. Chacune des strategies possede un certain nombre de parametres (transformation entre valeurs objectives et subjectives, seuils, importances relatives des attributs de description,) dont les valeurs dependent de l'expert etudie. Nous nous limitons au cas ou la decision ne s'etablit qu'entre deux objets. Dans ce cas, nous etudions les proprietes possedees par ces strategies, ayant trait a leur transitivite, aux conditions de pareto, et egalement a la reduction, que nous definissons ici, et qui relit les strategies entre elles. Pour les implementer efficacement, nous utilisons des heuristiques de type recuit simule, recherche tabou (que nous modifions en une recherche tabou a double voisinage et double fonction de cout), algorithmes genetiques. Finalement, nous testons les aspects apprentissage et generalisation de notre modele avec des conseillers d'orientation scolaire et dans le milieu bancaire
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Asim, Saad F. Asim. "The Binary Decision Diagram: Abstraction and Implementation." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu152414624378423.

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Badr, Bashar. "Implementation of decision trees for embedded systems." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/14711.

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This research work develops real-time incremental learning decision tree solutions suitable for real-time embedded systems by virtue of having both a defined memory requirement and an upper bound on the computation time per training vector. In addition, the work provides embedded systems with the capabilities of rapid processing and training of streamed data problems, and adopts electronic hardware solutions to improve the performance of the developed algorithm. Two novel decision tree approaches, namely the Multi-Dimensional Frequency Table (MDFT) and the Hashed Frequency Table Decision Tree (HFTDT) represent the core of this research work. Both methods successfully incorporate a frequency table technique to produce a complete decision tree. The MDFT and HFTDT learning methods were designed with the ability to generate application specific code for both training and classification purposes according to the requirements of the targeted application. The MDFT allows the memory architecture to be specified statically before learning takes place within a deterministic execution time. The HFTDT method is a development of the MDFT where a reduction in the memory requirements is achieved within a deterministic execution time. The HFTDT achieved low memory usage when compared to existing decision tree methods and hardware acceleration improved the performance by up to 10 times in terms of the execution time.
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Aurélio, Diogo. "Lean management implementation: a decision making framework." Master's thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/9685.

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Submitted to the graduate faculty Universidade Nova de Lisboa – Faculdade de Ciências e Tecnologia in partial fulfillment of the requirements for the degree of Master in Industrial Engineering<br>In this thesis the author proposes a thinking framework for decision makers considering the implementation of Lean Management methodologies in their industrial contex. The framework takes both internal and external context of each case into consderation. A logical order when considering Lean’s implementation is proposed, where strategic considerations should be taken first into consideration, and operational considerations after. Moreover, the framework proposes the power and leverage perspective on transactional exchange as the central factor to determine the strategic success of any implementation. Finally when it comes to operational considerations, a key thinking factor is taken into consideration, which is the potential penetration ability of Lean techniques according to the internal and external context. The framework is applied to a portuguese leading company in the molding industry that has already applied some Lean Management methodologies, which is Logoplaste. Results lead to conclusions that, not only Lean’s suitability varies according to context, but also that the potential penetration ability of Lean techniques decreases as the compliance level to Lean’s core principles increases, and that companies where Lean techniques with low penetration ability are suitable to be used, have a higher propensity to evolve into higher Lean maturity stages.
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Boland, Laura. "Implementation of Shared Decision Making in Pediatric Clinical Practice." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38181.

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Shared decision making (SDM) is rarely used in pediatric clinical practice. The purpose of this dissertation was to explore factors influencing SDM implementation in pediatric clinical practice. We conducted three studies that were guided by the Ottawa Model of Research Use (OMRU): Study 1 was a systematic review using Cochrane methods and the Mixed Methods Appraisal Tool to determine pediatric SDM barriers and facilitators from multiple perspectives. Eighty studies, of low to high quality, were included. At each OMRU level, frequently cited barriers were: option features (decision), poor quality information (innovation), emotional state (adopter), power relations (relational), and insufficient time (environment). Frequently cited facilitators were: lower stake decisions (decision), agreement with SDM (adopter), high quality information (innovation), trust and respect (relational), and SDM tools/resources (environment). Across participant types, frequently cited barriers were: insufficient time (healthcare providers (HCP)), option features (parents), power imbalances (children), and HCPs’ SDM skills (observers). Frequently cited facilitators were: good quality information (HCPs) and agreement with SDM (parents/children). Study 2 was a post-test design that evaluated SDM knowledge and acceptability of learners who completed the Ottawa Decision Support Tutorial (ODST). Most learners were HCPs (62%). Overall, ODST learners had a median knowledge test score of 8/10 (IQR = 7-9; n=6604) and 90% reported good or excellent impressions (n=4276) after completing the tutorial. Few learners suggested improvements. Study 3 used mixed methods to evaluate pediatric HCPs’ perceived SDM barriers and facilitators after training (ODST plus workshop). Participants completed a SDM barrier survey (n=60; 88% response rate) and semi-structured interview (n=11). Their intention to use SDM was high (mean score = 5.6/7, SD=0.8). However, 90% of respondents reported minimal SDM use after training. Main barriers were lack of buy-in (adopter level) and time constraints (environmental level). Healthcare providers wanted a team-based approach to SDM training (training level). Adopters face numerous and diverse barriers to SDM use, before and after SDM training. Pediatric HCPs who completed the ODST were knowledgeable about SDM. Despite positive intentions, training alone was insufficient to achieve routine SDM use. These findings can inform intervention development to promote SDM implementation in pediatric clinical practice.
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Chen, Chen. "SQL Implementation of Value Reduction with Multiset Decision Tables." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1387495607.

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Chen, Keyu. "A strategic decision making framework for organisational BIM implementation." Thesis, Cardiff University, 2015. http://orca.cf.ac.uk/88267/.

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Decision making during the adoption of Building Information Modelling (BIM) in current AEC projects is believed as a key element to improve both BIM performance and project outcome. In order to provide the most informed decision and strategic plan, two vital elements are required: a comprehensive set of decision making criteria and a reasonable priority system. The literature analysis has revealed that existing assessment frameworks have limitations concerning these two elements. Therefore, this research has been designed to develop a more effective BIM evaluation Framework (BeF), to assist new BIM users and also provide a more effective implementation approach for BIM. In order to accomplish this objective, research steps of theoretical and empirical nature have been adopted: (a) a multi-dimensional BIM implementation Framework (BiF) was proposed based on the literature review; (b) use of a case study to test the proposed BiF on a real-life project; (c) a questionnaire approach to test the comprehensiveness of the proposed BiF on an industry level; (d) applying the Delphi method to further refine the proposed criteria in a specific context; (e) using the Analytical Hierarchy Process (AHP) to develop a BeF and providing priority shifting for a more preferable strategic goal in Arup ShenZhen office (ASZ); (f) developing a validation system to prove the efficacy of the proposed BeF. The adoption priority and approach of BIM could be influenced by policies, culture, business structure, legislation etc. As a result, a specific context, China has been selected for this work. The research result could assist decision making in BIM management in the ASZ for a higher BIM performance. The framework by the Delphi method is suitable for the selected context: China. The proposed Delphi and AHP methodological framework can be replicated to assist decision making of BIM management in any AEC organisation.
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Books on the topic "Decision implementation"

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Palfrey, Thomas R. Bayesian implementation. Harwood Academic Publishers, 1993.

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Drechsler, Rolf. Binary decision diagrams: Theory and implementation. Kluwer Academic Publishers, 1998.

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Drechsler, Rolf. Binary Decision Diagrams: Theory and Implementation. Springer US, 1998.

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Takahashi, Peggy K. Japan: Implementation versus decision : an organic approach. Sophia University, 1985.

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1955-, Wettestad Jørgen, ed. EU emissions trading: Initiation, decision-making and implementation. Ashgate, 2008.

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Lewandowski, Andrzej, Paolo Serafini, and Maria Grazia Speranza, eds. Methodology, Implementation and Applications of Decision Support Systems. Springer Vienna, 1991. http://dx.doi.org/10.1007/978-3-7091-2606-6.

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Hänle, Michael. Systeme zur Unterstützung von Gruppenentscheidungen: Konzeption und Implementation eines Prototypen. J. Eul, 1993.

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W, Grigsby David, ed. Strategic management: Formulation and implementation. PWS-KENT, 1992.

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Booz, Allen & Hamilton. Environmental information management and decision support system: Implementation handbook. National Academy Press, 2003.

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Office, California Energy Commission Technology Market Development. Renewables portfolio standard: Decision on Phase 2 implementation issues. California Energy Commission, Technology Systems Division, Technology Market Development Office, 2003.

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Book chapters on the topic "Decision implementation"

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Schneeweiss, Christoph. "Implementation." In Distributed Decision Making. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24724-1_8.

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Hester, Patrick T., and Kevin MacG Adams. "Decision Implementation." In Systemic Decision Making. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54672-8_14.

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Schneeweiss, Christoph. "Implementation." In Hierarchies in Distributed Decision Making. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03830-7_8.

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Stark, John. "Examples of PDM Implementation." In Decision Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16134-7_20.

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Bresnick, Terry A., and Gregory S. Parnell. "Enable Decision Implementation." In Handbook of Decision Analysis. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118515853.ch14.

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Stark, John. "PLM Implementation Strategy and Plan." In Decision Engineering. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-546-0_27.

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McDonald, Kenneth W., and Daniel J. McCarthy. "Solution Implementation." In Decision Making in Systems Engineering and Management. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470926963.ch13.

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Munkvold, Bjørn Erik, Kristin Eim, and Øyvind Husby. "Collaborative IS Decision-Making: Analyzing Decision Process Characteristics and Technology Support." In Groupware: Design, Implementation, and Use. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11560296_24.

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Kóczy, László Á. "Implementation of the Core." In Theory and Decision Library C. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69841-0_6.

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Nakashima, Tomoharu, Keigo Tanaka, Noriyuki Fujimoto, and Ryosuke Saga. "GPGPU Implementation of Fuzzy Rule-Based Classifiers." In Intelligent Decision Technologies. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29920-9_33.

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Conference papers on the topic "Decision implementation"

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Roubtsov, Serguei, and Ella Roubtsova. "Implementation of Decision Modules." In the 2014 Workshop. ACM Press, 2014. http://dx.doi.org/10.1145/2630768.2630775.

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Ketler, Karen, John Willems, and Vicki Hampton. "The EDI implementation decision." In the 1997 ACM SIGCPR conference. ACM Press, 1997. http://dx.doi.org/10.1145/268820.268868.

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Lin, Jing-Jia, Chang-Chih Tsai, Ya-Wei Kuo, and H. L. Cheng. "Implementation of Shared Decision Making." In 2019 International Conference on Intelligent Computing and its Emerging Applications (ICEA). IEEE, 2019. http://dx.doi.org/10.1109/icea.2019.8858284.

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Cavarischia, Leonardo, and Leonardo Lanari. "Hierarchical tracking implementation." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434703.

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Malik, Raghav, Vidush Singhal, Benjamin Gottfried, and Milind Kulkarni. "Vectorized secure evaluation of decision forests." In PLDI '21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation. ACM, 2021. http://dx.doi.org/10.1145/3453483.3454094.

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Narayanan, Ramanathan, Daniel Honbo, Gokhan Memik, Alok Choudhary, and Joseph Zambreno. "An FPGA Implementation of Decision Tree Classification." In Design, Automation & Test in Europe Conference. IEEE, 2007. http://dx.doi.org/10.1109/date.2007.364589.

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Basit, Mujeeb A., Vaishnavi Kannan, and Duwayne L. Willett. "Agile Clinical Decision Support Development and Implementation." In BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, 2018. http://dx.doi.org/10.1145/3233547.3233663.

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Shi, Guoyong. "A simple implementation of determinant decision diagram." In 2010 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 2010. http://dx.doi.org/10.1109/iccad.2010.5654333.

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Xinmiao Zhang and K. K. Parhi. "Fast factorization architecture in soft-decision Reed-Solomon decoding." In 2004 IEEE Workshop on Signal Processing Systems Design and Implementation. IEEE, 2004. http://dx.doi.org/10.1109/sips.2004.1363032.

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Drews, Samuel, Aws Albarghouthi, and Loris D'Antoni. "Proving data-poisoning robustness in decision trees." In PLDI '20: 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation. ACM, 2020. http://dx.doi.org/10.1145/3385412.3385975.

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Reports on the topic "Decision implementation"

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Harris, John, Gregory S. Parnell, Brent Hoops, Sean Gardner, and Randy Mounts. BRAC 2005 Implementation Decision Support Tools. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada426284.

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Riedel, Sharon L. User Acceptance and Field Implementation of Decision Support Systems. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada200412.

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Smith, Timothy S., and Garry A. Wolfrum. Making The Decision: Pollution Prevention (P2) Equipment Evaluation, Procurement, Implementation and Monitoring. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada350676.

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Smith, Leon I. Implementation of Management Initiative Decision (Mid) 913 - Background and Impact within DOD. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada424013.

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Aznar, Alexandra, Megan Day, Elizabeth Doris, Shivani Mathur, and Paul Donohoo-Vallett. City-Level Energy Decision Making. Data Use in Energy Planning, Implementation, and Evaluation in U.S. Cities. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1215228.

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Aznar, Alexandra, Megan Day, Elizabeth Doris, Shivani Mathur, and Paul Donohoo-Vallett. City-Level Energy Decision Making: Data Use in Energy Planning, Implementation, and Evaluation in U.S. Cities. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1334392.

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Parman, Kristan. School Reform and Coaching: Identifying Structures for Successful Implementation of a Data Informed Decision-Making Program. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2484.

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Author, Not Given. Implementation plan for the record of decision for disposal of Hanford defense high-level, transuranic, and tank wastes. Office of Scientific and Technical Information (OSTI), 1988. http://dx.doi.org/10.2172/6305673.

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Quinn, N. W. T., R. Ortega, P. Rahilly, and C. B. Johnson. Wetland Flow and Salinity Budgets and Elements of a Decision Support System toward Implementation of Real-Time Seasonal Wetland Salinity Management. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1055704.

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Prindle, N. H., F. T. Mendenhall, K. Trauth, et al. The second iteration of the Systems Prioritization Method: A systems prioritization and decision-aiding tool for the Waste Isolation Pilot Plant: Volume 2, Summary of technical input and model implementation. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/244508.

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