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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 (April 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 single conceptual model and schema, as systems’ semantic meta-model. Deployment for industrial drilling operations is described. Agility is manifested with the ability to integrate data sources from a proprietary domain, dynamically discover new datasets and configure and task fusion algorithms to operate on them, aided by efficient information storage. The platform empowers decision support by enabling dynamic discovery of information and control of the fusion process across geo-distributed locations.
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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 (August 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 a generic meta-technique for classifier improvement showing performances comparable to boosting. The second lies in the ability of using the learned concept in constraint-based decision or optimization problems. We presents results using Decision Trees and Artificial Neural Networks for constraint learning and propagation. It opens a new way of integrating Machine Learning in Decision Support Systems.
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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 (April 13, 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 comparing untreated controls, calendar-based and DSS-based fungicide programs. Two meta-analysis generic models, a “fixed-effects” vs. a “random-effects” model within the framework of generalized linear models were evaluated to assess the efficacy of DSSs in reducing incidence. All models were fit using both frequentist and Bayesian estimation procedures and the results compared. Model including random effects showed better performance in terms of AIC or DIC and goodness of fit. In general, the frequentist and Bayesian approaches produced similar results. Odds ratio and incidence ratio values showed that calendar-based and DSS-based fungicide programs considerably reduced disease incidence compared to the untreated control. Moreover, calendar-based and DSS-based programs provided similar reductions in disease incidence, further supporting the efficacy of DSSs.
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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 (July 27, 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 to predict human emotions accurately. Most current research uses them in the lab experiments for data collection. In this work, we use direct and real time sensor data to construct a subject-independent (generic) multi-modal emotion prediction model. This research integrates both on-body physiological markers, surrounding sensory data, and emotion measurements to achieve the following goals: (1) Collecting a multi-modal data set including environmental, body responses, and emotions. (2) Creating subject-independent Predictive models of emotional states based on fusing environmental and physiological variables. (3) Assessing ensemble learning methods and comparing their performance for creating a generic subject-independent model for emotion recognition with high accuracy and comparing the results with previous similar research. To achieve that, we conducted a real-world study “in the wild” with physiological and mobile sensors. Collecting the data-set is coming from participants walking around Minia university campus to create accurate predictive models. Various ensemble learning models (Bagging, Boosting, and Stacking) have been used, combining the following base algorithms (K Nearest Neighbor KNN, Decision Tree DT, Random Forest RF, and Support Vector Machine SVM) as base learners and DT as a meta-classifier. The results showed that, the ensemble stacking learner technique gave the best accuracy of 98.2% compared with other variants of ensemble learning methods. On the contrary, bagging and boosting methods gave (96.4%) and (96.6%) accuracy levels respectively.
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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 contract, we focus on a more applied problem that is still unresolved: how do simple habitat models and PVA software packages affect the ranking of alternative management scenarios? We addressed this problem by comparing different packages (LARCH, META-X, VORTEX and RAMAS GIS). As a test case, we studied the impact of alternative landscape development scenarios (river regulation, grassland restoration, reforestation and renaturalisation) for the Vistula valley, Poland, on the natterjack toad (Bufo calamita). In this context we also aimed to assess whether the use of at least two different PVA packages can enable users to better understand the differences in model predictions, which would imply a greater awareness and critical use of the packages. Our model selection represents different approaches to population viability analysis, including habitat, local population and stochastic patch occupancy models. The models can be evaluated in regard to the complexity of parameters and to the way the landscape is handled. We used RAMAS GIS to create a habitat model (RAMASh) and a detailed spatially explicit stochastic metapopulation model (RAMASp) which combined served as a complete “virtual” dataset for parameterisation of other programs. As an example of a stochastic patch occupancy model, we selected the META-X software. For a more independent comparison we added VORTEX – another package that includes explicit population dynamics, similar to RAMAS. Additionally, we included the habitat model LARCH because this type of model is often used by policy makers. We compared the metapopulation structure produced by RAMASh and LARCH. Scenario ranking according to the predicted carrying capacity in both programs was exactly the same, because the quantitative results for each scenario were almost identical in both programs. However, the metapopulation structure showed big differences between the programs, especially in the number of small populations. The analyses of results of different PVA programs (RAMASp, VORTEX and META-X) showed that absolute values of viability measures partly differed among these programs. Slight differences in population growth rate in RAMASp and VORTEX were amplified by stochasticity and resulted in visibly lower values of final abundance in VORTEX than in RAMASp. Also the absolute values of intrinsic mean time to extinction showed some discrepancies in VORTEX and META-X. These results are in agreement with findings of previous PVA comparisons, which emphasizes that absolute values of viability measures produced by any single model should be treated with caution. Nevertheless, despite these differences the rankings of the scenarios were the same in all three programs. However the order of the scenarios was different than in habitat models. In addition, these rankings were robust to the choice of viability measure. Taken together, these results emphasize that scenario ranking delivered by PVA software is robust and thus very useful for conservation management. Furthermore, we recommend using at least two PVA software packages in parallel, as this forces user to scrutinize the simplifying assumptions of the underlying models and of the viability metrics used.
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Blanco Mejia, Sonia, Mark Messina, Siying S. Li, Effie Viguiliouk, Laura Chiavaroli, Tauseef A. Khan, Korbua Srichaikul, 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 (April 22, 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 adult men and women, with baseline circulating LDL cholesterol concentrations ranging from 110 to 201 mg/dL, as identified by the FDA, that studied the effects of soy protein on LDL cholesterol and total cholesterol (TC) compared with non-soy protein. Two independent reviewers extracted relevant data. Data were pooled by the generic inverse variance method with a random effects model and expressed as mean differences with 95% CI. Heterogeneity was assessed and quantified. Results Of the 46 trials identified by the FDA, 43 provided data for meta-analyses. Of these, 41 provided data for LDL cholesterol, and all 43 provided data for TC. Soy protein at a median dose of 25 g/d during a median follow-up of 6 wk decreased LDL cholesterol by 4.76 mg/dL (95% CI: −6.71, −2.80 mg/dL, P < 0.0001; I2 = 55%, P < 0.0001) and decreased TC by 6.41 mg/dL (95% CI: −9.30, −3.52 mg/dL, P < 0.0001; I2 = 74%, P < 0.0001) compared with non-soy protein controls. There was no dose–response effect or evidence of publication bias for either outcome. Inspection of the individual trial estimates indicated most trials (∼75%) showed a reduction in LDL cholesterol (range: −0.77 to −58.60 mg/dL), although only a minority of these were individually statistically significant. Conclusions Soy protein significantly reduced LDL cholesterol by approximately 3–4% in adults. Our data support the advice given to the general public internationally to increase plant protein intake. This trial was registered at clinicaltrials.gov as NCT03468127.
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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 (May 8, 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 periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., non-dominated sorting genetic algorithm II. Afterward, Technique for Order of Preference by Similarity to Ideal Solution method is used to determine the best operational solution based on the strategic decision maker’s idea. Findings This paper proposes a dynamic integrated solution for three main problems: strategic decisions in high level, operational decisions in low level and alignment of these two decision levels. Originality/value The authors propose a human intelligence-based process for high level decision and machine intelligence-based decision support systems for low level decision using a novel approach. High level and low level decisions are aligned by a machine intelligence model as well. The presented framework is based on change detection, event driven planning and real-time decision alignment.
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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 (March 22, 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-heuristics methods by considering more objectives rather than the utilized objectives. In this context, the optimal supplier selection problem for healthcare equipment was formulated as a mathematical model to expose the required objectives and constraints for the sake of searching for the optimal suppliers. Subsequently, the problem is realized as a multi-objective problem, with the help of this proposed model. The model has three minimization objectives: (1) transportation cost; (2) delivery time; and (3) the number of damaged items. The proposed system includes realistic constraints such as device quality, usability, and service quality. The model also takes into account capacity limits for each supplier. Next, it is proposed to adapt the well-known non-dominated sorting genetic algorithm (NSGA)-III algorithm to choose the optimal suppliers. The results of the adapted NSGA-III have been compared with several heuristic algorithms and two meta-heuristic algorithms (i.e., particle swarm optimization and NSGA-II). The obtained results show that the adapted NSGA-III outperformed the methods of comparison.
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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 (January 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 oblique decision trees with evolutionary learning. The authors compare the performance of these meta-heuristic algorithms with C4.5 decision tree model. The authors observe that the accuracy of C4.5 decision tree is 73.33%, whereas the accuracy of the hybrid decision tree genetic algorithm and oblique decision tree are 75.00% and 75.56%, respectively. These values indicate that distributional characteristics are helpful in identifying suitable training set for cross-project change prediction.
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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 (October 24, 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 solving fuzzy stochastic PDP: Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The proposed meta-heuristic algorithms are designed for route optimization in PDP and integrated with the GAMS optimization package in order to solve the remaining mathematical model which determines the other decisions in SCS, such as procurement decisions, production decisions, etc. The solution procedure in the literature has been extended by aggregating proposed meta-heuristic algorithms. The ACO and GA algorithms have been performed for test problems which are randomly generated. The results of the test problem showed that the both ACO and GA are capable to solve the NP-hard PDP for a big size SCS. However, GA produce better solutions than the ACO.
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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 (October 29, 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, Model Interpretation and Testing. The Training component extracts meta-features of each training dataset and uses a genetic algorithm to discover the best classifier combination. The Model Interpretation component interprets the relationships between meta-features and classifiers using a priori and multi-label decision tree algorithms. Finally, the Testing component uses a weighted k-nearest-neighbors algorithm to predict the best combination of classifiers for unseen datasets. We present extensive experimental results that demonstrate the performance of MEGA. MEGA achieves superior results in a comparison of three other methods and, most importantly, is able to find novel interpretable rules that can be used to select the best combination of classifiers for an unseen dataset.
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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 (June 30, 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 tree as a Meta classifier. Experimental results show that the proposed Grading method of classification offers accuracies of 81.3742%, 99.9159% and 99.8023% on testing, training datasets and cross validation respectively. It is found that the proposed graded classifier outperform its base classifiers and existing hybrid intrusion detection system in term of accuracy, false positive rate and model building time.
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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) (December 29, 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 set. Machine learning algorithms have their limitations, so creating a model with high accuracy is a difficult task. If you create and combine several models by combining and aggregating the results of each model, there is a chance to improve the overall accuracy, this problem is dealt with by ensembling. The basis of the information system of binary classification is the ensemble model. This model, in turn, contains a set of unique combinations of basic classifiers – a kind of algorithmic primitives. An ensemble model can be considered as some kind of meta-algorithm, which consists of unique sets of machine learning (ML) classification algorithms. The task of the ensemble model is to find such a combination of basic classification algorithms that would give the highest performance. The performance is evaluated according to the main ML metrics in classification tasks. Another aspect of scientific work is the creation of an aggregation mechanism for combining the results of basic classification algorithms. That is, each unique combination within the ensemble consists of a set of basic models (harbingers), the results of which must be aggregated. In this work, a non-hierarchical clustering method is used to aggregate (average) the predictions of the base models. A feature of this study is to find the correlation coefficients of the base models in each combination. With the help of the magnitude of correlations, the relationship between the prediction of the classifier (base model) and the true value is established, as a result of which space is opened for further research on improving the ensemble model (meta-algorithm)
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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 (September 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 function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.
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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 resources must be in communication and cooperation so that a preventive maintenance order can be completed. In this article, these resources are considered as technical supervisors, repairmen and maintenance managers who complete all maintenance orders in a flow shop environment as a job. For this problem, a new Mixed Integer Linear Programming (MILP) model has been formulated with the two-objective functions, minimizing total orders completion time and the human resources idle time. To solve the model on a small scale, CPLEX is used, and to solve it on a large scale, due to the fact that this problem is NP-Hard, a meta-heuristic algorithm named Genetic Algorithm (GA) is presented. Finally, the computational results have been done to validate the model, along with the analysis of the human resources idle time.
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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 (September 18, 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-reactor system for the production of acrylic acid, using artificial neural networks (ANNs) as meta-models, is proposed in an effort to reduce the computational time required to circumscribe the Pareto domain. The performance of the meta-model confirmed good agreement between the experimental data and the model-predicted values of the existent relationships between the eight decision variables and the nine performance criteria of the process. Once the meta-model was built, the Pareto domain was circumscribed based on a genetic algorithm (GA) and ranked with the net flow method (NFM). Using the ANN surrogate model, the optimization time decreased by a factor of 15.5.
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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 (June 18, 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 in which the true model tree is assumed to be included. Because the number of all the model trees in the model tree candidate set increases exponentially according to the maximum depth of model trees, the computational complexity of weighting them increases exponentially according to the maximum depth of model trees. To solve this issue, we introduce a notion of meta-tree and propose an algorithm called MTRF (Meta-Tree Random Forest) by using multiple meta-trees. Theoretical and experimental analyses of the MTRF show the superiority of the MTRF to previous decision tree-based algorithms.
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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 (December 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 search PubMed, Embase, the Cochrane Database of Systematic Reviews and the Database of Abstracts of Reviews of Effects from inception through 31 December 2021 with no language restriction and perform full-text evaluation of potentially relevant articles. We will include systematic reviews of randomised controlled trials or non-randomised studies of interventions that assessed a decision aid about cancer-screening decisions and compared it with an alternative tool or conventional management in healthy average-risk adults. Two reviewers will extract data and rate the study validity according to standard quality assessment measures. Our primary outcome will be intended and actual choice and adherence to selected options. The secondary outcomes will include attributes of the option-selection process, achieving shared decision-making and preference-linked psychosocial outcomes. We will qualitatively assess study, patient and intervention characteristics and outcomes. We will also take special care to investigate the presentation format, delivery methods and quality of the included decision aids and assess the degree to which the decision aid was delivered and used as intended. If appropriate, we will perform random-effects model meta-analyses to quantitatively synthesise the results.Ethics and disseminationEthics approval is not applicable as this is a secondary analysis of publicly available data. The review results will be submitted for publication in a peer-reviewed journal.Prospero registration numberCRD42021235957.
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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 (July 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 meta-model which allows managing different states of multidimensional data warehouse. The definition of reduced and unreduced multidimensional data warehouse schema can be carried out by instantiating the meta-model. Finally, they describe their experimental assessments and discuss their results. Evaluating their solution implies executing different queries in various contexts: unreduced single fact table, unreduced relational star schema, reduced star schema and reduced snowflake schema. The authors show that queries are more efficiently calculated within a reduced star schema.
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Young, Mike. "A meta model of change." Journal of Organizational Change Management 22, no. 5 (August 28, 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 (September 29, 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 optimization framework, integrates multi-objective meta-heuristic algorithms and entropy-weighted technique for order preference by similarity to an ideal solution (EW-TOPSIS). The optimization framework can comprehensively evaluate the performance of overall SSCN by EW-TOPSIS and guide the evolution process of algorithms. In this framework, decision-makers can obtain the feasible schemes calculated by meta-heuristics and determine the optimal one according to the performance value evaluated by EW-TOPSIS. This article combines three performance evaluation strategies with four meta-heuristic algorithms, namely, non-dominated Sorting Genetic Algorithm-II (NSGA-2), multi-objective differential evolutionary (MODE), multi-objective particle swarm optimization (MOPSO), and multi-objective gray wolr optimization (MOGWO), for verifying the effectiveness of the performance-oriented optimization framework. The results validate that the proposed framework has much better sustainability performance than the traditional optimization algorithms and evaluation methods. Furthermore, the proposed performance-oriented optimization framework can provide managers with a special optimal scheme with the best sustainability performance. Finally, some research prospects are presented such as more multi-criteria decision making methods.
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Kandala, Bhargava, Nele Plock, Akshita Chawla, Anna Largajolli, Seth Robey, Kenny Watson, Raj Thatavarti, 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 (June 30, 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 tree as a Meta classifier. Experimental results show that the proposed Grading method of classification offers accuracies of 81.3742%, 99.9159% and 99.8023% on testing, training datasets and cross validation respectively. It is found that the proposed graded classifier outperform its base classifiers and existing hybrid intrusion detection system in term of accuracy, false positive rate and model building time.
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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 (December 31, 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 (November 22, 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 shift days, and maximization of the overall satisfaction of nurses for their assigned shifts, are addressed in this model. Three meta-heuristics, namely, multi-objective Keshtel algorithm, non-dominated sorting genetic algorithm II, and multi-objective tabu search, are developed to solve the problem. Moreover, a data envelopment analysis method is employed to rank the obtained Pareto solutions. Afterwards, a real-life case at a large hospital in Tehran, Iran, is investigated. Eventually, the applicability and effectiveness of the proposed model are assessed based on the experimental results.
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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 (September 13, 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 algorithms were compared with each other. The data set contains 768 records and 8 dependent variables. This data set has 200 missing records, so the number of study records was reduced to 568 records.Results: The data were divided into two sets of training and testing by 10-Fold method. Then, all four algorithms of neural-genetic network, ant-neural colony network, neural network-particle Swarm optimization and neural network-cuckoo search on the data The trainings were performed and then evaluated by the test set. And the accuracy of 95.02 was obtained. Also, the final output of the algorithm was examined with two similar tasks and it was shown that the proposed model worked better.Conclusion: In this study showed that the combination of two neural network and genetic algorithms can provide a suitable predictive model for disease diagnosis.
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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 (February 15, 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 methods in solving the above model. So that the maximum percentage of relative differences between the methods mentioned with GAMS is less than 1%.Also, by solving the sample problems in larger sizes, it was observed that the league champions algorithm has the highest efficiency in terms of achieving the optimal value of the target function in a shorter time than the other algorithms used, with a useful weight of 0.998.
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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 (August 16, 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 meta-analysis was performed based on 41 SGA study’s findings, where the polymorphisms rs11549465 (1772 C/T) and rs11549467 (1790 G/A) of HIF1A gene were analyzed based on 11544 and 7426 cases and 11494 and 7063 control samples, respectively. Our results showed that the 1772 C/T polymorphism is not significantly associated with overall disease risks. The 1790 G/A polymorphism was significantly associated with overall diseases under recessive model (AA vs. AG + GG), which indicates that the A allele is responsible for overall diseases though it is recessive. The subgroup analysis based on ethnicity showed the significant association of 1772 C/T polymorphism with overall disease for Caucasian population under the all genetic models, which indicates that the C allele controls overall diseases. The ethnicity subgroup showed the significant association of 1790 G/A polymorphism with overall disease for Asian population under the recessive model (AA vs. AG + GG), which indicates that the A allele is responsible for overall diseases. The subgroup analysis based on disease types showed that 1772 C/T is significantly associated with chronic obstructive pulmonary disease (COPD) under two genetic models (C vs. T and CC vs. CT + TT), skin disease under two genetic models (CC vs. TT and CC + CT vs. TT), and diabetic complications under three genetic models (C vs. T, CT vs. TT and CC + CT vs. TT), where C allele is high risk factor for skin disease and diabetic complications (since, ORs > 1), but low risk factor for COPD (since, ORs < 1). Also the 1790 G/A variant significantly associated with the subgroup of cardiovascular disease (CVD) under homozygote model, diabetic complications under allelic and homozygote models, and other disease under four genetic models, where the A is high risk factor for diabetic complications and low risk factor for CVD. Thus, this study provided more evidence that the HIF1A gene is significantly associated with COPD, CVD, skin disease and diabetic complications. These might be the severe comorbidities and risk factors for multiple cancers due to the effect of HIF1A gene and need further investigations accumulating large number of studies.
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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 (December 19, 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, determine the "fitness" of the breeds in a meta-population that includes all breeds. As long as strong genotype-environment interaction for production traits is maintained by artificial selection, the economic self-interest of farmers should ensure the stability of genetic polymorphisms among breeds. Genetic variation would be conserved (in the among-breed component of genetic diversity) but not the primordial distribution of gene and genotype frequencies. Economic benefits to farmers, plus a high return on investment at the national or supra-national level, makes breed diversification an attractive conservation strategy even though it is admittedly a compromise from a purely genetic viewpoint.
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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 (July 23, 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 (September 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 whether, and to what extent, the varying conceptualizations of the distinctions between FE and RE models are reflected in published meta-analyses. The present study explores whether conceptualizations of model distinctions among psychological researchers are consistent with those in the methods literature. We also examine model choices and rationales given by psychological researchers in two samples of published meta-analyses in psychology-related journals. We identify four primary categories for distinguishing between FE and RE models, only two of which were predominant in our samples. Although model choice appears to be reported at a moderately high rate, many researchers continue not to provide explicit rationales for their model choices or do not clearly tie model choices to the specific research aims of the meta-analyses. Implications of these findings are discussed.
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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 (November 19, 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 such a multi-objective JSPM, Pareto-based optimization algorithms based on multi-objective meta-heuristics and multi-criteria decision making (MCDM) methods are utilized. Then, different comparison metrics (e.g., quality, mean ideal distance, and rate of achievement simultaneously) are used. Also, this paper includes two major phases to provide a new model of the FJSSP and introduce a new proposed HIA for solving the presented model, respectively. This algorithm is a hybrid genetic algorithm with the SAW/TOPSIS method, namely HGASAW/HGATOPSIS. The comparative results indicate that HGASAW and HGATOPSIS outperform the non-dominated sorting genetic algorithm (NSGA-II) to tackle the fuzzy multi-objective JSPM.
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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 (September 14, 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 dengan konsep meta-learner digunakan dalam penelitian ini. Terdapat lima tahapan utama metodologi, dimulai pengumpulan data saham dan berita, praproses data, ekstraksi fitur indikator teknikal dan sentimen serta analisis data, selanjutnya pengembangan model. Serangkaian uji coba parameter crossover dan mutasi GA memberi hasil optimum pencarian kombinatorik hyper-parameter model dengan accuracy 81.63% dan f1-score 82.21%. Evaluasi model terhadap kombinasi jenis dataset mampu meningkatkan accuracy prediksi dari 75.91% menajdi 81.63%, dan f1-score dari 77.56% menjadi 82.21%. Terhadap evaluasi trading, metode yang diusulkan terbukti memberi return yang fantastis sebesar 121.27% dalam setahun, dengan nilai maximum drawdown yang paling kecil juga nilai sharpe ratio yang tinggi. Evaluasi tersebut melampaui hasil penelitian serupa terdahulu, bahkan jauh diatas performa pergerakan saham itu sendiri terindikasi melalui strategi buy & hold
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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 (February 3, 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 meta-control systems are used for decision and meta-decision making. For this methodology, we present a real application for an Internet Service Provider. Finally, we give our personal aspects for Total Viable Development including Human Development.
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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 (December 31, 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 Destination). A total of 95,795,379 network packet headers from 96 subjects were successfully collected. Their meta-data header packets were statistically profiled to generate digital fingerprints that try to link their action on the network to their identity accurately. Hence, CHAID decision tree modelling using Destination IP, Unique protocols, and a combination of the two, including Port source and Port destination, resulted in an accuracy of 4.07%, 6.34%, and 6.36%, respectively. However, the modelling could not create a reliable decision tree for the Port source and destination. The validation study on all the combined variables had a similar accuracy of 6.36%, indicating model created had reproducibility capability. Despite the outcome, the proposed method is not yet sufficiently strong for suspect identification. Further enhancement to improve its accuracy is required.
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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 (December 31, 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 Destination). A total of 95,795,379 network packet headers from 96 subjects were successfully collected. Their meta-data header packets were statistically profiled to generate digital fingerprints that try to link their action on the network to their identity accurately. Hence, CHAID decision tree modelling using Destination IP, Unique protocols, and a combination of the two, including Port source and Port destination, resulted in an accuracy of 4.07%, 6.34%, and 6.36%, respectively. However, the modelling could not create a reliable decision tree for the Port source and destination. The validation study on all the combined variables had a similar accuracy of 6.36%, indicating model created had reproducibility capability. Despite the outcome, the proposed method is not yet sufficiently strong for suspect identification. Further enhancement to improve its accuracy is required.
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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 (November 8, 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 vehicles between every two hubs. The objectives of this model are to maximize the benefits of transportation by establishing hub facilities and to minimize the total transportation time. Since exact values of some parameters are not known in advance, a fuzzy multi-objective programming based approach is proposed to optimally solve small-sized problems. For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm. Our experimental results demonstrated the validity of our developed model and approaches. Moreover, an intensive sensitivity analyze study is carried out on a real-case application related to the monorail project of the holy city of Qom.
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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 (October 15, 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 algorithm for solving the model. This investigation showed that the network's overall transportation and operational expenses have risen as the rate of uncertainty and dependability has grown. MOGWO was chosen as an effective algorithm and employed in solving numerical examples of more significant size after the final examination of comparison indices between solution techniques (case study). According to the findings of a case study, the four oil businesses, Behran, Sepahan, Iranol, and Pars, were chosen as the best production hubs since they can generate 514 million liters of engine oil annually. As a consequence, building the network cost a total of 434321010 million Rials, required the employment of more than 37 thousand individuals, and left 90 million liters of fuel short.
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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 (November 28, 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. We used a meta-analysis and trial sequential analysis to determine whether ADAMTS5 rs226794 expression increases susceptibility to osteoarthritis. Methods: This study consisted of two parts: a case-control study and a meta-analysis. The case-control study included subjects who underwent knee radiography at the Health Examination Center of the Tri Service General Hospital from 2015 to 2019. The Kellgren–Lawrence (KL) grading system was used as diagnostic criteria. Patients with unsuccessful gene sequencing were excluded. There were 606 subjects in the knee osteoarthritis group (KL ≥ 2) and 564 in the control group (KL < 2). Gene sequencing was performed using iPLEX Gold to determine the association between the gene polymorphism of ADAMTS5 rs226794 and knee osteoarthritis. For the meta-analysis, databases such as PubMed, Embase, and Cochrane were queried to identify studies that examined the relationship between ADAMTS5 rs226794 and osteoarthritis. Next, the findings of the meta-analysis were incorporated with the results of the case-control study and samples from the published studies to estimate the association between the genetic polymorphism and osteoarthritis using an odds ratio and a 95% confidence interval. Results: We found a non-significant association between the G allele and knee OA (crude-OR: 0.93 (95% CI: 0.79–1.10) and adjusted-OR: 1.02 (95% CI: 0.76–1.36) in the allele model) in the present study, and the analysis of other genetic models revealed a similar trend. After including five published studies and our case-control study, the results with 2866 Asians indicated a conclusively null association between ADAMTS5 rs226794 and knee OA) OR: 1.09 (95% CI: 0.93–1.26). The results for Caucasians also revealed a null association (OR: 1.21 (95% CI: 0.81–1.82)). Conclusions: This study indicates that the gene polymorphism, ADAMTS5 rs226794, is not significantly associated with knee osteoarthritis. Additionally, assuming that the cumulative sample size in the allele model is sufficient, we confirmed that the G allele is not a risk factor for osteoarthritis. This study integrated all available evidence to arrive at this conclusion, and it suggests that no additional studies are necessary.
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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 (January 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, database) used in task analysis or the number of cues used in tasks were also considered. The findings indicated that research on judgment achievement should examine judgment tasks relating to specific research areas.
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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 minimizing overall costs and maximizing the amount of products ordered from different suppliers based on their weight value. Weights are calculated based on different criteria using the fuzzy analytic hierarchy process method for each supplier in different periods. Then, due to the multiobjective nature of the model, the proposed model has been solved by using the epsilon constraint in GAMS and nondominated sorting genetic algorithm II in MATLAB software. Considering the simultaneous order of inventory of multiproduct with several suppliers in several periods of time in discrete space with discount is one of the contributions of this research. To validate the proposed model, the results of the exact solution are compared with the meta-heuristic solution. Comparison results and assessment metrics indicate that the results of the proposed solution approach with an error of less than 1% had good performance. The results show that the system cost increases, by increasing the amount of discount, because of the increase in the amount of demand. Therefore, with a 30% increase in the discount, the system costs will increase to 36,496 units. Also, with a 20% reduction, the cost reduction will be reduced to 14,170 units.
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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 (January 11, 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 forecasting (e.g., anticipated regret, anticipated affect) as a means of changing health behaviors or decisions. We determined the overall effect of these interventions on targeted affective constructs and behavioral outcomes, and whether conceptual and methodological factors moderated these effects.ResultsA total of 133 independent effect sizes were identified from 37 publications (N = 72,020). Overall, affective forecasting interventions changed anticipated regret, d = 0.24, 95% confidence interval (CI) (0.15, 0.32), p < .001, behavior, d = 0.29, 95% CI (0.13, 0.45), p < .001, and behavioral intentions, d = 0.19, 95% CI (0.11, 0.28), p < .001, all measured immediately postintervention. Interventions did not change anticipated positive and negative affect, and effects on intentions and regret did not extend to follow-up time points, ps > .05. Generally, effects were not moderated by conceptual model, intervention intensity, or behavioral context.ConclusionsAffective forecasting interventions had a small consistent effect on behavioral outcomes regardless of intervention intensity and conceptual framework, suggesting such constructs are promising intervention targets across several health domains.
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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 (March 26, 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 detailed results of the list of journals used as the most common references from the journal Computers & Industrial Engineering are used to identify the results of this publication in more detail. The most common research model is the adaptive decision model, and the most common research methodology is quantitative. Advanced research with sophisticated applications from the latest technologies such as AI (Machine Learning) to (Deep Learning), this wider and varied use of data includes unstructured or unorganized data so that new concepts will lead to new decision model system innovations, still relatively little additional research that can be used in the realm of production in assembly, process quality, and the environment.
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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 (December 1, 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 feature importance to XGBoost with a weight value filter of 10. The ensemble learning method used is stacking. Stacking is composed of the base model and meta learner. The base model consists of K-nearest neighbor, decision tree, SVM, and random forest, while the meta learner used is LightGBM. The stacking model accuracy results can outperform the base model accuracy with an accuracy rate of 97%.</span>
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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 (December 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 employed, is proposed for solving the investigated integration problem in order to minimize the makespan of the manufacturing system. The experimental results on a set of random-generated test instances indicate that the presented model is effective and the proposed algorithm exhibits the satisfactory performance that outperforms two state-of-the-art algorithms from literature.
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He, Qingfeng, Zhihao Xu, Shaojun Li, Renwei Li, Shuai Zhang, Nianqin Wang, Binh Thai Pham, and Wei Chen. "Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling." Entropy 21, no. 2 (January 23, 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 correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.
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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 the data as data resources to manufacturing units at data warehouse access level. The foundation of our approach is a meta-data model which consists of data, concept, ontology and resource repositories. The model is used in a shipbuilding enterprise data warehouse development project. The result shows that with the guide of the meta-data model, our ontology approach could eliminate the data heterogeneity.
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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 research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively.
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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 (June 30, 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 were obtained, containing 43 studies to be analyzed. This meta-analysis uses a random effect model due to the heterogeneous data distribution. A publication bias test was carried out with the Fail-safe N model to ensure the quality of the data. The result of the analysis showed that the data distribution was heterogeneous, according to the I2 test, so selecting the random effect model was the right decision. Regarding publication bias, an accurate Fail-safe N test shows that the data are free from publication bias. Thus, the analysis uses a suitable model, and the results of the analysis can be trusted. The total effect size indicates a significant positive correlation (r = 0.295) between self-assessment and students' mathematics achievement. Therefore, the higher the self-assessment index, the higher one's mathematics learning achievement.