Готові списки джерел за темами / Predicting Innovation

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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Predicting Innovation"

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Schwabsky, Nitza, Ufuk Erdogan, and Megan Tschannen-Moran. "Predicting school innovation." Journal of Educational Administration 58, no. 2 (December 23, 2019): 246–62. http://dx.doi.org/10.1108/jea-02-2019-0029.

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Purpose The purpose of this paper is to examine the role of collective teacher efficacy, academic press and faculty trust, all of which are components of academic optimism (AO), in predicting school innovation. In addition, the authors explored the extent to which faculty trust mediates the association between collective teacher efficacy and academic press with school innovation. Design/methodology/approach In all, 1,009 teachers from 79 schools in Northern Israel completed anonymous questionnaires about AO and innovation. Aggregation, descriptive statistics, bivariate correlation analyses and mediation analysis were performed to analyze the data. Findings Results showed that the components of AO, i.e., collective teacher efficacy, academic press and trust, were positively correlated to school innovation, and that trust mediated the relationship between collective teacher efficacy and school innovation. The study results confirmed that AO holds a significant predictive value in school innovation and highlights the importance of trust in supporting innovation. Practical implications As school leaders are challenged to foster innovative new practices in their schools, the findings suggest that they will need to know how to cultivate collective teacher efficacy, academic press and faculty trust. Originality/value This is the first study to examine the role of the components of AO in predicting innovation. By using a robust sample, the authors were able to examine the proposed school-level model with respect to the factors that affect school innovation. Originality also lies in the organizational approach to educational innovation in relation to faculty’s beliefs and behaviors.
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2

Blagojevic, Marija, Zivadin Micic, and Momcilo Vujicic. "Cluster analysis of knowledge sources in standardized electrical engineering subfields." Serbian Journal of Electrical Engineering 13, no. 3 (2016): 405–22. http://dx.doi.org/10.2298/sjee1603405b.

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The paper presents a cluster analysis of innovation of knowledge sources based on the standards in the field of Electrical Engineering. Both local (SRPS) and global (ISO) knowledge sources have been analysed with the aim of innovating a Knowledge Base (KB). The results presented indicate a means/possibility of grouping the subfields within a cluster. They also point to a trend or intensity of knowledge source innovation for the purpose of innovating the KB that accompanies innovations. The study provides the possibility of predicting necessary financial resources in the forthcoming period by means of original mathematical relations. Furthermore, the cluster analysis facilitates the comparison of the innovation intensity in this and other (sub)fields. Future work relates to the monitoring of the knowledge source innovation by means of KB engineering and improvement of the methodology of prediction using neural networks.
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3

Hang Do, Thuy, Tim Mazzarol, Thierry Volery, and Sophie Reboud. "Predicting anticipated rent from innovation commercialisation in SMEs." European Journal of Innovation Management 17, no. 2 (May 6, 2014): 183–208. http://dx.doi.org/10.1108/ejim-12-2012-0113.

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Purpose – The purpose of this paper is to examine the relationship between the expectations that small business entrepreneurs hold in relation to the future returns from the commercialisation of innovations, and key organisational elements including inputs, knowledge, culture, strategy, portfolio, project management and commercialisation. More specifically, this research aims to deepen the knowledge of how small- and medium-sized enterprises (SMEs) manage their innovation and identify critical factors determining the potential innovation outcomes. Design/methodology/approach – This study draws on a large sample of innovative SMEs from multiple Organisation for Economic Co-Operation and Development countries. Data were collected using a questionnaire administered face-to-face with owners-managers or executives of SMEs who made critical decisions for the innovation management of the firm. First, a factor analysis is conducted to identify the most appropriate measures for each variable. Second, the authors test for multicollinearity among independent variables. The final step integrates results from the general linear model analysis that measures the relationship between organisational factors and the anticipated returns. Findings – Findings suggest that positive expectations over future investment in innovation – as measured by the anticipated rent – are influenced by organisational factors, including innovation strategy, portfolio management, project management, and organisational culture and commercialisation process. Conversely, the resource endowment is not perceived as a barrier to innovation and to the development of a competitive advantage. In addition, industrial knowledge management has an indirect effect on the anticipated returns. Originality/value – Despite extensive research in innovation management, the role of organisational factors on anticipated returns in SMEs has not been investigated to date. The study provides researchers with new insights into the resource-based view and the theory of entrepreneurial rent from the perspective of innovation management. The findings offer guidance to managers as to potential success factors in enhancing the rent, but also reflect entrepreneurial optimism in the management of innovation.
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Rojas-Córdova, Carolina, Boris Heredia-Rojas, and Patricio Ramírez-Correa. "Predicting Business Innovation Intention Based on Perceived Barriers: A Machine Learning Approach." Symmetry 12, no. 9 (August 19, 2020): 1381. http://dx.doi.org/10.3390/sym12091381.

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In the Industry 4.0 scenario, innovation emerges as a clear driver for the economic development of societies. This effect is particularly true for the least developed countries. Nevertheless, there is a lack of studies that analyze this phenomenon in these nations. In this context, this study aims to examine the impact of perceived barriers to innovation to predict companies′ innovative intentions in an emerging economy. This study is a preliminary effort to use data mining and symmetry-based learning concepts, especially classification, to assist the identification of strategies to incentivize intention to innovate in companies. Using the decision tree classification technique, we analyzed a sample of Chilean companies (N = 5876). The sample was divided into large enterprises (LEs) and small and medium enterprises (SMEs). In the group of large companies, the barriers that most impact the intention to innovate are innovation cost, lack of demand innovations, and lack of qualified personnel. Alternatively, in the group of small-medium companies, the barriers that most impact the intention to innovate are lack of own funds, lack of demand innovations, and lack of information about technology. These results show how the perceptions of barriers are significant to predict the intentions of innovation in Chilean companies. Furthermore, the perceptions of these barriers are contingent on the organizational sizes. These findings contribute to understanding the effect of contingencies on innovative intention in an emerging economy.
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Park, Kyungbo, Jeonghwa Cha, and Jongyi Hong. "Developing a Framework for Evaluating and Predicting Management Innovation in Public Research Institutions." Sustainability 15, no. 9 (April 27, 2023): 7261. http://dx.doi.org/10.3390/su15097261.

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As the external environment changes rapidly, organizations need management innovation to adapt to and exploit change as an opportunity. To innovate, it is necessary to evaluate management innovation, because if an organization can measure the degree of management innovation, it can also achieve it. Moreover, if management innovation is predictable, profits can be maximized, and costs can be minimized by allocating efficient resources and establishing appropriate strategies. Therefore, this study attempts to predict the management innovation in public research institutions. Basic data mining and ensemble data mining techniques were used for the prediction. This analysis targeted public research institutes in South Korea. The results showed that the predictive power of public research institutions with high innovation was high. This study suggests that management innovation can be predicted in highly innovative public research institutions. Furthermore, this study’s framework can be applied to other industries.
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6

Ramiz Abdinov, Vidadi Akhundov, Ramiz Abdinov, Vidadi Akhundov. "METHODOLOGY FOR ASSESSING THE IMPACT OF INNOVATIONS ON THE PRODUCTION OF THE REGION'S FINAL PRODUCTS." PIRETC-Proceeding of The International Research Education & Training Centre 21, no. 04 (November 9, 2022): 33–38. http://dx.doi.org/10.36962/piretc21042022-33.

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The article developed an algorithm for calculating the coefficient of the impact of innovations on the growth rate of the final product. This mathematical apparatus is a tool for adequate adjustment of the economic system, taking into account the potential of its innovative development. Next, the problem of predicting the regional final product is solved using the Cobb-Douglas(innovation) model. In the calculations, the values of the parameters are determined by the Gradient method. The result obtained indicates the adequacy of the approach used. In addition, the assumptions and hypotheses put forward in the study create prerequisites for further expanding the amount of knowledge in the field of studying the innovative impact on the economy. The practical significance of the proposed models of innovative development of economic systems lies in the emergence of new opportunities for building the innovation policy of the state. Keywords: innovation activity, fuzzy model, Gradient method, innovation index
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Swart, Rachelle R., Maria JG Jacobs, Cheryl Roumen, Ruud MA Houben, Folkert Koetsveld, and Liesbeth J. Boersma. "Factors predicting timely implementation of radiotherapy innovations: the first model." British Journal of Radiology 94, no. 1117 (January 1, 2021): 20200613. http://dx.doi.org/10.1259/bjr.20200613.

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Objective: The improvement of radiotherapy depends largely on the implementation of innovations, of which effectivity varies widely. The aim of this study is to develop a prediction model for successful innovation implementation in radiotherapy to improve effective management of innovation projects. Methods: A literature review was performed to identify success factors for innovation implementation. Subsequently, in two large academic radiotherapy centres in the Netherlands, an inventory was made of all innovation projects executed between 2011 and 2017. Semi-structured interviews were performed to record the presence/absence of the success factors found in the review for each project. Successful implementation was defined as timely implementation, yes/no. Cross-tables, Χ2 tests, t-tests and Benjamin-Hochberg correction were used for analysing the data. A multivariate logistic regression technique was used to build a prediction model. Results: From the 163 identified innovation projects, only 54% were successfully implemented. We found 31 success factors in literature of which 14 were significantly related to successful implementation in the innovation projects in our study. The prediction model contained the following determinants: (1) sufficient and competent employees, (2) complexity, (3) understanding/awareness of the project goals and process by employees, (4) feasibility and desirability. The area Under the curve (AUC) of the prediction model was 0.86 (0.8–0.92, 95% CI). Conclusion: A prediction model was developed for successful implementation of innovation in radiotherapy. Advances in knowledge: This prediction model is the first of its kind and, after external validation, could be widely applicable to predict the timely implementation of radiotherapy innovations.
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Gashema, Bruce. "Predicting innovative work behaviors through transformational leadership." International Journal of Research in Business and Social Science (2147- 4478) 10, no. 1 (February 11, 2021): 69–84. http://dx.doi.org/10.20525/ijrbs.v10i1.999.

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To stay competitive and viable in today's rapidly evolving and highly hostile market climate, companies need to concentrate more on innovation. In this regard, the specific quality of transformational leadership has been strongly linked to organizational innovativeness. However, the purpose of this study is to examine the relationship between transformational leadership and the innovation behavior of employees. The literature review developed in the current study is fundamentally centered on how transformational leaders value innovation by facilitating organizational learning to foster innovative behavior among employees. Corporate social responsibilities as an organizational level factor that can drive employee attitudes were also examined as a moderating effect in this relationship. The rationale of the relationship of the variables is supported by both transformational leadership theory (TFL) and social exchange theory (SET). Data were collected from corporate social responsibility engaged SMEs operating in Rwanda. Using Cranach’s alpha reliability and validity were tested while structural equation modeling (SEM) was used in analyzing data. The findings of this study intend to fill some gaps in the current literature such as the introduction of CSR in the relationship between top managerial level leadership, organizational learning, and innovation behavior of employees, also introducing SET to explain such important relationship
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Rani, Ruchi, Sumit Kumar, Rutuja Rajendra Kadam, and Sanjeev Kumar Pippal. "A machine learning model for predicting innovation effort of firms." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (August 1, 2023): 4633. http://dx.doi.org/10.11591/ijece.v13i4.pp4633-4639.

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<span lang="EN-US">Classification and regression tree (CART) data mining models have been used in several scientific fields for building efficient and accurate predictive models. Some of the application areas are prediction of disease, targeted marketing, and fraud detection. In this paper we use CART which widely used machine learning technique for predicting research and development (R&amp;D) intensity or innovation effort of firms using several relevant variables like technical opportunity, knowledge spillover and absorptive capacity. We found that accuracy of CART models is superior to the often-used linear parametric models. The results of this study are considered necessary for both financial analysts and practitioners. In the case of financial analysts, it establishes the power of data-driven prototypes to understand the innovation thinking of employees, whereas in the case of policymakers or business entrepreneurs, who can take advantage of evidence-based tools in the decision-making process.</span>
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Pan, Han, Wu Xin, and Yuping Li. "A review on the concept of consumer innovativeness." E3S Web of Conferences 251 (2021): 01080. http://dx.doi.org/10.1051/e3sconf/202125101080.

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Beginning in the 1970s, academia began to study consumer innovation and used it as an important indicator for predicting consumers’ new product adoption behavior. This article makes a more comprehensive summary and evaluation of the definition of consumer innovation from three aspects (innate innovativeness; special fields innovativeness; actualized innovativeness), summarizes the relationship between the three innovations, and builds a consumer innovation integration model.
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Дисертації з теми "Predicting Innovation"

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Reagan, James L. "Predicting disruptive innovation| Which factors determine success?" Thesis, Shenandoah University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3680894.

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Disruptive Innovation theory explains how industry entrants can defeat established firms and quickly gain a significant share of their key markets, in spite of the fact that incumbents tend to be significantly more experienced and better resourced. The theory has been criticized for being underspecified: whilst the general mechanics of the phenomenon of disruptive innovation are clear, it has not been established which individual variables are essential to the process and which ones are merely ancillary. As a consequence, to date it has not been possible to build a predictive model on the basis of the theory managers can use to assess the disruptive potential of their own and their competitors' innovation projects. In this research project the predictive power of each of the main variables that are mentioned in the literature has been assessed on the basis of a qualitative analysis of five real world case studies. Only variables for which information can be collected using publicly available data before disruption happens have been retained. By clarifying the detail of disruptive innovation theory, this study has been able to address a key issue in the debate, namely, whether products that are more expensive and more complex than the market standard can ever be classified as 'disruptive innovations' or whether they should always be regarded as 'high-end anomalies'. In this study two distinct disruptive innovation strategies have been identified based on the current phase of the product life cycle, the current focus of mainstream demand and the market segments first targeted when coming to market. The first strategy entails growing an existing market by moving the focus of demand on to a secondary market driver as soon as customers begin to lose their willingness to pay a premium for upgrades in the performance areas they historically used to value. Early on in the product life cycle, disruptors can conquer the mainstream market 'from above' with products that are different and more reliable or more convenient but not simpler or cheaper. The second strategy creates a new separate market by offering a radically new type of additional functionality. Over time the new market replaces the old market. These products are likely to be expensive because of their small production run and difficult to use because they are the first models of their kind. High-end customers constitute a natural foothold market for these products as they are wealthy and highly skilled.

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2

Heesen, Bernd. "Diffusion of innovations : factors predicting the use of e-learning at institutions of higher education in Germany." Berlin dissertation.de, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2833665&prov=M&dokv̲ar=1&doke̲xt=htm.

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Heesen, Bernd. "Diffusion of innovations factors predicting the use of e-learning at institutions of higher education in Germany." Berlin dissertation.de, 2004. http://deposit.d-nb.de/cgi-bin/dokserv?id=2833665&prov=M&dok_var=1&dok_ext=htm.

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Chan, Tan Fung Ivan. "Predicting the Probability for Adopting an Audience Response System in Higher Education." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/1529.

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Instructional technologies can be effective tools to foster student engagement, but university faculty may be reluctant to integrate innovative and evidence-based modern learning technologies into instruction. It is important to identify the factors that influence faculty adoption of instructional technologies in the teaching and learning process. Based on Rogers' diffusion of innovation theory, this quantitative, nonexperimental, one-shot cross-sectional survey determined what attributes of innovation (relative advantage, compatibility, complexity, trialability, and observability) predict the probability of faculty adopting the audience response system (ARS) into instruction. The sample for the study consisted of 201 faculty who have current teaching appointments at a university in the southeastern United States. Binary logistic regression analysis was used to determine the attributes of innovation that predict the probability of faculty adopting the ARS into instruction. The data indicated that the attributes of compatibility and trialability significantly predicted faculty adoption of ARS into instruction. Based on the results of the study, a professional development project that includes 3 full days of training and experiential learning was designed to assist faculty in adopting ARS into instruction. Because the current study only included the faculty at a single local university, future studies are recommended to explore a more holistic view of the problem from different institutions and from other stakeholders who may contribute to the process of instructional technology adoption. The project not only contributes to solving the local problem in ARS adoption, but it is also instrumental in promoting positive social change by fostering evidence-based teaching strategies and innovations that maximize student learning.
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Bohling, Timothy R. "Predicting Purchase Timing, Brand Choice and Purchase Amount of Firm Adoption of Radically Innovative Information Technology: A Business to Business Empirical Analysis." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/bus_admin_diss/3.

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Knowing what to sell, when to sell, and to whom to sell is essential buyer behavior insight to allocate scarce marketing resources efficiently and effectively. Applying the theory of relationship marketing (Morgan and Hunt 1994), this study seeks to investigate the link between commitment and trust and firm adoption of radically innovative information technology (IT). The construct of radical innovation is operationalized through the use of cloud computing. A review of the vast scholarly literature on radical innovation diffusion and adoption, and modeling techniques used to analyze buyer behavior is followed by empirical estimation of each of the radical innovation adoption questions of purchase timing, brand choice, and purchase amount. Then, the inefficiencies in the independent model process are highlighted, suggesting the need for an integrated model. Next, an integrated model is developed to link the purchase timing, brand choice, and purchase amount decisions. The essay concludes with insight for marketing practitioners on the strength of the factors of commitment and trust on adoption of radical innovation, an improved methodology for the business-to-business marketing literature, and potential further research paths.
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6

Ocal, Kubilay. "Predicting Employee Performance In Non-profit Sport Organizations: The Role Of Managerial And Financial Performance And The Mediating Role Of Support For Innovation And Individual Creativity." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613403/index.pdf.

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The purpose of the current study was to examine the level of support for innovation and individual creativity as potential mediators of relationship between managerial task performance, managerial contextual performance, organizational financial performance and employee performance in non-profit sport organizations in Turkey. For the purpose of the study, 721 volunteer managers and employees from 21 Department/School of Physical Education and Sport (D-SPES) and 23 Province Directorates of Youth and Sport (PDYS) were participated in the study. Individual Creativity Scale, Support for Innovation Scale, Managerial Task Performance Scale, Managerial Contextual Performance Scale, Organizational Financial Performance Scale, and Employee Performance Scale were used for data collection. Results of the Structural Equation Modeling (SEM) analysis revealed that the model adequately describes the data for the sample and the fit indices were all within the acceptable thresholds. The model accounted for 68% variance in support for innovation, 0.7% variance in individual creativity and 44% variance in employee performance. These results suggested that support for innovation and individual creativity significantly mediate the effects of managerial task performance, managerial contextual performance and organizational financial performance on employee performance.
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7

Spears-Dean, Dorothy. "Predicting the Diffusion of Next Generation 9-1-1 in the Commonwealth of Virginia: An Application Using the Deployment of Wireless E9-1-1 Technologies." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/183.

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This study examines the deployment of Wireless E9-1-1 Phase One and Wireless E9-1-1 Phase Two as a diffusion of innovation. The research method used in this study is a cross-sectional study employing secondary data in a discriminant function analysis. The study population is Virginia units of local governments (95 counties and 39 cities) that had not deployed Wireless E9-1-1 Phase One or Wireless E9-1-1 Phase Two as of January 1, 2001. The period of time included in this study is from 2001 to 2006. The purpose of the study is to assess the overall accuracy of the three principle theories of policy innovation adoption: diffusion, internal determinants, and unified theory, which are variations of the fundamental diffusion theory, in predicting the deployment of wireless E9-1-1 by Virginia units of local government. This assessment was conducted by identifying Virginia specific variables from models associated with these policy innovation theories to determine the best performing model for the deployment of Wireless E9-1-1 throughout the Commonwealth of Virginia. The Virginia specific variables utilized in this study are: Wealth, Population, Fiscal Health, Dedicated Funding, Financial Dependency, Urbanization, Regionalism, and Proximity to Interstate. Dedicated Funding and Regionalism had the largest absolute size of correlation among the predictor variables for the deployment of Wireless E9-1-1 Phase One and Wireless E9-1-1 Phase Two, thus generating the best performing model. This information will provide the basis from which to develop a statewide comprehensive policy and plan for Next Generation 9-1-1 and will help provide an answer to the question of when and how governments get involved in designing and implementing a 9-1-1 emergency service network.
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McSharry, Patrick E. "Innovations in consistent nonlinear deterministic prediction." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342590.

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9

Eustace, Paul Alan. "Structural mass of innovative concept aircraft." Thesis, Loughborough University, 2001. https://dspace.lboro.ac.uk/2134/7361.

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Le, An Hai. "Innovative neural network approaches for petrophysical parameter prediction." Thesis, Heriot-Watt University, 2004. http://hdl.handle.net/10399/326.

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Книги з теми "Predicting Innovation"

1

Christopher, Layne, Arquilla John, Rand Corporation, and Arroyo Center, eds. Predicting military innovation. Santa Monica, CA: RAND, 1999.

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2

Barbara, Collier, and U.S. Army Research Laboratory, eds. ARL, predictive technology. Adelphi, Md: Army Research Laboratory, 1993.

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3

Kiruluta, Andrew M. Predictive head movement tracking using innovations generated by Kalman filters. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.

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4

Brueck, Terrance M. Forecasting the future: Progress, change, and predictions for the water sector. Denver, Colo: Water Research Foundation, 2012.

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5

Lee, Jinsuk. Lifetime prediction for degradation of solar mirrors using step-stress accelerated testing. Golden, Colo.]: National Renewable Energy Laboratory, 2011.

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6

Littleton, Eliza Beth. Predicting rapid decision-making processes required by the dismounted objective force warrior. Alexandria, VA: United States Army Research Institute for the Behavioral and Social Sciences, 2003.

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7

Yŏng-sŏn, Kwŏn. Homo k'ŏnbŏjŏnsŭ: Che 4-ch'a sanŏp hyŏngmyŏng kwa mirae sahoe = Homo convergence. Kyŏnggi-do P'aju-si: Asia, 2016.

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8

(Firm), Knovel, ed. Case studies in novel food processing technologies: Innovations in processing, packaging and predictive modelling. Oxford: Woodhead Publishing, 2010.

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9

Transizione, dall'era industriale a quella post-industriale: Verso la Terza Guerra Mondiale o alla conquista del futuro? Milano: Nuovi autori, 1986.

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10

Maxence, Layet, Bultez Adams Philippe, and Kaplan Frédéric, eds. Futur 2.0: Comprendre les 20 prochaines années. Limoges: Fyp éditions, 2007.

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Частини книг з теми "Predicting Innovation"

1

Gatignon, Hubert, David Gotteland, and Christophe Haon. "Predicting New Product Acceptance." In Making Innovation Last: Volume 2, 211–71. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1007/978-1-137-57264-6_5.

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Ng, Shien Wee, Hoa Khanh Dam, Morakot Choetkiertikul, and Aditya Ghose. "Predicting Issues for Resolving in the Next Release." In Service Research and Innovation, 164–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76587-7_11.

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Hiltunen, Elina. "Some Thoughts about Predicting the Future, Its Ease and Difficulty." In Foresight and Innovation, 17–25. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9781137337702_2.

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Chadha, Akalbir Singh, Yashowardhan Shinde, Neha Sharma, and Prithwis Kumar De. "Predicting CO2 Emissions by Vehicles Using Machine Learning." In Data Management, Analytics and Innovation, 197–207. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2600-6_14.

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Bhosale, Hrushikesh, Aamod Sane, Vigneshwar Ramakrishnan, and Valadi K. Jayaraman. "Distributed Reduced Alphabet Representation for Predicting Proinflammatory Peptides." In Data Management, Analytics and Innovation, 161–73. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1414-2_14.

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Maiti, Reetun, and Balagopal G. Menon. "Predicting Injury Severity in Construction Using Logistic Regression." In Data Management, Analytics and Innovation, 175–85. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1414-2_15.

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Mohanty, Samuka, and Rajashree Dash. "Predicting the Price of Gold: A CSPNN-DE Model." In Smart Innovation, Systems and Technologies, 289–97. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6202-0_29.

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Williams, Louis, Salman Waqar, Tom Sherman, and Giovanni Masala. "Comparative Study of Pattern Recognition Methods for Predicting Glaucoma Diagnosis." In Innovation in Medicine and Healthcare, 93–103. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5852-8_9.

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Poovammal, E., Mayank Kumar Nagda, and K. Annapoorani. "Predicting Property Prices: A Universal Model." In EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, 259–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19562-5_26.

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Radhakrishnan, Hari Kumar, C. P. Ramanarayanan, and R. Bharath. "Machine Learning Based Automated Process for Predicting the Anomaly in AIS Data." In Data Management, Analytics and Innovation, 303–14. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2600-6_21.

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Тези доповідей конференцій з теми "Predicting Innovation"

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Ahmad Rafi, Mohamed Eshaq, and Scott Chase. "Transforming Grammars for Goal Driven Style Innovation." In eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.879.

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Thomson, A., M. Haggith, and Ravi Prabhu. "Innovation diffusion: predicting success of system development." In Proceedings. 15th International Workshop on Database and Expert Systems Applications, 2004. IEEE, 2004. http://dx.doi.org/10.1109/dexa.2004.1333545.

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Yu, Chen-Hsiang, Jungpin Wu, and Aa-Chi Liu. "PREDICTING LEARNING OUTCOMES WITH MOOCS CLICKSTREAMS." In 2nd Eurasian Conference on Educational Innovation 2019. International Institute of Knowledge Innovation and Invention Private Limited, 2019. http://dx.doi.org/10.35745/ecei2019v2.079.

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Korableva, Olga, Viktoriya Mityakova, and Olga Kalimullina. "Designing a Decision Support System for Predicting Innovation Activity." In 22nd International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009565706190625.

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Srivatsa, Hosur Srinivasan, Arun R, Sandeep N, and Vijaya Kumar S. "Predicting Green Innovation Levels Among Automobile and Earthmoving Sectors." In 2nd Indian International Conference on Industrial Engineering and Operations Management. Michigan, USA: IEOM Society International, 2022. http://dx.doi.org/10.46254/in02.20220283.

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Vesic, Ana, Vuk Ignjatovic, Sava Lakicevic, Luka Lakicevic, Bojan Gutic, Hristo Skacev, Dusan Dotlic, Andrej Micovic, Marina Marjanovic Jakovljevic, and Miodrag Zivkovic. "Predicting Plant Water and Soil Nutrient Requirements." In 2020 Zooming Innovation in Consumer Technologies Conference (ZINC). IEEE, 2020. http://dx.doi.org/10.1109/zinc50678.2020.9161433.

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Hajek, Petr, and Jan Stejskal. "Predicting the innovation activity of chemical firms using an ensemble of decision trees." In 2015 11th International Conference on Innovations in Information Technology (IIT). IEEE, 2015. http://dx.doi.org/10.1109/innovations.2015.7381511.

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Kekulanadara, K. M. O. V. K., B. T. G. S. Kumara, and Banujan Kuhaneswaran. "Comparative Analysis of Machine Learning Algorithms for Predicting Air Quality Index." In 2021 From Innovation To Impact (FITI). IEEE, 2021. http://dx.doi.org/10.1109/fiti54902.2021.9833033.

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Klavans, Richard, Kevin Boyack, and Caleb Smith. "Field Effects in Predicting Exceptional Growth in Research Communities." In 27th International Conference on Science, Technology and Innovation Indicators (STI 2023). International Conference on Science, Technology and Innovation Indicators, 2023. http://dx.doi.org/10.55835/643f1aa90f649f6042841876.

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Using a model of the literature indexed in Scopus, we have increased the accuracy of our ability to predict which of 20,747 research communities would achieve exceptional growth from 32.2 to 39.6 using double exponential smoothing of inertial indicators and by doing predictions in each of 26 fields rather than across the entire model. Each field nominated two (out of a possible 123) indicators as ‘best predictors’ following the procedure described in our previous studies. Significant diversity was found in which indicators performed best in each field, suggesting that field effects should be accounted for in predictive analytics. Nevertheless, there were groupings of contiguous fields with a surprising level of homogeneity in predictive indicators. Possible reasons for the similarities and differences are discussed.
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Khalaf, Fatema, and Subhashini S. Baskaran. "Predicting Acute Respiratory Failure Using Fuzzy Classifier." In 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD). IEEE, 2023. http://dx.doi.org/10.1109/itikd56332.2023.10099746.

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Звіти організацій з теми "Predicting Innovation"

1

Coughlan, Peter, William Gates, and Jeremy Arkes. Innovations in Defense Acquisition: Asymmetric Information, Mechanism Design and Prediction Markets. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada563409.

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Joseph, Earl C., and Steve Conway. Create full-scale predictive economic models on ROI and innovation with performance computing. Office of Scientific and Technical Information (OSTI), October 2017. http://dx.doi.org/10.2172/1405141.

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Bitz, Cecilia M. An Innovative Network to Improve Sea Ice Prediction in a Changing Arctic. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada617899.

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Goldsmith, Stephen, Susan Crawford, and Benjamin Weinryb Grohsgal. Innovations in Public Service Delivery: Issue No. 4: Predictive Analytics: Driving Improvements Using Data. Inter-American Development Bank, July 2016. http://dx.doi.org/10.18235/0000421.

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Sánchez- Sesma, Francisco José, Hiroshi Kawase, and Joseline Mena Negrete. Working Paper PUEAA No. 5. The collaboration between Mexico and Japan in earthquake engineering and seismology. Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.003r.2022.

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Despite their remoteness from each other, Japan and Mexico share a critical characteristic: the seismic hazard. In the past, both nations have been hit by great earthquakes that have caused serious human and material losses. Although the prediction of earthquakes is not yet possible, the development of early warning systems and their constant innovation is a priority, especially the studies of the horizontal-to-vertical spectral relationship of microseisms, which can help the study and understanding of earthquakes’ nature, as well as their impact on infrastructure. It is for mutual benefit to Japan and Mexico that cooperation between university institutions specialized in seismological studies increases to jointly create study and innovation mechanisms.
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Mattsson, Ann Elisabet, Scott A. Mitchell, and Stephen W. Thomas. LDRD 102610 final report new processes for innovative microsystems engineering with predictive simulation. Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/913217.

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Холошин, Ігор Віталійович, Наталя Борисівна Пантелєєва, Олександр Миколайович Трунін, Людмила Володимирівна Бурман, and Ольга Олександрівна Калініченко. Infrared Spectroscopy as the Method for Evaluating Technological Properties of Minerals and Their Behavior in Technological Processes. E3S Web of Conferences, 2020. http://dx.doi.org/10.31812/123456789/3929.

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Infrared spectroscopy (IR) is a highly effective method for the analysis of minerals, rocks and ores, capable of solving a whole range of problems when choosing innovative solutions for the technological processing of various types of mineral raw materials. The article considers the main directions of using the infrared spectroscopy method in assessing the technological properties of minerals and their behavior in technological processes: evaluation of the grade (quality) of mineral raw materials; analysis of the behavior of minerals in the technological process with prediction of their technological properties; analysis of changes in the structure and properties of minerals in technological processes; operational analysis of mineral substances at various stages of technological processing. The article illustrates all aspects of the use of infrared spectroscopy at various stages of studying the material composition of mineral raw materials in its enrichment assessment by specific examples of solving problems arising from the technological redistribution of various types of ore and non-metallic minerals.
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Sandford, Robert, Vladimir Smakhtin, Colin Mayfield, Hamid Mehmood, John Pomeroy, Chris Debeer, Phani Adapa, et al. Canada in the Global Water World: Analysis of Capabilities. United Nations University Institute for Water, Environment and Health, November 2018. http://dx.doi.org/10.53328/vsgg2030.

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This report critically examines, for the first time, the capacity of Canada’s water sector with respect to meeting and helping other countries meet the water-related targets of the UN’s global sustainable development agenda. Several components of this capacity are examined, including water education and research, investment in water projects that Canada makes internally and externally, and experiences in water technology and governance. Analysis of the water education system suggests that there is a broad capability in institutions of higher learning in Canada to offer training in the diverse subject areas important in water. In most cases, however, this has not led to the establishment of specific water study programmes. Only a few universities provide integrated water education. There is a need for a comprehensive listing of water-related educational activities in universities and colleges — a useful resource for potential students and employers. A review of recent Canadian water research directions and highlights reveals strong and diverse water research capacity and placed the country among global leaders in this field. Canada appears to be within the top 10 countries in terms of water research productivity (publications) and research impact (citations). Research capacity has been traditionally strong in the restoration and protection of the lakes, prediction of changes in climate, water and cryosphere (areas where water is in solid forms such as ice and snow), prediction and management of floods and droughts. There is also a range of other strong water research directions. Canada is not among the top 10 global water aid donors in absolute dollar numbers; the forerunners are, as a rule, the countries with higher GDP per capita. Canadian investments in Africa water development were consistently higher over the years than investments in other regions of the global South. The contributions dropped significantly in recent years overall, also with a decline in aid flow to Africa. Given government support for the right business model and access to resources, there is significant capacity within the Canadian water sector to deliver water technology projects with effective sustainable outcomes for the developing world. The report recommends several potential avenues to elevate Canada’s role on the global water stage, i.e. innovative, diverse and specific approaches such as developing a national inventory of available water professional capacity, and ranking Universities on the strength of their water programmes coordinating national contributions to global sustainability processes around the largest ever university-led water research programme in the world – the 7-year Global Water Futures program targeting specific developmental or regional challenges through overseas development aid to achieve quick wins that may require only modest investments resolving such chronic internal water challenges as water supply and sanitation of First Nations, and illustrating how this can be achieved within a limited period with good will strengthening and expanding links with UN-Water and other UN organisations involved in global water policy work To improve water management at home, and to promote water Canadian competence abroad, the diverse efforts of the country’s water sector need better coordination. There is a significant role for government at all levels, but especially federally, in this process.
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Saville, Alan, and Caroline Wickham-Jones, eds. Palaeolithic and Mesolithic Scotland : Scottish Archaeological Research Framework Panel Report. Society for Antiquaries of Scotland, June 2012. http://dx.doi.org/10.9750/scarf.06.2012.163.

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Why research Palaeolithic and Mesolithic Scotland? Palaeolithic and Mesolithic archaeology sheds light on the first colonisation and subsequent early inhabitation of Scotland. It is a growing and exciting field where increasing Scottish evidence has been given wider significance in the context of European prehistory. It extends over a long period, which saw great changes, including substantial environmental transformations, and the impact of, and societal response to, climate change. The period as a whole provides the foundation for the human occupation of Scotland and is crucial for understanding prehistoric society, both for Scotland and across North-West Europe. Within the Palaeolithic and Mesolithic periods there are considerable opportunities for pioneering research. Individual projects can still have a substantial impact and there remain opportunities for pioneering discoveries including cemeteries, domestic and other structures, stratified sites, and for exploring the huge evidential potential of water-logged and underwater sites. Palaeolithic and Mesolithic archaeology also stimulates and draws upon exciting multi-disciplinary collaborations. Panel Task and Remit The panel remit was to review critically the current state of knowledge and consider promising areas of future research into the earliest prehistory of Scotland. This was undertaken with a view to improved understanding of all aspects of the colonization and inhabitation of the country by peoples practising a wholly hunter-fisher-gatherer way of life prior to the advent of farming. In so doing, it was recognised as particularly important that both environmental data (including vegetation, fauna, sea level, and landscape work) and cultural change during this period be evaluated. The resultant report, outlines the different areas of research in which archaeologists interested in early prehistory work, and highlights the research topics to which they aspire. The report is structured by theme: history of investigation; reconstruction of the environment; the nature of the archaeological record; methodologies for recreating the past; and finally, the lifestyles of past people – the latter representing both a statement of current knowledge and the ultimate aim for archaeologists; the goal of all the former sections. The document is reinforced by material on-line which provides further detail and resources. The Palaeolithic and Mesolithic panel report of ScARF is intended as a resource to be utilised, built upon, and kept updated, hopefully by those it has helped inspire and inform as well as those who follow in their footsteps. Future Research The main recommendations of the panel report can be summarized under four key headings:  Visibility: Due to the considerable length of time over which sites were formed, and the predominant mobility of the population, early prehistoric remains are to be found right across the landscape, although they often survive as ephemeral traces and in low densities. Therefore, all archaeological work should take into account the expectation of Palaeolithic and Mesolithic ScARF Panel Report iv encountering early prehistoric remains. This applies equally to both commercial and research archaeology, and to amateur activity which often makes the initial discovery. This should not be seen as an obstacle, but as a benefit, and not finding such remains should be cause for question. There is no doubt that important evidence of these periods remains unrecognised in private, public, and commercial collections and there is a strong need for backlog evaluation, proper curation and analysis. The inadequate representation of Palaeolithic and Mesolithic information in existing national and local databases must be addressed.  Collaboration: Multi-disciplinary, collaborative, and cross- sector approaches must be encouraged – site prospection, prediction, recognition, and contextualisation are key areas to this end. Reconstructing past environments and their chronological frameworks, and exploring submerged and buried landscapes offer existing examples of fruitful, cross-disciplinary work. Palaeolithic and Mesolithic archaeology has an important place within Quaternary science and the potential for deeply buried remains means that geoarchaeology should have a prominent role.  Innovation: Research-led projects are currently making a substantial impact across all aspects of Palaeolithic and Mesolithic archaeology; a funding policy that acknowledges risk and promotes the innovation that these periods demand should be encouraged. The exploration of lesser known areas, work on different types of site, new approaches to artefacts, and the application of novel methodologies should all be promoted when engaging with the challenges of early prehistory.  Tackling the ‘big questions’: Archaeologists should engage with the big questions of earliest prehistory in Scotland, including the colonisation of new land, how lifestyles in past societies were organized, the effects of and the responses to environmental change, and the transitions to new modes of life. This should be done through a holistic view of the available data, encompassing all the complexities of interpretation and developing competing and testable models. Scottish data can be used to address many of the currently topical research topics in archaeology, and will provide a springboard to a better understanding of early prehistoric life in Scotland and beyond.
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Landau, Sergei Yan, John W. Walker, Avi Perevolotsky, Eugene D. Ungar, Butch Taylor, and Daniel Waldron. Goats for maximal efficacy of brush control. United States Department of Agriculture, March 2008. http://dx.doi.org/10.32747/2008.7587731.bard.

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Background. Brush encroachment constitutes a serious problem in both Texas and Israel. We addressed the issue of efficacy of livestock herbivory - in the form of goat browsing - to change the ecological balance to the detriment of the shrub vegetation. Shrub consumption by goats is kept low by plant chemical defenses such as tannins and terpenes. Scientists at TAES and ARO have developed an innovative, cost-effective methodology using fecal Near Infrared Spectrometry to elucidate the dietary percentage of targeted, browse species (terpene-richredberry and blueberry juniper in the US, and tannin-rich Pistacialentiscus in Israel) for a large number of animals. The original research objectives of this project were: 1. to clarify the relative preference of goat breeds and the individual variation of goats within breeds, when consuming targeted brush species; 2. to assess the heritability of browse intake and validate the concept of breeding goat lines that exhibit high preference for chemically defended brush, using juniper as a model; 3. to clarify the relative contributions of genetics and learning on the preference for target species; 4. to identify mechanisms that are associated with greater intake of brush from the two target species; 5. to establish when the target species are the most vulnerable to grazing. (Issue no.5 was addressed only partly.) Major conclusions, solutions, achievements: Both the Israel and US scientists put significant efforts into improving and validating the technique of Fecal NIRS for predicting the botanical composition of goat diets. Israeli scientists validated the use of observational data for calibrating fecal NIRS, while US scientists established that calibrations could be used across animals differing in breed and age but that caution should be used in making comparisons between different sexes. These findings are important because the ability to select goat breeds or individuals within a breed for maximal efficiency of brush control is dependent upon accurate measurement of the botanical composition of the diet. In Israel it was found that Damascus goats consume diets more than twice richer in P. lentiscus than Mamber or Boer goats. In the US no differences were found between Angora and Boer cross goats but significant differences were found between individuals within breeds in juniper dietary percentage. In both countries, intervention strategies were found that further increased the consumption of the chemically defended plant. In Israel feeding polyethylene glycol (PEG, MW 4,000) that forms high-affinity complexes with tannins increased P. lentiscus dietary percentage an average of 7 percentage units. In the US feeding a protein supplement, which enhances rates of P450-catalyzed oxidations and therefore the rate of oxidation of monoterpenes, increased juniper consumption 5 percentage units. However, the effects of these interventions were not as large as breed or individual animal effects. Also, in a wide array of competitive tannin-binding assays in Israel with trypsin, salivary proteins did not bind more tannic acid or quebracho tannin than non-specific bovine serum albumin, parotid saliva did not bind more tannins than mixed saliva, no response of tannin-binding was found to levels of dietary tannins, and the breed effect was of minor importance, if any. These fundings strongly suggest that salivary proteins are not the first line of defense from tannin astringency in goats. In the US relatively low values for heritability and repeatability for juniper consumption were found (13% and 30%, respectively), possibly resulting from sampling error or non-genetic transfer of foraging behavior, i.e., social learning. Both alternatives seem to be true as significant variation between sequential observations were noted on the same animal and cross fostering studies conducted in Israel demonstrated that kids raised by Mamber goats showed lower propensity to consume P. lentiscus than counterparts raised by Damascus goats.
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