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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

## 电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|Naive Bayes Classifier

This is a popular technique for carrying out classification with a postulation of absolute independence among various predictors. Naive Bayes is known to perform excellently well when compared with other prominent classification types. Bayes theorem gives a means of determining the $P(c \mid x)$ from $P(x), P(c)$ and $P(x \mid c)$-as depicted in Eq. 2. $P(x \mid c)$ can be defined as the possibility which is considered as the probability of predictor given class. $P(c)$ can also be defined as prior probability of class. $P(x)$ is defined as prior probability of predictor.

Figure 1 provides the step by step way of carrying the experimentation hefore arriving at the results obtained. The moment the URL is entered, a comparative assessment is observed with trusted domain list to ascertain if it is phishing or not. If there is no concrete assurance regarding the status of the URL, features are extracted for detection purpose after which training and classification will be carried out on the suspicious URL. If URL is considered as phishing, the whole process will stop.
\begin{aligned} p(c \mid x) &=\frac{p(x \mid c) p(c)}{p(x)} \ p(c \mid x) &=p\left(x_{1} \mid c\right) * p\left(x_{2} \mid c\right) * \ldots * p\left(x_{n} \mid c\right) * p(c) \end{aligned}
where each of the parameters is defined as follows:
$P(c \mid x)$ can be defined as posterior probability of class ( $c$, target) given predictor $(x$, attributes).

Fashion recommender systems have been proposed for two categories of users namely: the fashion designers as well as the consumers.

Tu and Dong in [11] proposed an intelligent personalised fashion recommendation system. The framework comprises of three distinct models namely: “interaction and recommender model, evolutionary hierarchical fashion multimedia mining model and colour tone analysis model which work together to give recommendations”. Style, favourite colour and skin colour were considered as the personalised index when recommending clothing matching.

Vogiatzis et al. [12] proposed a personalised clothing recommendation system for users that combines, “knowledge derived from fashion experts with the preferences of users towards garments”. The knowledge gathered from the experts is encoded as ontology.

Zeng et al. [13] proposed a “perception-based fashion recommender system for supporting fashion designers in selecting the best-personalised fashion design scheme”. It comprises of two distinct models, which work together to give recommendations. The two models characterise the relation between human body measurements and human perceptions on human body shapes.

Liu et al. [14] introduces a fashion recommendation system comprising of two subsystems. The first is the magic closet, which is an occasion-oriented clothing recommendation system while the second is Beauty E-expert for facial hairstyle and makeup recommendations. The system employs a latent Support Vector Machinebased recommendation model.

Ajmani et al. [15] presented a “method for content-based recommendation of media-rich commodities using probabilistic multimedia ontology. The ontology encodes subjective knowledge of experts that enables interpretation of media based and semantic product features in context of domain concepts. As a result, the recommendation is based on the semantic compatibility between the products and user profile in context of use. The architecture is extensible to other domains of mediarich products, where selection is primarily guided by the aesthetics of the media contents”.

Wakita et al. [16] proposed “a fashion-brand recommendation method that is based on both fashion features and fashion association rules”. The study was aimed at improving the accuracy of recommendation in Web services that sell fashion clothes.

# 数据管理和数据系统代考

## 电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|Naive Bayes Classifier

$$p(c \mid x)=\frac{p(x \mid c) p(c)}{p(x)} p(c \mid x) \quad=p\left(x_{1} \mid c\right) * p\left(x_{2} \mid c\right) * \ldots * p\left(x_{n} \mid c\right) * p(c)$$

$P(c \mid x)$ 可以定义为类的后验概率 $(c$, 目标) 给定的预测器 $(x ，$ 属性) 。

Tu和Dong在[11]中提出了一种智能的个性化时尚推荐系统。该框架包括三个不同的模型，即：“交互和推荐模型、进化分层时尚多媒体挖掘模型和色调分析模型，它们共同提供推荐”。在推荐服装搭配时，将款式、喜欢的颜色和肤色作为个性化指标。

Vogiatzis 等人。[12] 为用户提出了一个个性化的服装推荐系统，该系统将“来自时尚专家的知识与用户对服装的偏好相结合”。从专家那里收集的知识被编码为本体。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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