统计代写|运筹学作业代写operational research代考|MTH360

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

统计代写|运筹学作业代写operational research代考|Future and Summary of Work

This section discusses the future and summary of the proposed method:
i. Neutrosophic set theory is a new structure considering three independent membership functions to deal with the concept of incompleteness, uncertainty, and vagueness. The method of multi-criteria decision-making (MCDM) problem is an important key in the existence of multiple criteria and alternatives in solving sophisticated and complicated decision problems.
ii. This chapter derived a new neutrosophic method in multi-criteria decisionmaking problems to find the best alternatives in each and the entire regions nnder the nentrosophic envirnnment,
iii. This method considered neutrosophic sets with their unions and the complements with their intersections.
iv. The single-valued neutrosophic score functions are computed to find the best alternatives not only in each region but also in the entire region.
v. This chapter solved the plant hybridization problem as a real-life application of neutrosophic set theory to demonstrate the effectiveness of the proposed method.
vi. The present methodology may further be applied in content-based image retrieval (CBIR), dimensionality reduction in dimensional space, multimedia databases, manufacturing systems, personal selection in academia, project evaluation, and supply chain management.
vii. The proposed method can alternatively be used for other multi-criteria decision-making methods such as ELECTRE, DEMTEL, PROMOTEE, TOPSIS, and VIKOR methods.
viii. The techniques of this method may also be applied in fuzzy and intuitionistic fuzzy environments.

统计代写|运筹学作业代写operational research代考|Preliminaries

In this section we recall the basic definitions of QSVNS and IQNS which will be used in proving the rest of the paper.

Definition 2.1 [17] Let $\mathrm{X}$ be a non-empty set. A quadripartitioned single-valued neutrosophic set (QSVNS) $A$ over $X$ characterizes each element $x$ in $X$ by a truth membership function $T_{A}$, a contradiction membership function $C_{A}$, an ignorance membership function $U_{A}$, and a falsity membership function $F_{A}$ such that for each $x \in X, T_{A}, C_{A}, U_{A}, F_{A} \in[0,1]$ and $0 \leq T_{A}(x)+C_{A}(x)+U_{A}(x)+F_{A}(x) \leq 4$ when $X$ is discrete, $A$ is represented as $A=\sum_{i=1}^{n}\left\langle T_{A}\left(x_{i}\right), C_{A}\left(x_{i}\right), U_{A}\left(x_{i}\right), F_{A}\left(x_{i}\right)\right\rangle / x_{i}$, $x_{i} \in X$
However, when the universe of discourse is continuous, A is represented as
$$A=\int_{X}\left\langle T_{A}(x), C_{A}(x), U_{A}(x), F_{A}(x)\right\rangle / x, x \in X$$
Definition $2.2$ [17] Consider two QSVNSs $A$ and $B$, over $X . A$ is said to be contained in $B$, denoted by $A \subseteq B$ iff $T_{A}(x) \leq T_{B}(x), C_{A}(x) \leq C_{B}(x)$, $U_{A}(x) \geq U_{B}(x)$ and $F_{A}(x) \geq F_{B}(x)$.

Definition $2.3$ [17] The complement of a QSVNS A is denoted by $A^{C}$ and is defined as
$$\begin{gathered} A^{C}=\sum_{i=1}^{n}\left\langle F_{A}\left(x_{i}\right), U_{A}\left(x_{i}\right), C_{A}\left(x_{i}\right), T_{A}\left(x_{i}\right)\right\rangle / x_{i}, x_{i} \in X \ \text { i.e., } T_{A} c\left(x_{i}\right)=F_{A}\left(x_{i}\right), C_{A} c\left(x_{i}\right)=U_{A}\left(x_{i}\right), U_{A} C\left(x_{i}\right)=C_{A}\left(x_{i}\right) \text { and } F_{A} c\left(x_{i}\right) \ =T_{A}\left(x_{i}\right), x_{i} \in X \end{gathered}$$
Definition 2.4 [17] The union of two QSVNSs $A$ and $B$ is denoted by $A \cup B$ and is defined as
$$\begin{array}{r} A \cup B=\sum_{i=1}^{n}\left\langle T_{A}\left(x_{i}\right) \vee T_{B}\left(x_{i}\right), C_{A}\left(x_{i}\right) \vee C_{B}\left(x_{i}\right), U_{A}\left(x_{i}\right) \wedge U_{B}\left(x_{i}\right)\right. \ \left.F_{A}\left(x_{i}\right) \wedge F_{B}\left(x_{i}\right)\right\rangle / x_{i}, x_{i} \in X \end{array}$$

统计代写|运筹学作业代写operational research代考|Future and Summary of Work

i．中智集合论是一种新的结构，它考虑三个独立的隶属函数来处理不完备性、不确定性和模糊性的概念。多准则决策（MCDM）问题的方法是解决复杂和复杂决策问题存在多准则和备选方案的重要关键。
ii. 本章推导了一种新的多准则决策问题中智方法，以在中智环境下的每个和整个区域中找到最佳替代方案，
iii。该方法考虑了中智集及其并集和补集及其交集。
iv. 计算单值中智评分函数以不仅在每个区域而且在整个区域中找到最佳替代方案。
v. 本章将植物杂交问题作为中智集理论的实际应用解决，以证明所提出方法的有效性。

统计代写|运筹学作业代写operational research代考|Preliminaries

$x \in X, T_{A}, C_{A}, U_{A}, F_{A} \in[0,1]$ 和 $0 \leq T_{A}(x)+C_{A}(x)+U_{A}(x)+F_{A}(x) \leq 4$ 什么时候 $X$ 是离散的， $A$ 表示为 $A=\sum_{i=1}^{n}\left\langle T_{A}\left(x_{i}\right), C_{A}\left(x_{i}\right), U_{A}\left(x_{i}\right), F_{A}\left(x_{i}\right)\right\rangle / x_{i}, x_{i} \in X$

$$A=\int_{X}\left\langle T_{A}(x), C_{A}(x), U_{A}(x), F_{A}(x)\right\rangle / x, x \in X$$

$$A^{C}=\sum_{i=1}^{n}\left\langle F_{A}\left(x_{i}\right), U_{A}\left(x_{i}\right), C_{A}\left(x_{i}\right), T_{A}\left(x_{i}\right)\right\rangle / x_{i}, x_{i} \in X \text { i.e., } T_{A} c\left(x_{i}\right)=F_{A}\left(x_{i}\right), C_{A} c\left(x_{i}\right)=U_{A}$$

$$A \cup B=\sum_{i=1}^{n}\left\langle T_{A}\left(x_{i}\right) \vee T_{B}\left(x_{i}\right), C_{A}\left(x_{i}\right) \vee C_{B}\left(x_{i}\right), U_{A}\left(x_{i}\right) \wedge U_{B}\left(x_{i}\right) F_{A}\left(x_{i}\right) \wedge F_{B}\left(x_{i}\right)\right\rangle / x_{i}, x_{i}$$

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MATLAB代写

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