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Gotowe bibliografie tematyczne / GENERIC DECISION META-MODEL

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Gotowa bibliografia na temat „GENERIC DECISION META-MODEL”

Autor: Grafiati

Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "GENERIC DECISION META-MODEL"

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Zlatev, Zlatko, Galina Veres, and Zoheir Sabeur. "Agile Data Fusion and Knowledge Base Architecture for Critical Decision Support." International Journal of Decision Support System Technology 5, no. 2 (2013): 1–20. http://dx.doi.org/10.4018/jdsst.2013040101.

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This paper describes the architecture and deployment of a software platform for information fusion, knowledge hosting and critical decision support. The work has been carried out under the TRIDEC project (www.tridec-online.eu), focusing on geo-information fusion and collaborative decision making. Four technologies underpin the architecture: 1) A message oriented middleware, for distributed communications; 2) A leveraged hybrid storage solution, for efficient storage of heterogeneous datasets and semantic knowledge; 3) A generic data fusion container, for dynamic algorithms control; and 4) A si

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LALLOUET, ARNAUD, and ANDREI LEGTCHENKO. "BUILDING CONSISTENCIES FOR PARTIALLY DEFINED CONSTRAINTS WITH DECISION TREES AND NEURAL NETWORKS." International Journal on Artificial Intelligence Tools 16, no. 04 (2007): 683–706. http://dx.doi.org/10.1142/s0218213007003503.

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Partially Defined Constraints can be used to model the incomplete knowledge of a concept or a relation. Instead of only computing with the known part of the constraint, we propose to complete its definition by using Machine Learning techniques. Since constraints are actively used during solving for pruning domains, building a classifier for instances is not enough: we need a solver able to reduce variable domains. Our technique is composed of two steps: first we learn a classifier for each constraint projections and then we transform the classifiers into a propagator. The first contribution is

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Lázaro, Elena, David Makowski, Joaquín Martínez-Minaya, and Antonio Vicent. "Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence." Agronomy 10, no. 4 (2020): 560. http://dx.doi.org/10.3390/agronomy10040560.

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Diseases of fruit and foliage caused by fungi and oomycetes are generally controlled by the application of fungicides. The use of decision support systems (DSSs) may assist to optimize fungicide programs to enhance application on the basis of risk associated with disease outbreak. Case-by-case evaluations demonstrated the performance of DSSs for disease control, but an overall assessment of the efficacy of DSSs is lacking. A literature review was conducted to synthesize the results of 67 experiments assessing DSSs. Disease incidence data were obtained from published peer-reviewed field trials

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Younis, Eman M. G., Someya Mohsen Zaki, Eiman Kanjo, and Essam H. Houssein. "Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion." Sensors 22, no. 15 (2022): 5611. http://dx.doi.org/10.3390/s22155611.

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Automatic recognition of human emotions is not a trivial process. There are many factors affecting emotions internally and externally. Expressing emotions could also be performed in many ways such as text, speech, body gestures or even physiologically by physiological body responses. Emotion detection enables many applications such as adaptive user interfaces, interactive games, and human robot interaction and many more. The availability of advanced technologies such as mobiles, sensors, and data analytics tools led to the ability to collect data from various sources, which enabled researchers

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Franz, Kamila, Jerzy Romanowski, Karin Johst, and Volker Grimm. "Porównawcza ocena programów analizy żywotności populacji (PVA) w rankingu scenariuszy przekształceń krajobrazu = A comparative assessment of PVA software packages applied to rank the landscape management scenarios." Przegląd Geograficzny 93, no. 3 (2021): 365–85. http://dx.doi.org/10.7163/przg.2021.3.3.

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Because of the scale and speed of species extinctions conservationists require methods that facilitate decision making. Therefore, a wide range of habitat and population viability analysis (PVA) software has been developed. Given the diversity of available programs it is currently challenging to decide which program is the most appropriate for a particular problem and what has to be considered when interpreting and comparing results from different approaches. Previous comparisons of PVA software addressed more generic questions such as data requirements, assumptions and predictive accuracy. In

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Blanco Mejia, Sonia, Mark Messina, Siying S. Li, et al. "A Meta-Analysis of 46 Studies Identified by the FDA Demonstrates that Soy Protein Decreases Circulating LDL and Total Cholesterol Concentrations in Adults." Journal of Nutrition 149, no. 6 (2019): 968–81. http://dx.doi.org/10.1093/jn/nxz020.

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ABSTRACT Background Certain plant foods (nuts and soy protein) and food components (viscous fibers and plant sterols) have been permitted by the FDA to carry a heart health claim based on their cholesterol-lowering ability. The FDA is currently considering revoking the heart health claim for soy protein due to a perceived lack of consistent LDL cholesterol reduction in randomized controlled trials. Objective We performed a meta-analysis of the 46 controlled trials on which the FDA will base its decision to revoke the heart health claim for soy protein. Methods We included the 46 trials on adul

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Rezaei, Mahdi, Mohsen Akbarpour Shirazi, and Behrooz Karimi. "IoT-based framework for performance measurement." Industrial Management & Data Systems 117, no. 4 (2017): 688–712. http://dx.doi.org/10.1108/imds-08-2016-0331.

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Purpose The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The aims of the proposed model are to optimize the performance indicator based on integrated supply chain operations reference metrics. Design/methodology/approach The SC multi-dimensional structure is modeled by multi-objective optimization methods. The operational presented model considers important SC features thoroughly such as multi-echelons, several suppliers, several manufacturers and several products during multiple

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Mohamed, Marwa F., Mohamed Meselhy Eltoukhy, Khalil Al Ruqeishi, and Ahmad Salah. "An Adapted Multi-Objective Genetic Algorithm for Healthcare Supplier Selection Decision." Mathematics 11, no. 6 (2023): 1537. http://dx.doi.org/10.3390/math11061537.

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With the advancement of information technology and economic globalization, the problem of supplier selection is gaining in popularity. The impact of supplier selection decisions made were quick and noteworthy on the healthcare profitability and total cost of medical equipment. Thus, there is an urgent need for decision support systems that address the optimal healthcare supplier selection problem, as this problem is addressed by a limited number of studies. Those studies addressed this problem mathematically or by using meta-heuristics methods. The focus of this work is to advance the meta-heu

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Bansal, Ankita, and Sourabh Jajoria. "Cross-Project Change Prediction Using Meta-Heuristic Techniques." International Journal of Applied Metaheuristic Computing 10, no. 1 (2019): 43–61. http://dx.doi.org/10.4018/ijamc.2019010103.

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Changes in software systems are inevitable. Identification of change-prone modules can help developers to focus efforts and resources on them. In this article, the authors conduct various intra-project and cross-project change predictions. The authors use distributional characteristics of dataset to generate rules which can be used for successful change prediction. The authors analyze the effectiveness of meta-heuristic decision trees in generating rules for successful cross-project change prediction. The employed meta-heuristic algorithms are hybrid decision tree genetic algorithms and obliqu

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SAKALLI, Umit Sami, and Irfan ATABAS. "Ant Colony Optimization and Genetic Algorithm for Fuzzy Stochastic Production-Distribution Planning." Applied Sciences 8, no. 11 (2018): 2042. http://dx.doi.org/10.3390/app8112042.

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In this paper, a tactical Production-Distribution Planning (PDP) has been handled in a fuzzy and stochastic environment for supply chain systems (SCS) which has four echelons (suppliers, plants, warehouses, retailers) with multi-products, multi-transport paths, and multi-time periods. The mathematical model of fuzzy stochastic PDP is a NP-hard problem for large SCS because of the binary variables which determine the transportation paths between echelons of the SCS and cannot be solved by optimization packages. In this study, therefore, two new meta-heuristic algorithms have been developed for

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