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      Economic Order Quantity Model-Based Optimized Fuzzy Nonlinear Dynamic Mathematical Schemes


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          Fuzzy mathematics-informed methods are beneficial in cases when observations display uncertainty and volatility since it is of vital importance to make predictions about the future considering the stages of interpreting, planning, and strategy building. It is possible to realize this aim through accurate, reliable, and realistic data and information analysis, emerging from past to present time. The principal expenditures are treated as fuzzy numbers in this article, which includes a blurry categorial prototype with pattern-diverse stipulation and collapse with salvation worth. Multiple parameters such as a shortage, ordering, and degrading cost are not fixed in nature due to uncertainty in the marketplace. Obtaining an accurate estimate of such expenditures is challenging. Accordingly, in this research, we develop an adaptive and integrative economic order quantity model with a fuzzy method and present an appropriate structure to manage such uncertain parameters, boosting the inventory system's exactness, and computing efficiency. The major goal of the study was to assess a set of changes to the company current inventory processes that allowed an achievement in its inventory costs optimization and system development in optimizing inventory costs for better control and monitoring. The approach of graded mean integration is used to determine the most efficient actual solution. The evidence-based model is illustrated with the help of appropriate numerical and sensitivity analysis through the related visual graphical depictions. The proposed method in our study aims at investigating the economic order quantity (EOQ), as the optimal order quantity, which is significant in inventory management to minimize the total costs related to ordering, receiving, and holding inventory in the dynamic domains with nonlinear features of the complex dynamic and nonlinear systems as well as structures.

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          Most cited references20

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          Simultaneous feature selection and clustering using mixture models.

          Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is difficult because, unlike in supervised learning, there are no class labels for the data and, thus, no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue. In this paper, we propose the concept of feature saliency and introduce an expectation-maximization (EM) algorithm to estimate it, in the context of mixture-based clustering. Due to the introduction of a minimum message length model selection criterion, the saliency of irrelevant features is driven toward zero, which corresponds to performing feature selection. The criterion and algorithm are then extended to simultaneously estimate the feature saliencies and the number of clusters.
            • Record: found
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            Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy

              • Record: found
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              LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals

              Markerless 3D pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D pose by multi-view triangulation of deep network-based 2D pose estimates. However, triangulation requires multiple, synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3D’s versatility by applying it to multiple experimental systems using flies, mice, rats, and macaque monkeys and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotyped and non-stereotyped behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays, tedious calibration procedures, and despite occluded body parts in freely behaving animals.

                Author and article information

                Comput Intell Neurosci
                Comput Intell Neurosci
                Computational Intelligence and Neuroscience
                15 July 2022
                : 2022
                : 3881265
                1PG and Research Department of Mathematics, Cauvery College for Women (Affiliated to Bharathidasan University), Tiruchirappalli 620018, Tamil Nadu, India
                2Department of Mathematics, Yildiz Technical University, Faculty of Arts and Science, Esenler 34210, Istanbul, Turkey
                3Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan
                4University of Massachusetts Medical School (UMASS), Worcester, MA 01655, USA
                5Department of Logistics, University of Defence in Belgrade, Belgrade, Serbia
                6College of Education, Applied Sciences and Arts, Amran University, Amran, Yemen
                Author notes

                Academic Editor: Dalin Zhang

                Author information
                Copyright © 2022 Kalaiarasi Kalaichelvan et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 25 May 2022
                : 13 June 2022
                : 17 June 2022
                Research Article



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