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Journal articles on the topic 'GENERIC DECISION META-MODEL'

Author: Grafiati

Published: 11 September 2023

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1

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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Golshanrad, Paria, Hossein Rahmani, Banafsheh Karimian, Fatemeh Karimkhani, and Gerhard Weiss. "MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm." Intelligent Data Analysis 25, no. 6 (2021): 1547–63. http://dx.doi.org/10.3233/ida-205494.

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Classifier combination through ensemble systems is one of the most effective approaches to improve the accuracy of classification systems. Ensemble systems are generally used to combine classifiers; However, selecting the best combination of individual classifiers is a challenging task. In this paper, we propose an efficient assembling method that employs both meta-learning and a genetic algorithm for the selection of the best classifiers. Our method is called MEGA, standing for using MEta-learning and a Genetic Algorithm for algorithm recommendation. MEGA has three main components: Training,

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Gaikwad, D. P., and S. V. Chaitanya. "Grading Method of Ensemble and Genetic Algorithm for Intrusion Detection System." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, Spl-2 issu (2022): 262–70. http://dx.doi.org/10.18090/samriddhi.v14spli02.11.

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Intrusion Detection System is very important tool for network security. However, Intrusion Detection System suffers from the problem of handling large volume of data and produces high false positive rate. In this paper, a novel Grading method of ensemble has proposed to overcome limitation of intrusion detection system. Partial decision tree (PART), RIpple DOwn Rule (RIDOR) learner and J48 decision tree have used as base classifiers of Grading classifier. Optimzed Genetic Search algorithm have used for selection of features.These three base classifiers have graded using RandomForest decision t

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STEBELETSKYI, Myroslav, Eduard MANZIUK, Tetyana SKRYPNYK, and Ruslan BAHRIY. "METHOD OF BUILDING ENSEMBLES OF MODELS FOR DATA CLASSIFICATION BASED ON DECISION CORRELATIONS." Herald of Khmelnytskyi National University. Technical sciences 315, no. 6(1) (2022): 224–33. http://dx.doi.org/10.31891/2307-5732-2022-315-6-224-233.

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The scientific work highlights the problem of increasing the accuracy of binary classification predictions using machine learning algorithms. Over the past few decades, systems that consist of many machine learning algorithms, also called ensemble models, have received increasing attention in the computational intelligence and machine learning community. This attention is well deserved, as ensemble systems have proven to be very effective and extremely versatile in a wide range of problem domains and real-world applications. One algorithm may not make a perfect prediction for a particular data

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Ansarifar, Javad, Reza Tavakkoli-Moghaddam, Faezeh Akhavizadegan, and Saman Hassanzadeh Amin. "Multi-objective integrated planning and scheduling model for operating rooms under uncertainty." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 232, no. 9 (2018): 930–48. http://dx.doi.org/10.1177/0954411918794721.

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This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective functio

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Fekri, Masoud, Mehdi Heydari, and Mohammad Mahdavi Mazdeh. "Two-objective optimization of preventive maintenance orders scheduling as a multi-skilled resource-constrained flow shop problem." Decision Science Letters 12, no. 1 (2023): 41–54. http://dx.doi.org/10.5267/j.dsl.2022.10.007.

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In this article, the application of the Multi-Skilled Resource-Constrained Flow Shop Scheduling Problem (MSRC-FSSP) in preventive maintenance as a case study has been investigated. In other words, to complete each maintenance order at each stage, in addition to the machine, a set of required human resources with different skills must be available. According to human resources skills, each of them can perform at least one order or at most N orders, and each maintenance order must be done by a set of human resources with different skills. To carry out a maintenance order, different human resourc

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Sepulveda, Geraldine Cáceres, Silvia Ochoa, and Jules Thibault. "Methodology to Solve the Multi-Objective Optimization of Acrylic Acid Production Using Neural Networks as Meta-Models." Processes 8, no. 9 (2020): 1184. http://dx.doi.org/10.3390/pr8091184.

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It is paramount to optimize the performance of a chemical process in order to maximize its yield and productivity and to minimize the production cost and the environmental impact. The various objectives in optimization are often in conflict, and one must determine the best compromise solution usually using a representative model of the process. However, solving first-principle models can be a computationally intensive problem, thus making model-based multi-objective optimization (MOO) a time-consuming task. In this work, a methodology to perform the multi-objective optimization for a two-react

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Dobashi, Nao, Shota Saito, Yuta Nakahara, and Toshiyasu Matsushima. "Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction." Entropy 23, no. 6 (2021): 768. http://dx.doi.org/10.3390/e23060768.

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This paper deals with a prediction problem of a new targeting variable corresponding to a new explanatory variable given a training dataset. To predict the targeting variable, we consider a model tree, which is used to represent a conditional probabilistic structure of a targeting variable given an explanatory variable, and discuss statistical optimality for prediction based on the Bayes decision theory. The optimal prediction based on the Bayes decision theory is given by weighting all the model trees in the model tree candidate set, where the model tree candidate set is a set of model trees

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Hibino, Masaya, Chisato Hamashima, Mitsunaga Iwata, and Teruhiko Terasawa. "Effectiveness of decision aids on cancer-screening decision-making: an umbrella review protocol." BMJ Open 11, no. 12 (2021): e051156. http://dx.doi.org/10.1136/bmjopen-2021-051156.

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IntroductionAlthough systematic reviews have shown how decision aids about cancer-related clinical decisions improve selection of key options and shared decision-making, whether or not particular decision aids, defined by their specific presentation formats, delivery methods and other attributes, can perform better than others in the context of cancer-screening decisions is uncertain. Therefore, we planned an overview to address this issue by using standard umbrella review methods to repurpose existing systematic reviews and their component comparative studies.Methods and analysisWe will searc

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Atigui, Faten, Franck Ravat, Jiefu Song, Olivier Teste, and Gilles Zurfluh. "Facilitate Effective Decision-Making by Warehousing Reduced Data." International Journal of Decision Support System Technology 7, no. 3 (2015): 36–64. http://dx.doi.org/10.4018/ijdsst.2015070103.

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The authors' aim is to provide a solution for multidimensional data warehouse's reduction based on analysts' needs which will specify aggregated schema applicable over a period of time as well as retain only useful data for decision support. Firstly, they describe a conceptual modeling for multidimensional data warehouse. A multidimensional data warehouse's schema is composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. The derivation between states is carried out through combination of reduction operators. Secondly, they present a

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Young, Mike. "A meta model of change." Journal of Organizational Change Management 22, no. 5 (2009): 524–48. http://dx.doi.org/10.1108/09534810910983488.

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Guo, Yurong, Quan Shi, and Chiming Guo. "A Performance-Oriented Optimization Framework Combining Meta-Heuristics and Entropy-Weighted TOPSIS for Multi-Objective Sustainable Supply Chain Network Design." Electronics 11, no. 19 (2022): 3134. http://dx.doi.org/10.3390/electronics11193134.

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The decision-making of sustainable supply chain network (SSCN) design is a strategy capacity for configuring network facility and product flow. When optimizing conflicting economic, environmental, and social performance objectives, it is difficult to select the optimal scheme from obtained feasible decision schemes. In this article, according to the triple bottom line of sustainability, a multi-objective sustainable supply chain network optimization model is developed, and a novel performance-oriented optimization framework is proposed. This framework, referred to as performance-oriented optim

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Kandala, Bhargava, Nele Plock, Akshita Chawla, et al. "Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach." eBioMedicine 84 (October 2022): 104264. http://dx.doi.org/10.1016/j.ebiom.2022.104264.

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Banubakode, Abhijit, and Mohammed Gadhia. "Query Optimization in Object Oriented Database Using Cursor with Special Reference to Parallel Processing." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, Spl-2 issu (2022): 301–6. http://dx.doi.org/10.18090/samriddhi.v14spli02.18.

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Intrusion Detection System is very important tool for network security. However, Intrusion Detection System suffers from the problem of handling large volume of data and produces high false positive rate. In this paper, a novel Grading method of ensemble has proposed to overcome limitation of intrusion detection system. Partial decision tree (PART), RIpple DOwn Rule (RIDOR) learner and J48 decision tree have used as base classifiers of Grading classifier. Optimzed Genetic Search algorithm have used for selection of features.These three base classifiers have graded using RandomForest decision t

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Kaufmann, Esther, Ulf-Dietrich Reips, and Werner W. Wittmann. "A Critical Meta-Analysis of Lens Model Studies in Human Judgment and Decision-Making." PLoS ONE 8, no. 12 (2013): e83528. http://dx.doi.org/10.1371/journal.pone.0083528.

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Hamid, Mahdi, Reza Tavakkoli-Moghaddam, Fereshte Golpaygani, and Behdin Vahedi-Nouri. "A multi-objective model for a nurse scheduling problem by emphasizing human factors." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 234, no. 2 (2019): 179–99. http://dx.doi.org/10.1177/0954411919889560.

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Assigning nurses to appropriate departments and work shifts based on human factors can strengthen teamwork and boost the efficiency of healthcare systems. The human factors considered in this study include skill, preference, and compatibility of nurses. In this regard, a unique multi-objective mathematical model for nurse scheduling is proposed in this article, in which nurses’ decision-making styles are taken into account. Three objectives, including minimization of the total cost of staffing, minimization of the sum of incompatibility among nurses’ decision-making styles assigned to the same

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Minaei, Farshad, Hassan Dosti, Ebrahim Salimi Turk, and Amin Golabpour. "Provide a Diagnostic Model Using a Combination of Two Neural Network Algorithms and a Genetic Algorithm." Frontiers in Health Informatics 10, no. 1 (2021): 91. http://dx.doi.org/10.30699/fhi.v10i1.303.

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Introduction: Improvement of technology can increase the use of machine learning algorithms in predicting diseases. Early diagnosis of the disease can reduce mortality and morbidity at the community level.Material and Methods: In this paper, a clinical decision support system for the diagnosis of gestational diabetes is presented by combining artificial neural network and meta-heuristic algorithm. In this study, four meta-innovative algorithms of genetics, ant colony, particle Swarm optimization and cuckoo search were selected to be combined with artificial neural network. Then these four algo

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Samadi Parviznejad, Paria, and Farshid Golzadeh. "The problem of production-distribution under uncertainty based on Vendor Managed Inventory." International Journal of Innovation in Engineering 2, no. 1 (2022): 22–39. http://dx.doi.org/10.59615/ijie.2.1.22.

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In this paper, a problem of managed inventory by the vendor in the production-distribution supply chain is presented based on the scenario. The main purpose of presenting the model of maximizing producer profit in a three-level supply chain network consisting of various strategic and tactical decisions under uncertainty. Due to the nonlinearity and NP-Hardness of the problem, meta-heuristic genetic algorithms, Whale optimization algorithm and league champions algorithm have been used. The results of problem solving show the high efficiency of meta-heuristic algorithms compared to accurate meth

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Harun-Or-Roshid, Md, Md Borqat Ali, Jesmin, and Md Nurul Haque Mollah. "Association of hypoxia inducible factor 1-Alpha gene polymorphisms with multiple disease risks: A comprehensive meta-analysis." PLOS ONE 17, no. 8 (2022): e0273042. http://dx.doi.org/10.1371/journal.pone.0273042.

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HIF1A gene polymorphisms have been confirmed the association with cancer risk through the statistical meta-analysis based on single genetic association (SGA) studies. A good number SGA studies also investigated the association of HIF1A gene with several other diseases, but no researcher yet performed statistical meta-analysis to confirm this association more accurately. Therefore, in this paper, we performed a statistical meta-analysis to draw a consensus decision about the association of HIF1A gene polymorphisms with several diseases except cancers giving the weight on large sample size. This

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Doyle, R. W., N. L. Shackel, Z. Basiao, S. Uraiwan, T. Matricia, and A. J. Talbot. "Selective Diversification of Aquaculture Stocks: A Proposal for Economically Sustainable Genetic Conservation." Canadian Journal of Fisheries and Aquatic Sciences 48, S1 (1991): 148–54. http://dx.doi.org/10.1139/f91-313.

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The genetic diversity of aquaculture stocks can be maintained, and their genetic impact on wild stocks minimized, by breeding programmes that deliberately generate genetic diversity. Current animal breeding practices are likely to reduce the diversity of domestic stocks if they are extended to aquaculture. It is proposed that national breeding programmes for aquaculture should, instead, try to develop numerous breeds specially adapted to local environments and aquaculture systems. An economic model is presented of decision-making by individual farmers who, in choosing which breed to produce, d

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Kisamore, Jennifer L., and Michael T. Brannick. "An Illustration of the Consequences of Meta-Analysis Model Choice." Organizational Research Methods 11, no. 1 (2007): 35–53. http://dx.doi.org/10.1177/1094428106287393.

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Slaney, Kathleen L., Donna Tafreshi, and Richard Hohn. "Random or Fixed? An Empirical Examination of Meta-Analysis Model Choices." Review of General Psychology 22, no. 3 (2018): 290–304. http://dx.doi.org/10.1037/gpr0000140.

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When conducting meta-analyses, researchers must make decisions about which statistical model is most appropriate for the specific context and aims of the meta-analysis. Although there are several meta-analysis models, most researchers choose between two general models: fixed-effect (FE) and random-effects (RE). Yet, the basis on which these two general models are distinguished and of when it is appropriate to use one or the other varies in the methodological literature. Although model-to-inference inconsistencies have been previously noted, there has been little empirical investigation of whet

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Basiri, Mohammad-Ali, Esmaeil Alinezhad, Reza Tavakkoli-Moghaddam, and Nasser Shahsavari-Poure. "A hybrid intelligent algorithm for a fuzzy multi-objective job shop scheduling problem with reentrant workflows and parallel machines." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7769–85. http://dx.doi.org/10.3233/jifs-201120.

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This paper presents a multi-objective mathematical model for a flexible job shop scheduling problem (FJSSP) with fuzzy processing times, which is solved by a hybrid intelligent algorithm (HIA). This problem contains a combination of a classical job shop problem with parallel machines (JSPM) to provide flexibility in the production route. Despite the previous studies, the number of parallel machines is not pre-specified in this paper. This constraint with other ones (e.g., sequence-dependent setup times, reentrant workflows, and fuzzy variables) makes the given problem more complex. To solve su

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WIRANATA, RICO BAYU. "A Genetic Algorithm Hyper-parameter Optimization of Ensemble Approach: Strategi Prediksi Saham Mempertimbangkan Indikator Teknikal & Sentimen Berita." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 8, no. 3 (2021): 1442–56. http://dx.doi.org/10.35957/jatisi.v8i3.1112.

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Investor harus memprediksi saham dengan tepat agar keuntungan maksimal sekaligus terhindar kebangkrutan. Namun bursa saham sulit dideteksi situasinya. Perilakunya berubah-ubah dipengaruhi berbagai faktor seperti situasi politik, ekonomi perusahaan dan global, maupun ekspektasi investor yang tersedia melalui berita. Penelitian ini bertujuan mengembangkan model yang dapat memprediksi saham lebih akurat mengkombinasikan indikator teknikal saham dan sentimen berita. Genetic algorithm (GA) mengoptimalisasi beberapa ensemble decision tree-based yang ditumpuk menggunakan metode stacked-generalization

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Assimakopoulos, Nikitas A. "Structured Total systems Intervention systemic multi-MEthodology of VIable Systems and metasystems (STIMEVIS)." Human Systems Management 19, no. 1 (2000): 61–69. http://dx.doi.org/10.3233/hsm-2000-19107.

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In this paper we present the Viable System Model (VSM) of Beer by examining its effectiveness in practice using a different and structured design. In order to support better viable systems, we deliver briefly our Problem Structuring Methodology (PSM) in strategic and procedure level. The conceptual environment of VSM within PSM will be presented as a systemic multi-methodology called “STIMEVIS” using the principles of Total Systems Intervention (TSI) methodology. In the new viable and operational system we analyse organisational structures in strategic and procedure level where control and met

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Osman, Khairul, and T'Ng Qi Feng. "Validation of Individual Identification Through Decision Tree Packet Header Profiling." Asia-Pacific Journal of Information Technology and Multimedia 11, no. 02 (2022): 97–111. http://dx.doi.org/10.17576/apjitm-2022-1102-08.

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The drastic rise in the cybercrime rate associated with the surge of users' dependence on the Internet has elevated the concern of digital forensic examiners toward the footprints of perpetrators left in a virtual environment. However, suspect identification is a big challenge in network forensics due to the anonymous nature of data transmission across the network. This study utilises the decision tree classification approach to characterise users from their behavioural web navigation pattern using the meta-data of captured network packets (Destination IP, Protocol, Port Source, and Port Desti

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Osman, Khairul, and Hairee Izzam Mohd Noor. "VALIDATION OF INDIVIDUAL IDENTIFICATION THROUGH DECISION TREE PACKET HEADER PROFILING." Asia-Pacific Journal of Information Technology and Multimedia 11, no. 02 (2022): 97–111. http://dx.doi.org/10.17576/apjitm.2022-0101-07.

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The drastic rise in the cybercrime rate associated with the surge of users' dependence on the Internet has elevated the concern of digital forensic examiners toward the footprints of perpetrators left in a virtual environment. However, suspect identification is a big challenge in network forensics due to the anonymous nature of data transmission across the network. This study utilises the decision tree classification approach to characterise users from their behavioural web navigation pattern using the meta-data of captured network packets (Destination IP, Protocol, Port Source, and Port Desti

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Kaveh, Firoozeh, Reza Tavakkoli-Moghaddam, Chefi Triki, Yaser Rahimi, and Amin Jamili. "A new bi-objective model of the urban public transportation hub network design under uncertainty." Annals of Operations Research 296, no. 1-2 (2019): 131–62. http://dx.doi.org/10.1007/s10479-019-03430-9.

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AbstractThis paper presents a new bi-objective multi-modal hub location problem with multiple assignment and capacity considerations for the design of an urban public transportation network under uncertainty. Because of the high construction costs of hub links in an urban public transportation network, it is not economic to create a complete hub network. Moreover, the demand is assumed to be dependent on the utility proposed by each hub. Thus, the elasticity of the demand is considered in this paper. The presented model also has the ability to compute the number of each type of transportation

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Moosavi, Seyed Mohammad Shams, Mehdi Seifbarghy, and Seyed Mohammad Haji Molana. "Flexible fuzzy-robust optimization method in closed-loop supply chain network problem modeling for the engine oil industry." Decision Making: Applications in Management and Engineering 6, no. 2 (2023): 461–502. http://dx.doi.org/10.31181/dmame622023569.

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This study models a closed loop supply chain network for the Iranian engine oil market. The primary goal of the created model is to summarize tactical choices like choosing the best degree of discount and allocating the best flow of products across facilities as well as strategic decisions like selecting a supplier and finding new facilities. The three aim functions of reducing overall expenses, optimizing employment rate, and limiting unrealized demand are considered. The novel flexible fuzzy robust optimization approach also controls the uncertainty parameters and the meta-heuristics algorit

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Tavakoli, Mohammad Mehdi, Seyed Mojtaba Sajadi, and Seyed Ali Sadeghi Aghili. "Proposing a new mathematical model and a meta-heuristic algorithm for scheduling and allocating automated guided vehicle." International Journal of Mathematics in Operational Research 13, no. 2 (2018): 202. http://dx.doi.org/10.1504/ijmor.2018.094055.

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Kao, Chung-Cheng, Hsiang-En Hsu, Yi-Chou Chen, Ming-Yu Tu, Su-Wen Chuang, and Sui-Lung Su. "The Decisive Case-Control Study Elaborates the Null Association between ADAMTS5 rs226794 and Osteoarthritis in Asians: A Case-Control Study and Meta-Analysis." Genes 12, no. 12 (2021): 1916. http://dx.doi.org/10.3390/genes12121916.

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Background: Osteoarthritis is an important health issue for the elderly. Many studies indicate that genetics is an important risk factor for osteoarthritis, and a disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS5) is one gene that is most frequently implicated. Many recent studies have examined the relationship between a polymorphism in the ADAMTS5 gene (rs226794) and the risk for developing osteoarthritis without definitive results. Objective: In this case-control study, we examined the correlation between the ADAMTS5 gene polymorphism, rs226794, and knee osteoarthritis.

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Kaufmann, Esther, and James A. Athanasou. "A Meta-Analysis of Judgment Achievement as Defined by the Lens Model Equation." Swiss Journal of Psychology 68, no. 2 (2009): 99–112. http://dx.doi.org/10.1024/1421-0185.68.2.99.

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This research determined the extent of judgment achievement (i.e., accuracy) across different decision-making domains (e.g., medicine, business, education, psychology). Judgment achievement was examined in terms of the lens model equation ( Tucker, 1964 ). A meta-analysis of 29 studies incorporating 1,032 people across 43 judgment tasks was performed. Overall judgment achievement across different tasks was found to be moderate (r = .42), ranging from .22 for studies in the area of psychology to .58 for those in other professional areas. Methodological moderator factors (type of correlation, da

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Hazrati, Hanif, Abbas Barzegarinegad, and Hamid Siaby-Serajehlo. "A Hybrid Mathematical and Decision-Making Model to Determine the Amount of Economic Order considering the Discount." Mathematical Problems in Engineering 2021 (December 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/5229949.

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Suppliers are one of the most important parts of the supply chain, whose performance indirectly has a significant impact on customer satisfaction. Because customer demands are different from organizations, organizations have to consider different criteria for selecting their suppliers. In recent years, many studies in this field have been conducted using various criteria and methods. The main purpose defined in this research is to develop a model for simultaneous item ordering systems in real business conditions. In this research, a model is developed by considering the two objectives of minim

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Ellis, Erin M., Glyn Elwyn, Wendy L. Nelson, Peter Scalia, Sarah C. Kobrin, and Rebecca A. Ferrer. "Interventions to Engage Affective Forecasting in Health-Related Decision Making: A Meta-Analysis." Annals of Behavioral Medicine 52, no. 2 (2018): 157–74. http://dx.doi.org/10.1093/abm/kax024.

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AbstractBackgroundPeople often use affective forecasts, or predictions about how a decision will make them feel, to guide medical and health decision making. However, these forecasts are susceptible to biases and inaccuracies that can have consequential effects on decision making and health.PurposeA meta-analysis was performed to determine the effectiveness of intervening to address affective forecasting as a means of helping patients make better health-related choices.MethodsWe included between-subjects experimental and intervention studies that targeted variables related to affective forecas

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Dwilaga, Armando Tirta. "Decision Model and Industry Optimization in Production: A Systematic Literature review." Sainteks: Jurnal Sains dan Teknik 5, no. 1 (2023): 57–71. http://dx.doi.org/10.37577/sainteks.v5i1.528.

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This article aims to discover the modeling and optimization options relevant to production-related industrial sectors. PRISMA (Preferred Reporting Items for Systematic Review and Meta-analyses) is a preferred submission method with inclusive and exclusive criteria, one of the bases for the selection made from the ScienceDirect index database only for 2018, 2019, 2020, 2021, and 2022 is understanding decision models and optimization with production keywords. As a result, 823 articles were converted to 100 articles and 16 articles adjacent to the final selection of 10 articles were used. The det

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Muslim, Much Aziz, and Yosza Dasril. "Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5549. http://dx.doi.org/10.11591/ijece.v11i6.pp5549-5557.

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<span>Company bankruptcy is often a very big problem for companies. The impact of bankruptcy can cause losses to elements of the company such as owners, investors, employees, and consumers. One way to prevent bankruptcy is to predict the possibility of bankruptcy based on the company's financial data. Therefore, this study aims to find the best predictive model or method to predict company bankruptcy using the dataset from Polish companies bankruptcy. The prediction analysis process uses the best feature selection and ensemble learning. The best feature selection is selected using featur

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Wang, Na, Yaping Fu, and Hongfeng Wang. "A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system." Advances in Mechanical Engineering 11, no. 12 (2019): 168781401989834. http://dx.doi.org/10.1177/1687814019898347.

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With the wide application of advanced information technology and intelligent equipment in the manufacturing system, the decisions of design and operation have become more interdependent and their integration optimization has gained great concerns from the community of operational research recently. This article investigates an optimization problem of integrating dynamic resource allocation and production schedule in a parallel machine environment. A meta-heuristic algorithm, in which heuristic-based partition, genetic-based sampling, promising index calculation, and backtracking strategies are

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He, Qingfeng, Zhihao Xu, Shaojun Li, et al. "Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling." Entropy 21, no. 2 (2019): 106. http://dx.doi.org/10.3390/e21020106.

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Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using c

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Xu, B. Y., H. M. Cai, and C. Xie. "An Ontology Approach for Manufacturing Enterprise Data Warehouses Development." Advanced Materials Research 215 (March 2011): 77–82. http://dx.doi.org/10.4028/www.scientific.net/amr.215.77.

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Data warehouse (DW) is a powerful and useful technology for decision making in manufacturing enterprises. Because that the operational data often comes from distributed units for manufacturing enterprises, there exits an urgent need to study on the methods of integrating heterogonous data in data warehouse. In This paper, an ontology approach is proposed to eliminate data source heterogeneity. The approach is based on the exploitation of the application of domain ontology methods in data warehouse design, representing the semantic meanings of the data by ontology at database level and pushing

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Mojahedi, Houri, Amin Babazadeh Sangar, and Mohammad Masdari. "Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm." Mathematical Problems in Engineering 2022 (September 5, 2022): 1–17. http://dx.doi.org/10.1155/2022/1027518.

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This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This researc

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Hadi, Fransidha Sidhara, Janu Arlinwibowo, and Gupita Nadindra Fatima. "A meta-analysis of the relationship between self-assessment and Mathematics learning achievement." Jurnal Penelitian dan Evaluasi Pendidikan 27, no. 1 (2023): 39–51. http://dx.doi.org/10.21831/pep.v27i1.60617.

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This study uses a meta-analysis model to describe the relationship between self-assessment and achievement in learning mathematics. This meta-analysis covers articles published from 2011 to 2022 and is restricted to studies published in English. Indexed articles by Google Scholar were chosen. The data must meet these criteria: quantitative research, containing correlational research on the relationship between self-assessment and mathematics learning achievement (including correlation values and sample size). Through the data screening process with predetermined criteria, 12 research results w

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