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Relevant bibliographies by topics / Network of expert systems

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Journal articles
Dissertations / Theses
Books
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Academic literature on the topic 'Network of expert systems'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Network of expert systems"

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Gupta, Ashok, and Uma Gupta. "Expert systems in network configuration." Computers & Industrial Engineering 15, no. 1-4 (1988): 236–39. http://dx.doi.org/10.1016/0360-8352(88)90092-7.

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KONOVALOV, S. "FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS." Digital Technologies 26 (2019): 36–46. http://dx.doi.org/10.33243/2313-7010-26-36-46.

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In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid e

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Ershov, A. V., F. P. Kapsargin, A. G. Berezhnoy, and M. P. Miltigashev. "Expert systems in uroflowmetry data evaluation." Herald Urology 6, no. 3 (2018): 12–16. http://dx.doi.org/10.21886/2308-6424-2018-6-3-12-16.

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Introduction. In the practice of an urologist, it is customary to assess the type of urination by two parameters: most often it is the effective volume of the bladder (V) and the maximum volume rate of urination (Qmax). Since the expert assessment of the digital characteristics of urine flow is often ambiguous, they are not taken into consideration by some doctors and often remain without due attention. Today there is a tendency in medicine to objectify by quantification of clinical parameters. The main technology used to solve the tasks of data processing and analysis, as well as their classi

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Mardin, A. "Neural Network Learning and Expert Systems." AI Communications 7, no. 3-4 (1994): 238–40. http://dx.doi.org/10.3233/aic-1994-73-412.

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Castillo, E., A. Cobo, J. M. Gutiérrez, A. Iglesias, and H. Sagástegui. "Causal Network Models in Expert Systems." Computer-Aided Civil and Infrastructure Engineering 9, no. 5 (1994): 315–28. http://dx.doi.org/10.1111/j.1467-8667.1994.tb00339.x.

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Kerr, Sandra M. "Neural network learning and expert systems." Artificial Intelligence in Engineering 8, no. 4 (1993): 315–16. http://dx.doi.org/10.1016/0954-1810(93)90016-9.

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Hunt, John. "Neural network learning and expert systems." Engineering Applications of Artificial Intelligence 9, no. 4 (1996): 465. http://dx.doi.org/10.1016/0952-1976(96)81679-4.

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Luna, Francesco. "Neural network learning and expert systems." Journal of Economic Dynamics and Control 19, no. 4 (1995): 741–45. http://dx.doi.org/10.1016/0165-1889(95)90003-9.

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Gao, Shengxiang, Zhengtao Yu, Linbin Shi, Xin Yan, and Haixia Song. "A Method to Review Expert Recommendation Using Topic Relevance and Expert Relationship." International Journal of Cooperative Information Systems 27, no. 01 (2018): 1741004. http://dx.doi.org/10.1142/s0218843017410040.

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In the process of recommending review experts to projects, in order to effectively make use of the relevance among topics and the relationship among experts, a new method is proposed for review expert recommendation using topic relevance and expert relationship. In this method, firstly, the relevance among topics and the relationships among experts are used to respectively construct the Markov network of topics and the Markov network of experts. Next, the maximum topic clique is extracted from the topic Markov network and the maximum expert clique is extracted from the expert Markov network; t

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Lv, Tai Fu. "Research on High-Density Network Intrusion Features Detection Algorithm." Applied Mechanics and Materials 644-650 (September 2014): 1054–57. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1054.

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Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network

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Dissertations / Theses on the topic "Network of expert systems"

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Vefghi, Lotfollah. "Expert neural network systems in clinical anaesthesia." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267120.

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Yang, Shao-Shi 1958. "Expert system for broadband network cable plant physical layout." Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276832.

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Broadband local area network cable plant design is a time consuming work, it takes lots of computation and it depends a lot on designer's experience. In this thesis the author presents an approach to cable plant design automation, the expert system was implemented in language SCHEME, symbol manipulation was used to generate the data structure (tree) which represent the design results. Signal level simulation can be performed based on the data structure.

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Chen, Lyu-Shi 1958. "EXPERT SYSTEM FOR BROADBAND NETWORK CABLE PLANT DESIGN." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276490.

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This thesis implements the expert system technology in broadband network cable plant design to provide an automated design tool for the design engineer. Under this scheme, the knowledge of the cable plant design engineer can be captured and adapted into a manageable form. The various processes of this system include design rule checking, automatically blueprint layout, signal quality analysis and report generator. As we know, the broadband cable plant design shares 50% of the installation budget at the same time, it is a critical issue in the reliability, the extendability, and the manageabili

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Fan, Yu. "Continuous time Bayesian Network approximate inference and social network applications." Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://proquest.umi.com/pqdweb?index=0&did=1957308751&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268330625&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2009.<br>Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 130-133). Also issued in print.

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Helmy, Dia E. (Dia Edin) Carleton University Dissertation Engineering Electrical. "A diagnostic expert system for dataroute network." Ottawa, 1987.

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Tomsovic, Kevin L. "Development of expert systems as on-line operational aids /." Thesis, Connect to this title online; UW restricted, 1987. http://hdl.handle.net/1773/5995.

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Gibson, Paul Martyn. "The application of hybrid neural network models to archaeofaunal ageing and interpretation." Thesis, University of York, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296383.

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Gilson, Robert. "Minimizing input acquisition costs in a Bayesian belief network-based expert system /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8763.

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Greer, Kieran R. C. "A neural network based search heuristic and its application to computer chess." Thesis, University of Ulster, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243736.

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Islam, Raihan Ul. "Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System." Licentiate thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-66415.

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Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people. This licentiate thesis fo

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Books on the topic "Network of expert systems"

1

Kornfeld, Ari. Belief-network expert systems. SRI International, 1990.

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Castillo, Enrique, José Manuel Gutiérrez, and Ali S. Hadi. Expert Systems and Probabilistic Network Models. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2270-5.

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Medsker, Larry R. Hybrid Neural Network and Expert Systems. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2726-8.

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Gallant, Stephen I. Neural network learning and expert systems. MIT Press, 1993.

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Medsker, Larry R. Hybrid Neural Network and Expert Systems. Springer US, 1994.

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Medsker, L. R. Hybrid neural network and expert systems. Kluwer Academic, 1993.

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Medsker, Larry. Hybrid neural network and expert systems. Kluwer Academic, 1994.

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1969-, Gutiérrez José Manuel, and Hadi Ali S, eds. Expert systems and probabilistic network models. Springer, 1997.

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1951-, Ericson Eric C., Ericson Lisa Traeger 1954-, and Minoli Daniel 1952-, eds. Expert systems applications in integrated network management. Artech House, 1989.

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Badiru, Adedeji Bodunde. Fuzzy Engineering Expert Systems with Neural Network Applications. John Wiley & Sons, Ltd., 2002.

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Book chapters on the topic "Network of expert systems"

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Castillo, Enrique, José Manuel Gutiérrez, and Ali S. Hadi. "Probabilistic Expert Systems." In Expert Systems and Probabilistic Network Models. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2270-5_3.

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Castillo, Enrique, José Manuel Gutiérrez, and Ali S. Hadi. "Rule-Based Expert Systems." In Expert Systems and Probabilistic Network Models. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2270-5_2.

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van Oorschot, Jan, Wil Korremans, and Kees Vos. "Intelligent Network Management." In Database and Expert Systems Applications. Springer Vienna, 1991. http://dx.doi.org/10.1007/978-3-7091-7555-2_16.

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Chorafas, Dimitris N., and Heinrich Steinmann. "Artificial Intelligence and Expert Network Systems." In Implementing Networks in Banking and Financial Services. Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-09479-0_10.

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Guardalben, Giovanni V., and Hi T. Srl. "Hypertext Browsing and Probabilistic Network Searching." In Database and Expert Systems Applications. Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-7557-6_91.

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Medsker, Larry R. "Tools and Development Systems." In Hybrid Neural Network and Expert Systems. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2726-8_10.

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Medsker, Larry R. "Models for Integrating Systems." In Hybrid Neural Network and Expert Systems. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2726-8_3.

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Rabie, Sameh. "Applications of Expert Systems to Network Surveillance." In Network Management and Control. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-1471-4_20.

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Georgopoulos, V. C., and D. Preis. "Signal Representation Issues in Neural-Network Modeling of Perceptual Properties." In Expert Systems and Robotics. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76465-3_11.

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McMichael, Alan, Jennifer Thien, and Jason Tsay. "“TERESA: An Integrated System for Network Diagnoses”." In Database and Expert Systems Applications. Springer Vienna, 1990. http://dx.doi.org/10.1007/978-3-7091-7553-8_21.

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Conference papers on the topic "Network of expert systems"

1

Sowmya, R., Ruchira Rawat, Dinesh Mishra, Mamtaben Kamleshbhai Megha, Monikaben Maheshbhai Patel, and Sathish Kannan. "Quantized Wavelet Dilated Convolutional Neural Network Based Intelligent Surveillance System for Public Safety in IoT Security Networks." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763283.

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P, Yashini, Karthika G, Sunitha T, R. Renugadevi, and Berlin Magthalin R. "Machine Learning-Based Textile Fabric Defect Detection Network." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763088.

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Kumar, Atul, and Kalpna Guleria. "Network Security Attack Detection Using Machine Learning Algorithms." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763075.

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U, Kumaran, B. Umah, Neeharika Ayalasomayujala, Akshith Kosanam, and G. Vivek. "MAC Address Spoofing and its Impact on Network Security." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763130.

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Shourie, Poonam, Vatsala Anand, and Sheifali Gupta. "FootwearFusion: Convolutional Neural Network for Shoe, Sandal, and Boot Classification." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763197.

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Thapa, Sneha, Anjan Bandyopadhyay, Mahendra Kumar Gourisaria, Satyendr Singh, and Sudhansu Shekhar Patra. "Securing Mobile Adhoc Network with Quantum Key Distribution: An Experimental Analysis." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763155.

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Agarwal, Saurav Kumar, and Manitha P. V. "Comparative Analysis of Feedforward Neural Network and LSTM Model for Load Forecasting." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763070.

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Devasena, D., Y. Dharshan, S. Nandhanaa, K. Trinetraa, and A. Sherlin Lincy. "Prediction of Stability of Real Time Network Control System for Pressure Process Station." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763202.

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Mohamed, M. Varusai, Ankita Nainwal, Ayshwarya B, H. S. Kumar, Dhaval Rabadiya, and Syed Noeman Taqui. "Optimized Siamese Humboldt Squid Attention Network Architecture for Cyber-Attack Detection in IoT 4.0." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763149.

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K, Kirubanandham, Suriyaprakash S, Sabareeswaran G, Sanjay P, and Shalini A. "Deep Belief Network-based Decentralized Secure, Lightweight Data Auditing Scheme for Streaming Outsourcing Data." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763348.

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Reports on the topic "Network of expert systems"

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Reifman, J., T. Y. C. Wei, and J. E. Vitela. PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 1, Theory. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/219266.

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Reifman, J., and T. Y. C. Wei. PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 2, Code manual. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/219265.

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Miles, Gaines E., Yael Edan, F. Tom Turpin, et al. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were st

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Kim, Ozano, Sophie Witter, Jo Keatinge, Beth Scott, and Nicola Wardrop. What Works for Health Systems Strengthening: An Overview of the Evidence – Resource Toolkit. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.102.

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This resource toolkit has been produced by K4D in partnership with the Health Systems Team in the Foreign, Commonwealth & Development Office (FCDO) Human Development Department and renowned global health systems expert Prof. Sophie Witter (Queen Margaret University, Edinburgh). The toolkit is aimed at FCDO’s network of health advisors, and policy and programme managers based in country offices and central teams. The purpose of this toolkit is to summarise and present key messages from the latest evidence on ‘what works’ for health systems strengthening (HSS) to help embed a stronger HSS ap

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Willis, Larkin, Aneesha Badrinarayan, and Monica Martinez. Quality criteria for systems of performance assessment for school, district, and network leaders. Learning Policy Institute, 2022. http://dx.doi.org/10.54300/439.730.

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The Quality Criteria for Systems of Performance Assessment capture a set of research-based expectations for what it takes to support high-quality performance assessment systems within k–12 schools, districts, and networks. The Quality Criteria are based on a review of relevant literature and have been vetted by a panel of academic scholars and practitioner-experts.

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Castelaz, McAllister, Annaliese Cothron, Valerie Nieto, Irene Hilton, and Candace Owen. A Compendium of Veteran Oral Health Best, Promising, and Emerging Practice Approaches. American Institute of Dental Public Health, 2023. http://dx.doi.org/10.58677/eabt9111.

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Veterans often struggle to access affordable, high-quality dental care. Only 15% of veterans are eligible for dental care through the Veterans Affairs health care system. With the 2018 MISSION Act, Veterans Affairs (VA) expanded access to care for veterans through the Community Care Network (CCN). The CCN comprises six regional networks that serve as the contract vehicle for the VA to finance care for veterans from community providers like federally qualified health centers (FQHCs). This compendium was developed by the American Institute of Dental Public Health (AIDPH) in collaboration with NA

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Kaminski, Linda, Magaly Lavadenz, Elvira Armas, and Grecya López. No. 11, November 2022: Insights from Co-Designed English Learner Improvement Networks. Center for Equity for English Learners, 2022. http://dx.doi.org/10.15365/ceel.policy.12.

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This research brief presents a case study of an English Learner Improvement Network (ELIN), a group of educators focused on a shared problem of practice in English Learner education and supported through extensive collaboration between researchers and practitioners in English Learner education and Improvement Science. The case study involves an urban school district and a charter organization each serving between 50-80% of students who have ever been English Learners. The research brief identifies five key themes that contribute to knowledge of the English Learner Improvement Networks’ ability

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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Groeneveld, Andrew B., Stephanie G. Wood, and Edgardo Ruiz. Estimating Bridge Reliability by Using Bayesian Networks. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/39601.

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As part of an inspection, bridge inspectors assign condition ratings to the main components of a bridge’s structural system and identify any defects that they observe. Condition ratings are necessarily somewhat subjective, as they are influenced by the experience of the inspectors. In the current work, procedures were developed for making inferences on the reliability of reinforced concrete girders with defects at both the cross section and the girder level. The Bayesian network (BN) tools constructed in this work use simple structural m echanics to model the capacity of girders. By using expe

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Reeve, Sophie, Susanna Cartmell, Alice Mutimer, and Olivia Frost. e-Dialogues Spark Debate on the Dynamics of Agricultural Commercialisation. APRA, Future Agricultures Consortium, 2022. http://dx.doi.org/10.19088/apra.2022.029.

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In early 2022, the Agricultural Policy Research in Africa (APRA) Programme of the Future Agricultures Consortium (FAC), in partnership with the United Nations Sustainable Development Solutions Network and Foresight4Food, held an e-Dialogue series: Towards an Equitable and Sustainable Transformation of Food Systems. This followed an earlier, highly successful series organised with the same partners in the second half of 2020 on What Future for Small-Scale Farming? The latest series included three online Zoom sessions led by APRA over January-March 2022 on topics including COVID-19 and its effec

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