## 计算机代写|神经网络代写neural networks代考|NIT6004

statistics-lab™ 为您的留学生涯保驾护航 在代写神经网络neural networks方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写神经网络neural networks代写方面经验极为丰富，各种代写神经网络neural networks相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|神经网络代写neural networks代考|Traditional Graph Embedding

Traditional graph embedding methods are originally studied as dimension reduction techniques. A graph is usually constructed from a feature represented data set, like image data set. As mentioned before, graph embedding usually has two goals, i.e. reconstructing original graph structures and support graph inference. The objective functions of traditional graph embedding methods mainly target the goal of graph reconstruction.

Specifically, Tenenbaum et al (2000) first constructs a neighborhood graph $G$ using connectivity algorithms such as $K$ nearest neighbors (KNN). Then based on $G$, the shortest path between different data can be computed. Consequently, for all the $N$ data entries in the data set, we have the matrix of graph distances. Finally, the classical multidimensional scaling (MDS) method is applied to the matrix to obtain the coordinate vectors. The representations learned by Isomap approximately preserve the geodesic distances of the entry pairs in the low-dimensional space. The key problem of Isomap is its high complexity due to the computing of pair-wise shortest pathes. Locally linear embedding (LLE) (Roweis and Saul, 2000) is proposed to eliminate the need to estimate the pairwise distances between widely separated entries. LLE assumes that each entry and its neighbors lie on or close to a locally linear patch of a mainfold. To characterize the local geometry, each entry can be reconstructed from its neighbors. Finally, in the low-dimensional space, LLE constructs a neighborhood-preserving mapping based on locally linear reconstruction. Laplacian eigenmaps (LE) (Belkin and Niyogi, 2002) also begins with constructing a graph using $\varepsilon$-neighborhoods or $\mathrm{K}$ nearest neighbors. Then the heat kernel (Berline et al, 2003) is utilized to choose the weight of two nodes in the graph. F1nally, the node representations can be obtained by based on the Laplacian matrix regularization. Furthermore, the locality preserving projection (LPP) (Berline et al, 2003), a linear approximation of the nonlinear LE, is proposed.

## 计算机代写|神经网络代写neural networks代考|Structure Preserving Graph Representation Learning

Graph structures can be categorized into different groups that present at different granularities. The commonly exploited graph structures in graph representation learning include neighborhood structure, high-order node proximity and graph communities.

How to define the neighborhood structure in a graph is the first challenge. Based on the discovery that the distribution of nodes appearing in short random walks is similar to the distribution of words in natural language, DeepWalk (Perozzi et al, 2014) employs the random walks to capture the neighborhood structure. Then for each walk sequence generated by random walks, following Skip-Gram, DeepWalk aims to maximize the probability of the neighbors of a node in a walk sequence. Node2vec defines a flexible notion of a node’s graph neighborhood and designs a second order random walks strategy to sample the neighborhood nodes, which can smoothly interpolate between breadth-first sampling (BFS) and depth-first sampling (DFS). Besides the neighborhood structure, LINE (Tang et al, 2015b) is proposed for large scale network embedding. which can preserve the first and second order proximities. The first order proximity is the observed pairwise proximity between two nodes. The second order proximity is determined by the similarity of the “contexts” (neighbors) of two nodes. Both are important in measuring the relationships beetween two nodess. Essentially, LINE is based on the shallow model, consequently, the representation ability is limited. SDNE (Wang et al, 2016) proposes a deep model for network embedding, which also aims at capturing the first and second order proximites. SDNE uses the deep auto-encoder architecture with multiple non-linear layers to preserve the second order proximity. To preserve the first-order proximity, the idea of Laplacian eigenmaps (Belkin and Niyogi, 2002) is adopted. Wang et al (2017g) propose a modularized nonnegative matrix factorization (M-NMF) model for graph representation learning, which aims to preserve both the microscopic structure, i.e., the first-order and second-order proximities of nodes, and the mesoscopic community structure (Girvan and Newman, 2002).

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|神经网络代写neural networks代考|STAT3007

statistics-lab™ 为您的留学生涯保驾护航 在代写神经网络neural networks方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写神经网络neural networks代写方面经验极为丰富，各种代写神经网络neural networks相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|神经网络代写neural networks代考|Representation Learning for Networks

Beyond popular data like images, texts, and sounds, network data is another important data type that is becoming ubiquitous across a large scale of real-world applications ranging from cyber-networks (e.g., social networks, citation networks, telecommunication networks, etc.) to physical networks (e.g., transportation networks, biological networks, etc). Networks data can be formulated as graphs mathematically, where vertices and their relationships jointly characterize the network information. Networks and graphs are very powerful and flexible data formulation such that sometimes we could even consider other data types like images, and texts as special cases of it. For example, images can be considered as grids of nodes with RGB attributes which are special types of graphs, while texts can also be organized into sequential-, tree-, or graph-structured information. So in general, representation learning for networks is widely considered as a promising yet more challenging tasks that require the advancement and generalization of many techniques we developed for images, texts, and so forth. In addition to the intrinsic high complexity of network data, the efficiency of representation learning on networks is also an important issues considering the large-scale of many real-world networks, ranging from hundreds to millions or even billions of vertices. Analyzing information networks plays a crucial role in a variety of emerging applications across many disciplines. For example, in social networks, classifying users into meaningful social groups is useful for many important tasks, such as user search, targeted advertising and recommendations; in communication networks, detecting community structures can help better understand the rumor spreading process; in biological networks, inferring interactions between proteins can facilitate new treatments for diseases. Nevertheless, efficient and effective analysis of these networks heavily relies on good representations of the networks.

## 计算机代写|神经网络代写neural networks代考|Graph Representation Learning: An Introduction

Many complex systems take the form of graphs, such as social networks, biological networks, and information networks. It is well recognized that graph data is often sophisticated and thus is challenging to deal with. To process graph data effectively, the first critical challenge is to find effective graph data representation, that is, how to represent graphs concisely so that advanced analytic tasks, such as pattern discovery, analysis, and prediction, can be conducted efficiently in both time and space.

Traditionally, we usually represent a graph as $\mathscr{G}=(\mathscr{V}, \mathscr{E})$, where $\mathscr{V}$ is a node set and $\mathscr{E}$ is an edge set. For large graphs, such as those with billions of nodes, the traditional graph representation poses several challenges to graph processing and analysis.
(1) High computational complexity. These relationships encoded by the edge set $E$ take most of the graph processing or analysis algorithms either iterative or combinatorial computation steps. For example, a popular way is to use the shortest or average path length between two nodes to represent their distance. To compute such a distance using the traditional graph representation, we have to enumerate many possible paths between two nodes, which is in nature a combinatorial problem. Such methods result in high computational complexity that prevents them from being applicable to large-scale real-world graphs.
(2) Low parallelizability. Parallel and distributed computing is de facto to process and analyze large-scale data. Graph data represented in the traditional way, however, casts severe difficulties to design and implementat of parallel and distributed algorithms. The bottleneck is that nodes in a graph are coupled to each other explicitly reflected by $E$. Thus, distributing different nodes in different shards or servers often causes demandingly high communication cost among servers, and holds back speed-up ratio.

## 计算机代写|神经网络代写neural networks代考|Graph Representation Learning: An Introduction

(1) 计算复杂度高。这些由边集编码的关系和采用大多数图形处理或分析算法迭代或组合计算步骤。例如，一种流行的方法是使用两个节点之间的最短或平均路径长度来表示它们的距离。为了使用传统的图形表示来计算这样的距离，我们必须枚举两个节点之间的许多可能路径，这本质上是一个组合问题。这样的方法导致高计算复杂性，从而阻止它们适用于大规模的真实世界图。
(2) 并行性低。并行和分布式计算实际上是处理和分析大规模数据。然而，以传统方式表示的图形数据给并行和分布式算法的设计和实现带来了严重的困难。瓶颈是图中的节点相互耦合，显式反映为和. 因此，将不同的节点分布在不同的分片或服务器中往往会导致服务器之间的通信成本很高，并且会阻碍加速比。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|神经网络代写neural networks代考|COMP5329

statistics-lab™ 为您的留学生涯保驾护航 在代写神经网络neural networks方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写神经网络neural networks代写方面经验极为丰富，各种代写神经网络neural networks相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|神经网络代写neural networks代考|Representation Learning for Speech Recognition

Nowadays, speech interfaces or systems have become widely developed and integrated into various real-life applications and devices. Services like Siri ${ }^{1}$, Cortana ${ }^{2}$, and Google Voice Search ${ }^{3}$ have become a part of our daily life and are used by millions of users. The exploration in speech recognition and analysis has always been motivated by a desire to enable machines to participate in verbal human-machine interactions. The research goals of enabling machines to understand human speech, identify speakers, and detect human emotion have attracted researchers’ attention for more than sixty years across several distinct research areas, including but not limited to Automatic Speech Recognition (ASR), Speaker Recognition (SR), and Speaker Emotion Recognition (SER).

Analyzing and processing speech has been a key application of machine learning (ML) algorithms. Research on speech recognition has traditionally considered the task of designing hand-crafted acoustic features as a separate distinct problem from the task of designing efficient models to accomplish prediction and classification decisions. There are two main drawbacks of this approach: First, the feature engineering is cumbersome and requires human knowledge as introduced above; and second, the designed features might not be the best for the specific speech recognition tasks at hand. This has motivated the adoption of recent trends in the speech community towards the utilization of representation learning techniques, which can learn an intermediate representation of the input signal automatically that better fits into the task at hand and hence lead to improved performance. Among all these successes, deep learning-based speech representations play an important role. One of the major reasons for the utilization of representation learning techniques in speech technology is that speech data is fundamentally different from two-dimensional image data. Images can be analyzed as a whole or in patches, but speech has to be formatted sequentially to capture temporal dependency and patterns.

## 计算机代写|神经网络代写neural networks代考|Representation Learning for Natural Language Processing

Besides speech recognition, there are many other Natural Language Processing (NLP) applications of representation learning, such as the text representation learning. For example, Google’s image search exploits huge quantities of data to map images and queries in the same space (Weston et al, 2010) based on NLP techniques. In general, there are two types of applications of representation learning in $\mathrm{NLP}$. In one type, the semantic representation, such as the word embedding, is trained in a pre-training task (or directly designed by human experts) and is transferred to the model for the target task. It is trained by using language modeling objective and is taken as inputs for other down-stream NLP models. In the other type, the semantic representation lies within the hidden states of the deep learning model and directly aims for better performance of the target tasks in an end-to-end fashion. For example, many NLP tasks want to semantically compose sentence or document representation, such as tasks like sentiment classification, natural language inference, and relation extraction, which require sentence representation.

Conventional NLP tasks heavily rely on feature engineering, which requires careful design and considerable expertise. Recently, representation learning, especially deep learning-based representation learning is emerging as the most important technique for NLP. First, NLP is typically concerned with multiple levels of language entries, including but not limited to characters, words, phrases, sentences, paragraphs, and documents. Representation learning is able to represent the semantics of these multi-level language entries in a unified semantic space, and model complex semantic dependence among these language entries. Second, there are various NLP tasks that can be conducted on the same input. For example, given a sentence, we can perform multiple tasks such as word segmentation, named entity recognition, relation extraction, co-reference linking, and machine translation. In this case, it will be more efficient and robust to build a unified representation space of inputs for multiple tasks. Last, natural language texts may be collected from multiple domains, including but not limited to news articles, scientific articles, literary works, advertisement and online user-generated content such as product reviews and social media. Moreover, texts can also be collected from different languages, such as English, Chinese, Spanish, Japanese, etc. Compared to conventional NLP systems which have to design specific feature extraction algorithms for each domain according to its characteristics, representation learning enables us to build representations automatically from large-scale domain data and even add bridges among these languages from different domains. Given these advantages of representation learning for NLP in the feature engineering reduction and performance improvement, many researchers have developed efficient algorithms on representation learning, especially deep learning-based approaches, for NLP.

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|深度学习代写deep learning代考|STAT3007

statistics-lab™ 为您的留学生涯保驾护航 在代写深度学习deep learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写深度学习deep learning代写方面经验极为丰富，各种代写深度学习deep learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|深度学习代写deep learning代考|Subdifferentials

The directional derivative of $f$ at $\boldsymbol{x} \in \operatorname{dom} f$ in the direction of $\boldsymbol{y} \in \mathcal{H}$ is defined by
$$f^{\prime}(x ; y)=\lim _{\alpha \downarrow 0} \frac{f(x+\alpha y)-f(x)}{\alpha}$$ if the limit exists. If the limit exists for all $y \in \mathcal{H}$, then one says that $f$ is Gãteaux differentiable at $\boldsymbol{x}$. Suppose $f^{\prime}(\boldsymbol{x} ; \cdot)$ is linear and continuous on $\mathcal{H}$. Then, there exist a unique gradient vector $\nabla f(\boldsymbol{x}) \in \mathcal{H}$ such that
$$f^{\prime}(\boldsymbol{x} ; \boldsymbol{y})=\langle\boldsymbol{y}, \nabla f(\boldsymbol{x})\rangle, \quad \forall \boldsymbol{y} \in \mathcal{H}$$
If a function is differentiable, the convexity of a function can easily be checked using the first- and second-order differentiability, as stated in the following:

Proposition $1.1$ Let $f: \mathcal{H} \mapsto(-\infty, \infty]$ be proper. Suppose that $\operatorname{dom} f$ is open and convex, and $f$ is Gâteux differentiable on $\operatorname{dom} f$. Then, the followings are equivalent:

1. $f$ is convex.
2. (First-order): $f(\boldsymbol{y}) \geq f(\boldsymbol{x})+\langle\boldsymbol{y}-\boldsymbol{x}, \nabla f(\boldsymbol{x})\rangle, \quad \forall \boldsymbol{x}, \boldsymbol{y} \in \mathcal{H}$.
3. (Monotonicity of gradient): $\langle\boldsymbol{y}-\boldsymbol{x}, \nabla f(\boldsymbol{y})-\nabla f(\boldsymbol{x})\rangle \geq 0, \quad \forall \boldsymbol{x}, \boldsymbol{y} \in \mathcal{H}$.
If the convergence in (1.48) is uniform with respect to $\boldsymbol{y}$ on bounded sets, i.e.
$$\lim _{\boldsymbol{0} \neq \boldsymbol{y} \rightarrow \mathbf{0}} \frac{f(\boldsymbol{x}+\boldsymbol{y})-f(\boldsymbol{x})-\langle\boldsymbol{y}, \nabla f(\boldsymbol{x})\rangle}{|\boldsymbol{y}|}=0$$

## 计算机代写|深度学习代写deep learning代考|Linear and Kernel Classifiers

Classification is one of the most basic tasks in machine learning. In computer vision, an image classifier is designed to classify input images in corresponding categories. Although this task appears trivial to humans, there are considerable challenges with regard to automated classification by computer algorithms.

For example, let us think about recognizing “dog” images. One of the first technical issues here is that a dog image is usually taken in the form of a digital format such as JPEG, PNG, etc. Aside from the compression scheme used in the digital format, the image is basically just a collection of numbers on a twodimensional grid, which takes integer values from 0 to 255 . Therefore, a computer algorithm should read the numbers to decide whether such a collection of numbers corresponds to a high-level concept of “dog”. However, if the viewpoint is changed, the composition of the numbers in the array is totally changed, which poses additional challenges to the computer program. To make matters worse, in a natural setting a dog is rarely found on a white background; rather, the dog plays on the lawn or takes a nap in the living room, hides underneath furniture or chews with her eyes closed, which makes the distribution of the numbers very different depending on the situation. Additional technical challenges in computer-based recognition of a dog come from all kinds of sources such as different illumination conditions, different poses, occlusion, intra-class variation, etc., as shown in Fig. 2.1. Therefore, designing a classifier that is robust to such variations was one of the important topics in computer vision literature for several decades.

In fact, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [7] was initiated to evaluate various computer algorithms for image classification at large scale. ImageNet is a large visual database designed for use in visual object recognition software research [8]. Over 14 million images have been hand-annotated in the project to indicate which objects are depicted, and at least one million of the images also have bounding boxes. In particular, ImageNet contains more than 20,000 categories made up of several hundred images. Since 2010, the ImageNet project has organized an annual software competition, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), in which software programs compete for the correct classification and recognition of objects and scenes. The main motivation is to allow researchers to compare progress in classification across a wider variety of objects. Since the introduction of AlexNet in 2012 [9], which was the first deep learning approach to win the ImageNet Challenge, the state-of-the art image classification methods are all deep learning approaches, and now their performance even surpasses human observers.

## 计算机代写|深度学习代写deep learning代考|Subdifferentials

$$f^{\prime}(x ; y)=\lim _{\alpha \downarrow 0} \frac{f(x+\alpha y)-f(x)}{\alpha}$$

$$f^{\prime}(\boldsymbol{x} ; \boldsymbol{y})=\langle\boldsymbol{y}, \nabla f(\boldsymbol{x})\rangle, \quad \forall \boldsymbol{y} \in \mathcal{H}$$

1. $f$ 是凸的。
2. (第一个订单) : $f(\boldsymbol{y}) \geq f(\boldsymbol{x})+\langle\boldsymbol{y}-\boldsymbol{x}, \nabla f(\boldsymbol{x})\rangle, \quad \forall \boldsymbol{x}, \boldsymbol{y} \in \mathcal{H}$.
3. (梯度的单调性) : $\langle\boldsymbol{y}-\boldsymbol{x}, \nabla f(\boldsymbol{y})-\nabla f(\boldsymbol{x})\rangle \geq 0, \quad \forall \boldsymbol{x}, \boldsymbol{y} \in \mathcal{H}$. 如果 (1.48) 中的收敛是一致的 $\boldsymbol{y}$ 在有界集上，即
$$\lim _{\boldsymbol{0} \neq \boldsymbol{y} \rightarrow 0} \frac{f(\boldsymbol{x}+\boldsymbol{y})-f(\boldsymbol{x})-\langle\boldsymbol{y}, \nabla f(\boldsymbol{x})\rangle}{|\boldsymbol{y}|}=0$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|深度学习代写deep learning代考|COMP5329

statistics-lab™ 为您的留学生涯保驾护航 在代写深度学习deep learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写深度学习deep learning代写方面经验极为丰富，各种代写深度学习deep learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|深度学习代写deep learning代考|Some Definitions

Let $\mathcal{X}, \mathcal{Y}$ and $Z$ be non-empty sets. The identity operator on $\mathcal{H}$ is denoted by $I$, i.e. $I x=x, \forall x \in \mathcal{H}$. Let $\mathcal{D} \subset \mathcal{H}$ be a non-emply sel. The set of the fixed points of an operator $\mathcal{T}: D \mapsto D$ is denoted by
$$\operatorname{Fix} \mathcal{T}={x \in \mathcal{D} \mid \mathcal{T} x=x}$$
Let $\mathcal{X}$ and $\mathcal{Y}$ be real normed vector space. As a special case of an operator, we define a set of linear operators:
$$\mathcal{B}(\mathcal{X}, \mathcal{Y})={\mathcal{T}: \mathcal{Y} \mapsto \mathcal{Y} \mid \mathcal{T} \text { is linear and continuous }}$$
and we write $\mathcal{B}(\mathcal{X})=\mathcal{B}(\mathcal{X}, \mathcal{X})$. Let $f: \mathcal{X} \mapsto[-\infty, \infty]$ be a function. The domain of $f$ is
$$\operatorname{dom} f={\boldsymbol{x} \in \mathcal{X} \mid f(\boldsymbol{x})<\infty}$$
the graph of $f$ is
$$\operatorname{gra} f={(\boldsymbol{x}, y) \in \mathcal{X} \times \mathbb{R} \mid f(\boldsymbol{x})=y},$$
and the epigraph of $f$ is
$$\text { eنi } f={(x, y) . x \in X, y \in \mathbb{R}, y \geq f(x)} \text {. }$$

## 计算机代写|深度学习代写deep learning代考|Convex Sets, Convex Functions

A function $f(\boldsymbol{x})$ is a convex function if $\operatorname{dom} f$ is a convex set and
$$f\left(\theta \boldsymbol{x}{1}+(1-\theta) \boldsymbol{x}{2}\right) \leq \theta f\left(\boldsymbol{x}{1}\right)+(1-\theta) f\left(\boldsymbol{x}{1}\right)$$
for all $x_{1}, x_{2} \in \operatorname{dom} f, 0 \leq \theta \leq 1$. A convex set is a set that contains every line segment between any two points in the set (see Fig. 1.3). Specifically, a set $C$ is convex if $\boldsymbol{x}{1}, \boldsymbol{x}{2} \in \mathcal{C}^{\prime}$, then $\theta \boldsymbol{x}{1}+(1-\theta) \boldsymbol{x}{2} \in \mathcal{C}$ for all $0 \leq \theta \leq 1$. The relation between a convex function and a convex set can also be stated using its epigraph. Specifically, a function $f(x)$ is convex if and only if its epigraph epi $f$ is a convex set.

Convexity is preserved under various operations. For example, if $\left{f_{i}\right}_{i \in I}$ is a family of convex functions, then, $\sup {i \in I} f{i}$ is convex. In addition, a set of convex functions is closed under addition and multiplication by strictly positive real numbers. Moreover, the limit point of a convergent sequence of convex functions is also convex. Important examples of convex functions are summarized in Table $1.1$.

## 计算机代写|深度学习代写deep learning代考|Some Definitions

$\operatorname{Fix} \mathcal{T}=x \in \mathcal{D} \mid \mathcal{T} x=x$

$\mathcal{B}(\mathcal{X}, \mathcal{Y})=\mathcal{T}: \mathcal{Y} \mapsto \mathcal{Y} \mid \mathcal{T}$ is linear and continuous

$$\operatorname{dom} f=\boldsymbol{x} \in \mathcal{X} \mid f(\boldsymbol{x})<\infty$$

$$\operatorname{gra} f=(\boldsymbol{x}, y) \in \mathcal{X} \times \mathbb{R} \mid f(\boldsymbol{x})=y,$$

$$\text { eui } f=(x, y) . x \in X, y \in \mathbb{R}, y \geq f(x)$$

## 计算机代写|深度学习代写deep learning代考|Convex Sets, Convex Functions

$$f(\theta \boldsymbol{x} 1+(1-\theta) \boldsymbol{x} 2) \leq \theta f(\boldsymbol{x} 1)+(1-\theta) f(\boldsymbol{x} 1)$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|深度学习代写deep learning代考|COMP30027

statistics-lab™ 为您的留学生涯保驾护航 在代写深度学习deep learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写深度学习deep learning代写方面经验极为丰富，各种代写深度学习deep learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|深度学习代写deep learning代考|Metric Space

A metric space $(\mathcal{X}, d)$ is a set $\chi$ together with a metric $d$ on the set. Here, a metric is a function that defines a concept of distance between any two members of the set, which is formally defined as follows.

Definition 1.1 (Metric) A metric on a set $X$ is a function called the distance $d$ : $\mathcal{X} \times \mathcal{X} \mapsto \mathbb{R}{+}$, where $\mathbb{R}{+}$is the set of non-negative real numbers. For all $x, y, z \in \mathcal{X}$, this function is required to satisfy the following conditions:

1. $d(x, y) \geq 0$ (non-negativity).
2. $d(x, y)=0$ if and only if $x=y$.
3. $d(x, y)=d(y, x)$ (symmetry).
4. $d(x, z) \leq d(x, y)+d(y, z)$ (triangle inequality).
A metric on a space induces topological properties like open and closed sets, which lead to the study of more abstract topological spaces. Specifically, about any point $x$ in a metric space $\mathcal{X}$, we define the open ball of radius $r>0$ about $x$ as the set
$$B_{r}(x)={y \in \mathcal{X}: d(x, y)0 such that B_{r}(x) is contained in U. The complement of an open set is called closed. ## 计算机代写|深度学习代写deep learning代考|Banach and Hilbert Space An inner product space is defined as a vector space that is equipped with an inner product. A normed space is a vector space on which a norm is defined. An inner product space is always a normed space since we can define a norm as |f|= \sqrt{\langle\boldsymbol{f}, \boldsymbol{f}\rangle}, which is often called the induced norm. Among the various forms of the normed space, one of the most useful normed spaces is the Banach space. Definition 1.7 The Banach space is a complete normed space. Here, the “completeness” is especially important from the optimization perspective, since most optimization algorithms are implemented in an iterative manner so that the final solution of the iterative method should belong to the underlying space \mathcal{H}. Recall that the convergence property is a property of a metric space. Therefore, the Banach space can be regarded as a vector space equipped with desirable properties of a metric space. Similarly, we can define the Hilbert space. Definition 1.8 The Hilbert space is a complete inner product space. We can easily see that the Hilbert space is also a Banach space thanks to the induced norm. The inclusion relationship between vector spaces, normed spaces, inner product spaces, Banach spaces and Hilbert spaces is illustrated in Fig. 1.1. As shown in Fig. 1.1, the Hilbert space has many nice mathematical structures such as inner product, norm, completeness, etc., so it is widely used in the machine learning literature. The following are well-known examples of Hilbert spaces: • l^{2}(\mathbb{Z}) : a function space composed of square summable discrete-time signals, i.e.$$
l^{2}(\mathbb{Z})=\left{x=\left.\left{x_{l}\right}_{l=-\infty}^{\infty}\left|\sum_{l=-\infty}^{\infty}\right| x_{l}\right|^{2}<\infty\right} .
$$## 深度学习代写 ## 计算机代写|深度学习代写deep learning代考|Metric Space 度量空间 (\mathcal{X}, d) 是一个集合 \chi 连同一个指标 d 在片场。这里，度量是定义集合中任意两个成员之间距离概念的函 数，其正式定义如下。 定义 1.1 (度量) 集合上的度量 X 是一个叫做距离的函数 d: \mathcal{X} \times \mathcal{X} \mapsto \mathbb{R}+ ，在哪里 \mathbb{R}+ 是一组非负实数。对所 有人 x, y, z \in \mathcal{X} ，该函数需要满足以下条件: 1. d(x, y) \geq 0 (非消极性) 。 2. d(x, y)=0 当且仅当 x=y. 3. d(x, y)=d(y, x) (对称)。 4. d(x, z) \leq d(x, y)+d(y, z) (三角不等式)。 空间上的度量会引发诸如开集和闭集之类的拓扑性质，从而导致对更抽象的拓扑空间的研究。具体来说，关 于任何一点 x 在度量空间 \mathcal{X} ，我们定义半径的开球 r>0 关于 x 作为集合 \ \$$
$\mathrm{B}{-}{\mathrm{r}}(\mathrm{x})=\left{\mathrm{y} \backslash\right.$ in Imathcal ${\mathrm{X}}: \mathrm{d}(\mathrm{x}, \mathrm{y}) 0$ suchthat $\mathrm{B}{-}{\mathrm{r}}(\mathrm{x})$ iscontainedin 美元。开集的补集称为闭集。

## 计算机代写|深度学习代写deep learning代考|Banach and Hilbert Space

• $l^{2}(\mathbb{Z})$ : 由平方和离散时间信号组成的函数空间，即

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|机器学习代写machine learning代考|COMP4702

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|机器学习代写machine learning代考|Proposed Artificial Dragonfly Algorithm for solving Optimization Problem

In this work, modified ADA is implemented for training the NN classifier. The DA model $[21,23]$ concerns on five factors for updating the location of the dragonfly. They are (i) Control cohesion (ii) Alignment (iii) Separation (iv) Attraction (iv) Distraction. The separation of $r^{t h}$ dragonfly, $M_{r}$ is calculated by Equation (1.24) and here $A$ denotes the current dragonfly position, $A_{s}^{\prime}$ refers to the location of $s^{\text {th }}$ neighbouring dragonfly and $H^{\prime}$ denotes the count of neighboring dragonflies.
$$M_{r}=\sum_{s=1}^{H^{\prime}}\left(A^{\prime}-A_{s}^{\prime}\right)$$
The alignment and cohesion are computed by Equation (1.25) and Equation (1.26). In Equation (1.25), $Q_{s}^{\prime}$ refers to the velocity of $s^{\text {th }}$ neighbour dragonfly.
\begin{aligned} J_{r} &=\frac{\sum_{s=1}^{H^{\prime}} Q_{s}^{\prime}}{H^{\prime}} \ V_{r} &=\frac{\sum_{s=1}^{H^{\prime}} A_{s}^{\prime}}{I I^{\prime}}-A \end{aligned}
Attraction towards food and distraction to the enemy are illustrated in Equation (1.27) and Equation (1.28). In Equation (1.27), $F v$ refers to the food position and in Equation (1.28), ene denotes the enemy position.
\begin{aligned} &W_{r}=F o-A^{\prime} \ &Z_{r}=e n e+A^{\prime} \end{aligned}
The vectors such as position $A^{\prime}$ and $\Delta A^{\prime}$ step are considered here for updating the position of the dragonfly. The step vector $\Delta A^{\prime}$ denotes the moving direction of dragonflies as given in Equation (1.29), in which $q^{\prime}, t^{\prime}$, $v^{\prime}, u^{\prime}, z^{\prime}$ and $\delta$ refers the weights for separation, alignment, cohesion, food factor, enemy factor, and inertia respectively and $l$ denotes to the iteration count.

## 计算机代写|机器学习代写machine learning代考|Result Interpretation

The presentation scrutiny of the implemented model with respect to varied values of $T$ is given by Figures $1.6-1.8$ and $1.9$ for accuracy, sensitivity, specificity, and F1 Score respectively. For instance, from Figure $1.6$ accuracy of $T$ at 97 is high, which is $3.06 \%, 3.06 \%, 8.16 \%$, and $6.12 \%$ better than $T$ at $94,95,98,99$, and 100 when $v^{\prime}$ is $0.2$. From Figure 1.6, the accuracy of the adopted model when $T=95$ is high, which is $8.16 \%, 13.27 \%, 8.16 \%$ and $16.33 \%$ better than $T$ at $97,98,99$ and 100 when $v^{\prime}$ is $0.4$. On considering Figure $1.6$, the accuracy at $T=95$ is high, which is $7.53 \%, 3.23 \%, 3.23 \%$ and $3.23 \%$ better than $T$ at $97,98,99$ and 100 when $v^{\prime}$ is $0.2$. Likewise, from Figure $1.7$, the sensitivity of the adopted scheme when $T=97$ is higher, which is $1.08 \%, 2.15 \%, 1.08 \%$, and $16.13 \%$ better than $T$ at $94,95,98$,99 and 100 when $v^{\prime}$ is $0.9$. Also, from Figure $1.7$, the sensitivity at $T=97$ is more, which is $7.22 \%, 12.37 \%, 7.22 \%$ and $6.19 \%$ better than $T$ at 95,98 , 99 and 100 when $v^{\prime}$ is $0.7$. Moreover, Figure $1.8$ shows the specificity of the adopted model, which revealed better results for all the two test cases. From Figure $1.8$, the specificity of the presented model at $T=95$ is high, which is $3.23 \%, 8.6 \%, 8.6 \%$, and $8.6 \%$ better than $T$ at $97,98,99$ and 100 when $v^{\prime}$ is $0.7$. From Figure 1.8, the specificity of the presented model at $T=99$ is high, which is $13.04 \%, 2.17 \%, 2.17 \%$ and $13.04 \%$ better than $T$ at 95,97 , 98 and 100 when $v^{\prime}$ is $0.6$. From Figure $1.8$, the specificity when $T=99$ is high, which is $21.05 \%, 21.05 \%, 47.37 \%$ and $47.37 \%$ better than $T$ at 95,97 , 98 and 100 when $v^{\prime}$ is $0.7$. The F1-score of the adopted model is revealed by Figure 1.9, which shows betterment for all values of $T$. From Figure $1.9$, the F1-score of the implemented model at $T=95$ is high, which is $3.23 \%, 8.6 \%$, $8.6 \%$ and $8.6 \%$ better than $T$ at $97,98,99$ and 100 when $v^{\prime}$ is $0.4$. From Figure $1.9$, the F1-score at $T=99$ is high, which is $3.23 \%, 8.6 \%, 8.6 \%$ and $8.6 \%$ better than $T$ at $95,97,98$ and 100 when $v^{\prime}$ is $0.4$. Thus, the betterment of the adopted scheme has been validated effectively.

## 计算机代写|机器学习代写machine learning代考|Related Work

A comprehensive review of various DL approaches has been done and existing methods for detecting and diagnosing cancer is discussed.

Siddhartha Bhatia et al. [4], implemented a model to predict the lung lesion from CT scans by using Deep Convolutional Residual techniques. Various classifiers like XGBoost and Random Forest are used to train the model. Preprocessing is done and feature extraction is done by implementing UNet and ResNet models. LIDC-IRDI dataset is utilized for evaluation and $84 \%$ of accuracy is recorded.

A. Asuntha et al. [5], implemented an approach to detect and label the pulmonary nodules. Novel deep learning methods are utilized for the detection of lung nodules. Various feature extraction techniques are used then feature selection is done by applying the Fuzzy Particle Swarm Optimization (FPSO) algorithm. Finally, classification is done by Deep learning methods. FPSOCNN is used to reduce the computational problem of CNN. Further valuation is done on a real-time dataset collected from Arthi Scan Hospital. The experimental analysis determines that the novel FPSOCNN gives the best results compared to other techniques.

Fangzhou Lia et al. [6], developed a 3D deep neural network model which comprises of two modules one is to detect the nodules namely the 3D region proposal network and the other module is to evaluate the cancer probabilities, both the modules use a modified U-net network. 2017 Data Science Bowl competition the proposed model won first prize. The overall model achieved better results in the standard competition of lung cancer classification.

Qing Zeng et al. [7]. implemented three variants of DL algorithms namely, CNN, DNN, and SAE. The proposed models are applied to the $\mathrm{Ct}$ scans for the classification and the model is experimented on the LIDC-IDRI dataset and achieved the best performance with $84.32 \%$ specificity, $83.96 \%$ sensitivity and accuracy is $84.15 \%$.

## 计算机代写|机器学习代写machine learning代考|Proposed Artificial Dragonfly Algorithm for solving Optimization Problem

$$M_{r}=\sum_{s=1}^{H^{\prime}}\left(A^{\prime}-A_{s}^{\prime}\right)$$

$$J_{r}=\frac{\sum_{s=1}^{H^{\prime}} Q_{s}^{\prime}}{H^{\prime}} V_{r}=\frac{\sum_{s=1}^{H^{\prime}} A_{s}^{\prime}}{I I^{\prime}}-A$$

$$W_{r}=F o-A^{\prime} \quad Z_{r}=e n e+A^{\prime}$$

## 计算机代写|机器学习代写machine learning代考|Result Interpretation

$3.23 \%, 8.6 \%, 8.6 \%$ ，和 $8.6 \%$ 好于 $T$ 在 $97,98,99$ 和 100 时 $v^{\prime}$ 是 $0.7$. 从图 $1.8$ 可以看出，所呈现模型的特殊性 在 $T=99$ 很高，即 $13.04 \%, 2.17 \%, 2.17 \%$ 和 $13.04 \%$ 好于 $T$ 在 $95,97 ， 98$ 和 100 时 $v^{\prime}$ 是 $0.6$. 从图 $1.8$, 时的特 异性 $T=99$ 很高，即 $21.05 \%, 21.05 \%, 47.37 \%$ 和 $47.37 \%$ 好于 $T$ 在 $95,97 ， 98$ 和 100 时 $v^{\prime}$ 是 $0.7$. 图 $1.9$ 显示 了所采用模型的 F1 分数，它显示了 $T$. 从图 $1.9$, 实现模型的 F1-score 在 $T=95$ 很高，即 $3.23 \%, 8.6 \%, 8.6 \%$ 和 $8.6 \%$ 好于 $T$ 在 $97,98,99$ 和 100 时 $v^{\prime}$ 是 $0.4$. 从图 $1.9$ ， F1 分数在 $T=99$ 很高，即 $3.23 \%, 8.6 \%, 8.6 \%$ 和 $8.6 \%$ 好 于 $T$ 在 $95,97,98$ 和 100 时 $v^{\prime}$ 是 $0.4$. 因此，有效地验证了所采用方案的改进。

## 计算机代写|机器学习代写machine learning代考|Related Work

A. Asuntha 等人。[5]，实施了一种检测和标记肺结节的方法。新的深度学习方法用于检测肺结节。使用各种特征提取技术，然后通过应用模糊粒子群优化 (FPSO) 算法完成特征选择。最后，分类是通过深度学习方法完成的。FPSOCNN 用于减少 CNN 的计算问题。对从 Arthi Scan 医院收集的实时数据集进行进一步评估。实验分析确定，与其他技术相比，新型 FPSOCNN 给出了最好的结果。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|机器学习代写machine learning代考|COMP30027

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|机器学习代写machine learning代考|Contrast Enhancement

The contrasting of the resized input image $\operatorname{Im}^{\mathrm{g}}$ is enhanced here. The particular procedure controls the image intensity $[16,22,23]$ and thus the image resolution is developed via the brightness and darkness of $\mathrm{Im}^{\mathrm{g}}$, as given by Equation (1.1), in which $V$ refers to the contrast improvement of the image. Therefore, the current $\operatorname{Im}^{\mathrm{g}}$ transforms into a grey image $\operatorname{Im}_{n e w}^{\mathrm{g}}$.
$$V=\left(\begin{array}{l} \left.((\text { Im -low_in }) /(\text { high_in-low_in }))^{\wedge} \text { gamma }\right) \ *(\text { high_out-low_out }) \end{array}\right)+\text { low_out }$$
Grey thresholding: The Otsu’s oriented grey thresholding [20] method portrays the threshold of the image, which is exploited for converting the grey pixel to either black or white. This is performed depending on the grey intensity (refer Figure 1.3).

Active contour [19]: Here, 2 types of driven forces namely, external and internal energy are exploited. This framework gets smoothed via internal forces and it is reallocated in the direction through the external energy. Therefore, the contour $G(n)$ is formed by the coordinate sets such as $l(n)$ and $k(n)$ as given in Equation (1.2), where $(k, l)$ indicates the contour coordinates and denotes the normalized index of the control point.
$$G(n)=(k(n) l(n)) ; G(n) \in \operatorname{Im}{n e w}^{C}(k, l)$$ Equation (1.3) shows the total energy of deformed design, where $\operatorname{Im}^{\mathrm{g}^{\text {int }} \text { indi- }}$ cates the internal energy of the curve, $\operatorname{Im}^{\mathrm{g}^{\text {con }}}$ denotes the exterior restriction, denotes the energy of the image. $$F O^{*}=\int{0}^{1}\left(F O^{\text {int } l} G(n)+F O^{i m} G(n)+F O^{c o n} G(n)\right) d n$$
In addition, the bending energy and elastic energy are summed up to form the internal energy as specified in Equation (1.4), where $\alpha(n), \beta(n)$ indicates the varying parameter that denotes continuity and contour curving respectively.
\begin{aligned} F O^{\text {int } l} &=F O^{\text {elastic }}+F O^{\text {bend }}=\alpha(n)\left|\frac{d u}{d n}\right|^{2}+\beta(n)\left|\frac{d^{2} u}{d n^{2}}\right|^{2} \ F O^{\text {elastic }} &=\alpha(G(n)-G(n-1))^{2} d n \ F O^{\text {bend }} &=\beta\left(G(n-1)-G(n)+(G(n+1))^{2} d n\right. \end{aligned}
Finally, the pre-processed image $\operatorname{Im}_{\text {pre }}$ is determined from the initial stage.

## 计算机代写|机器学习代写machine learning代考|Classification

This work exploits $\mathrm{NN}[18,24]$ for recognizing caries. The input feature set is given by Equation (1.7), in which $N_{D}$ denotes the count of elected features.
$$F E^{\text {weight }}=\left[F_{1}, F_{2}, F_{3}, F_{4} \ldots F_{N_{D}}\right]$$
The weight $W E$ of the network model is portrayed by the LM framework. Equation (1.8) portrays the NN framework, in which the resultant output from $i^{\text {th }}$ node of $j^{\text {th }}$ layer is given by $o u_{l}^{(j)}$. The input is signified by $F E^{\text {weight }}{ }{i}^{j}$, $a f(\bullet)$ indicates the activation function, the entire count of input to $j^{\text {th }}$ layer is given by $n u^{(j)}, b i{i}$ symbolizes the input bias to $j^{\text {th }}$ layer, $c$ and $d$ denotes the weight coefficient of $W E$ as specified in Equation (1.9). The predicted network output $\hat{P}$ is given by Equation (1.10), in which $w^{0}$ signifies the bias weight and $w^{(h)}$ defines the hidden neuron weight.
$$\begin{gathered} o u_{l}^{(j)}=a f\left[c_{l}^{(j)} b i_{j}+\sum_{i=1}^{n u^{(j)}} F E_{i}^{\text {weight }(j)} d_{i l}^{(j)}\right] \ W E=[c ; d] \ \hat{P}=w^{0}+\sum_{i=1}^{n u^{(j)}} o u_{l}^{(j)} w_{i}^{(h)} W E \end{gathered}$$
So as to train the network, the network weight $W E^{*}$ is optimally chosen with the determination of objective function as in Equation (1.11), where $P$ indicates the actual output.
$$W E^{*}=\arg \min [W E]|P-\hat{P}|$$
Thus the classifier classifies the input image (non-caries or caries image).

## 计算机代写|机器学习代写machine learning代考|Nonlinear Programming Optimization

The issue regarding the nonlinear program is given in Equation (1.15), in which $\hat{h}(\hat{x}), \hat{i}(\hat{x})$ and $\hat{j}(\hat{x})$ are portrayed as ‘deferential functions’.
$$\min {\hat{y}} \hat{h}(\hat{x})=0$$ So that \begin{aligned} &\hat{i}(\hat{x})=0 \ &\hat{j}(\hat{x})=0 \end{aligned} The substitution of Equation (1.15) is done by a sequence of barrier sub issues as specified in Equation (1.17), in which $\hat{l}>0$ points out the vector of slack parameters, $\hat{k}=(\hat{x}, \hat{l})$ and $\mu>0$ denotes the barrier constraint. $$\min {\hat{k}} \varphi_{\mu}(\hat{k}) \equiv \hat{h}(\hat{x})-\mu \sum_{\hat{o}}^{\hat{n}} \operatorname{In} \hat{l}_{\hat{o}}$$ $$\hat{i}(\hat{y})=0$$
So that $\hat{j}(\hat{x})+\hat{l}=0$
The Lagrangian function associated with Equation (1.17) is specified in Equation (1.19), in which $\zeta_{\hat{i}}, \zeta_{\hat{a}}$ indicates the ‘Lagrange multipliers’ and $\zeta=\left(\zeta_{\hat{i}}, \zeta_{\hat{a}}\right)$
$$\aleph(\hat{k}, \zeta ; \mu)=\varphi_{\mu}(\hat{k})+\zeta_{\hat{i}}^{\hat{v}} \hat{i}(\hat{x})+\zeta_{\hat{a}}^{\hat{v}}(\hat{a}(\hat{x})+\hat{l})$$
The optimality states in Equation (1.17) could be specified as per Equation (1.20), in which $\hat{l}$ and $\zeta_{\hat{a}}$ are non-negative, $\hat{Y}{\hat{i}}$ and $\hat{Y}{\hat{a}}$ refers to Jacobian matrices, $\hat{D}$ and $\Gamma_{\hat{a}}$ points out the diagonal matrices.
$$\left[\begin{array}{c} \nabla \hat{h}(\hat{x})+\hat{Y}{\hat{i}}(\hat{x})^{\hat{v}} \zeta{\hat{i}}+\hat{Y}{\hat{a}}(\hat{x})^{\hat{v}} \zeta{\hat{a}} \ \hat{D} \Gamma_{\hat{a}} \hat{e}-\mu \hat{e} \end{array}\right]=\left[\begin{array}{l} 0 \ 0 \end{array}\right]$$
Further, the current iterate $(\hat{k}, \zeta)$ outcomes in the primal-dual system as given by Equation (1.21), in which $\hat{z}{\hat{k}}=\left[\begin{array}{c}\dot{z}{\hat{x}} \ \hat{z}{\hat{l}}\end{array}\right], \quad \hat{z}{\zeta}=\left[\begin{array}{c}\dot{z}{\hat{i}} \ \hat{z}{\hat{a}}\end{array}\right]$,
$\hat{c}(\hat{k})=\left[\begin{array}{l}\hat{i}(\hat{x}) \ \hat{j}(\hat{x})+\hat{l}\end{array}\right], \hat{Y}(\hat{x})=\left[\begin{array}{cc}\hat{Y}{\hat{i}}(\hat{x}) & o \ \hat{Y}{\hat{a}}(\hat{x}) & 1\end{array}\right]$ and $\hat{R}(\hat{k}, \zeta ; \mu)=$
$\left[\begin{array}{cc}\nabla_{\hat{x} \hat{x}}^{2} \aleph(\hat{k}, \zeta ; \mu) & 0 \ 0 & \hat{D}^{-1} \Gamma_{\hat{a}}\end{array}\right]$
$\left[\begin{array}{cc}\hat{R}(\hat{\hat{k}}, \zeta ; \mu) & \hat{Y}(\hat{x})^{\hat{v}} \ \hat{Y}(\hat{x}) & 0\end{array}\right]\left[\begin{array}{c}\hat{z}{\hat{k}} \ \hat{z}{\zeta}\end{array}\right]=-\left[\begin{array}{c}\nabla_{\dot{k}} \aleph(\hat{k}, \zeta ; \mu) \ \hat{c}(\hat{k})\end{array}\right]$

## 计算机代写|机器学习代写machine learning代考|Contrast Enhancement

$$\left.V=\left(((\text { Im-low_in }) /(\text { high_in-low_in }))^{\wedge} \text { gamma }\right) (\text { high_out-low_out })\right)+\text { low_out }$$ 灰度阈值: Otsu 的定向灰度阈值 [20] 方法描绘了图像的阈值，用于将灰度像素转换为黑色或白色。这取决于灰度 强度 (参见图 1.3）。 活动轮廓[19]: 这里利用了两种类型的驱动力，即外部能量和内部能量。这个框架通过内力得到平滑，并通过外部 能量在方向上重新分配。因此，轮廓 $G(n)$ 由坐标集形成，例如 $l(n)$ 和 $k(n)$ 如公式 $(1.2)$ 中给出的，其中 $(k, l)$ 表示 轮廓坐标，表示控制点的归一化索引。 $$G(n)=(k(n) l(n)) ; G(n) \in \operatorname{Im} n e w^{C}(k, l)$$ 等式 (1.3) 显示了变形设计的总能量，其中 $\operatorname{Im}^{\mathrm{g}^{\mathrm{int}}}$ indi- 表示曲线的内能， $\mathrm{Im}^{\mathrm{g}{ }^{\mathrm{con}}}$ 表示外部限制，表示图像的能 量。 $$F O^{}=\int 0^{1}\left(F O^{\text {int } l} G(n)+F O^{i m} G(n)+F O^{c o n} G(n)\right) d n$$

## 计算机代写|机器学习代写machine learning代考|Classification

$$F E^{\text {weight }}=\left[F_{1}, F_{2}, F_{3}, F_{4} \ldots F_{N_{D}}\right]$$ 示为 $F E^{\text {weight }} i^{j}, a f(\bullet)$ 表示激活函数，输入到的整个计数 $j^{\text {th }}$ 层由下式给出 $n u^{(j)}, b i i$ 表示输入偏置为 $j^{\text {th }}$ 层， $c$ 和 $d$ 表示权重系数 $W E$ 如公式 (1.9) 中所述。预测的网络输出 $\hat{P}$ 由公式 (1.10) 给出，其中 $w^{0}$ 表示偏置权重和 $w^{(h)}$ 定 义隐藏的神经元权重。
$$o u_{l}^{(j)}=a f\left[c_{l}^{(j)} b i_{j}+\sum_{i=1}^{n u^{(j)}} F E_{i}^{\text {weight }(j)} d_{i l}^{(j)}\right] W E=[c ; d] \hat{P}=w^{0}+\sum_{i=1}^{n u^{(j)}} o u_{l}^{(j)} w_{i}^{(h)} W E$$

## 计算机代写|机器学习代写machine learning代考|Nonlinear Programming Optimization

$$\min \hat{y} \hat{h}(\hat{x})=0$$

$$\hat{i}(\hat{x})=0 \quad \hat{j}(\hat{x})=0$$

$$\begin{gathered} \min \hat{k} \varphi_{\mu}(\hat{k}) \equiv \hat{h}(\hat{x})-\mu \sum_{\hat{o}}^{\hat{n}} \operatorname{In} \hat{l}{\hat{o}} \ \hat{i}(\hat{y})=0 \end{gathered}$$ 以便 $\hat{j}(\hat{x})+\hat{l}=0$ 与方程 (1.17) 相关的拉格朗日函数在方程 (1.19) 中指定，其中 $\zeta{\hat{i}}, \zeta_{\hat{a}}$ 表示“拉格朗日乘数”和 $\zeta=\left(\zeta_{\hat{i}}, \zeta_{\hat{a}}\right)$
$$\aleph(\hat{k}, \zeta ; \mu)=\varphi_{\mu}(\hat{k})+\zeta_{\hat{i}}^{\hat{i}} \hat{i}(\hat{x})+\zeta_{\hat{a}}^{\hat{v}}(\hat{a}(\hat{x})+\hat{l})$$

$$\left[\nabla \hat{h}(\hat{x})+\hat{Y} \hat{i}(\hat{x})^{\hat{v}} \zeta \hat{i}+\hat{Y} \hat{a}(\hat{x})^{\hat{v}} \zeta \hat{a} \hat{D} \Gamma_{\hat{a}} \hat{e}-\mu \hat{e}\right]=\left[\begin{array}{lll} 0 & 0 \end{array}\right]$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|机器学习代写machine learning代考|COMP5318

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|机器学习代写machine learning代考|Related Work

In 2019, Ayşe et al. [1] have obtainable a vivo study for confirming the recognition of proximal caries by means of NILTI. Moreover, the diagnostic performance of the device was compared over other caries recognition techniques, together with visual assessment. Accordingly, here a total of nine seventy-four proximal surfaces of stable posterior teeth from thirty-four patients were taken into account. The data were examined with statistical analysis and the AUC, specificity, and sensitivity were computed.

In 2019, Darshan et al. [2] have computed the relationship among susceptibility of dental caries progression risk and ENAM gene polymorphisms. The implemented analysis was performed on one sixty-eight children from South India and kids affected by dental caries were also taken into account. ‘Preliminary Insilco analysis’ has revealed that variations in ‘rs7671281 (Ile648Thr) amino acid’ leads to the functional and structural changes in the ENAM.

In 2018, Lee et al. [26] have adopted a method for evaluating the efficiency of DCNN approaches for diagnosis and detection of dental caries on ‘periapical radiographs’. Accordingly, this analysis focused on the potential effectiveness of the DCNN framework for the diagnosis and detection of dental caries. From the analysis, the DCNN framework has offered significant performance in recognizing dental caries in ‘periapical radiographs’.

In 2019, Yue et al. [27] have carried out an analysis on detecting dental caries on three eighty-six kids residing in Mexico town. Here, ‘graphitefurnace atomic-absorption spectroscopy’ was used for quantifying the $\mathrm{Pb}$ levels of blood. Accordingly, the existence of dental caries was computed by means of DMFT scores. Furthermore, the residual approach was exploited in this work for determining the total energy produced in the children based on the consumption of sweets and beverages.

In 2019, Cácia et al. [28] have analyzed how the risk factors of patients influenced operative diagnostic decisions in a dental oriented system in the Netherlands. In this work, the data were gathered from eleven dental practices and the patients attended the practice regularly throughout the observation time. Consequently, a descriptive study was carried out after performing the MLR process.

## 计算机代写|机器学习代写machine learning代考|Proposed Model for Cavities Detection

Figure $1.1$ reveals the schematic depiction of the embraced dental cavities detection model. The instigated outline comprises three foremost steps:

• Enhancement and Pre-processing;
• Feature Extraction;
• Classification;
• Optimization.
At the outset, the input image Im is imperiled to noise removing, brightening, and enriching through pre-processing, which comprises four important image upgrading features such as CLLAHE, contrast upgrading, grey thresholding, and active contour. From the pre-processed image $I_{\text {pre }}$, the features are mined by the aid of the MSL method like MLDA \& MPCA model. These mined features are then imperiled to cataloging using NN classifier that bids the categorized outcome (Cavities or No Cavities) [13-16].

## 计算机代写|机器学习代写machine learning代考|Pre-processing

The image Im is improved by carrying out the below processes.
Conventional Adaptive Histogram Equalisation is apt to over intensify the contrast in near-constant provinces of the image, meanwhile the histogram in such areas is exceedingly strenuous. As a consequence, Adaptive Histogram Equalization may root noise to be enlarged in near-constant areas. Contrast Limited AHE (CLAHE) is modified of adaptable and adjustable histogram equalization in which the dissimilarity intensification is inadequate, so as to diminish this delinquent of noise intensification.

In Contrast Limited AHE (CLAHE), the contrast solidification in the vicinity of a quantified pixel worth is quantified by the gradient of the variation function. This is interactive to the slope of the locality accumulative dissemination function and accordingly to the cost of the histogram at that pixel cost. Contrast Limited AHE confines the intensification by trimming the histogram at a predefined value before calculating the CDF. This confines the slant of the CDF and consequently of the alteration function. The cost at which the histogram is cropped, the ostensible clip perimeter, be governed by normalization of the histogram and thus on the extent of the vicinity region. Collective values limit the resultant intensification. It is advantageous not to discard the part of the histogram that exceeds the clip limit but to redistribute it equally among all histogram bins (refer Figure $1.2$ ) [17-21].

## 计算机代写|机器学习代写machine learning代考|Related Work

2019 年，Ayşe 等人。[1] 获得了一项体内研究，以确认通过 NILTI 识别近端龋齿。此外，该设备的诊断性能与其他龋齿识别技术以及视觉评估进行了比较。因此，这里总共考虑了来自 34 名患者的稳定后牙的 9 74 个近端面。用统计分析检查数据并计算AUC、特异性和敏感性。

2019 年，Darshan 等人。[2] 计算了龋齿进展风险的易感性与 ENAM 基因多态性之间的关系。对来自南印度的 168 名儿童进行了实施分析，并且还考虑了受龋齿影响的儿童。“初步 Insilco 分析”显示，“rs7671281 (Ile648Thr) 氨基酸”的变异导致 ENAM 的功能和结构变化。

2018 年，Lee 等人。[26] 采用了一种方法来评估 DCNN 方法在“根尖片”上诊断和检测龋齿的效率。因此，本分析侧重于 DCNN 框架在龋齿诊断和检测方面的潜在有效性。从分析来看，DCNN 框架在识别“根尖片”中的龋齿方面提供了显着的性能。

2019年，岳等人。[27] 对居住在墨西哥城的 3 名 86 名儿童的龋齿进行了分析。在这里，“石墨炉原子吸收光谱”用于量化磷b血液水平。因此，龋齿的存在是通过 DMFT 评分来计算的。此外，在这项工作中利用剩余方法来确定基于糖果和饮料消费的儿童产生的总能量。

2019 年，Cácia 等人。[28] 分析了患者的风险因素如何影响荷兰牙科系统中的手术诊断决策。在这项工作中，数据来自 11 家牙科诊所，患者在整个观察期间定期参加诊所。因此，在执行 MLR 过程后进行了描述性研究。

## 计算机代写|机器学习代写machine learning代考|Proposed Model for Cavities Detection

• 增强和预处理；
• 特征提取;
• 分类;
• 优化。
首先，输入图像Im通过预处理进行去噪、增亮和富集，包括CLLAHE、对比度升级、灰度阈值和主动轮廓等四个重要的图像升级特征。从预处理图像我预 ，特征是通过MLDA \& MPCA模型等MSL方法挖掘出来的。然后，使用 NN 分类器对分类结果（Cavities or No Cavities）出价 [13-16] 对这些挖掘的特征进行编目。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|机器学习代写machine learning代考|A Cross-Domain Landscape of ICT Services in Smart Cities

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 计算机代写|机器学习代写machine learning代考|Layered View of Smart Services

To unify the structure of smart city services and to build a basis for their interconnected holistic description, we introduce the concept of a layered view of the smart city [1]. The approach is based on service value proposition where the structure emerges automatically from the ordering of services according to their purpose.

In this model, five layers of services are identified where the layer one is proposing the value to the final user such as city citizen. Services from lower layers are providing their functionality to services from the upper level. These five layers are: (1) smart features – complex services, offering high perceived value to the city citizens. The value proposition depends on a particular configuration of services from the lower levels (e.g., mobility); (2) smart services-complex services that are using other (more simple) services. Their value proposition is aimed at smart features, even the possibility to use them directly is not excluded, but very limited (e.g., traffic control); (3) support services-simple services with predefined API that you can use to obtain particular information (e.g., vehicle of public transport position check); (4) software-layer that contains all basic software systems that are used to collect, store, process, or control the data; (5) hardware-layer of basic devices to get the data, e.g., sensors, activators, servers, and networks. This approach allows us to model the structure of the services across different domains and it identifies the smart service system in which the value can be perceived, diffused, and co-created on different layers of the service structure.

## 计算机代写|机器学习代写machine learning代考|Urban Planning

Building a smart city needs to take many factors into consideration. To create a city that can adapt citizens needs and demands as well as changing technologies, it is necessary to approach the smart city design in a holistic way. Urban planning aims at connecting all the other parts of the city into one interconnected functional entity. Therefore, urban planning can be defined as “a technical and political process concerned with the control of land use and design of urban environments, which can benefit from trace data, analysis and mining” $[30,31]$. We consider that urban planning creates prerequisites for all other services within the smart city and is responsible for deciding how many infrastructures the city needs, how they should be distributed and whether the infrastructure is sufficient for the needs of citizens. We can therefore state that “an efficient urban planning can, without any doubts, improve the quality of the life of all the citizens” [24].

Important tools for efficient urban planning are data tracing, data mining and analysis. There are open datasets as well as preserved datasets from the enterprises. Currently, due to crowdsourcing initiatives, when citizens play a role of sensor (usually by means of their smart phones), open data contain a wide range of data about location of citizens. On the other hand, preserved data from telecommunication companies also provide valuable insights into mobility across the city. Additionally, there are plenty of datasets regarding demographic and geographic information provided by the city. These datasets represent a valuable source for data mining and analysis and can support efficient urban planning [31]. As plotted in Fig. 1, urban planning includes a wide range of services. One of the most significant groups of services is smart buildings. Smart buildings are defined as the buildings with intelligent features such as ability to measure, store, and analyze data from the environment, namely for the purpose of household automation $[13,8]$, energy savings $[23]$, or building safety $[32]$.

## 计算机代写|机器学习代写machine learning代考|Smart Energy

One of the fundamental elements in smart cities is the optimization of energy use within the entire community, which aims at achieving eco-friendly lifestyle with high quality of living $[27,34,7]$. The crucial role in this process is operated by ICT empowered electricity grid, which is known as the smart grid. In the smart grid, the ICT infrastructure improves efficient use of the physical infrastructure, providing the capacity to safely integrate more renewable energy sources and smart devices, delivering power more efficiently in secure and reliable way through new control and monitoring capabilities. By using automatic grid reconfiguration, the city can prevent or restore outages and enable consumers to have greater control over their consumption of electricity $[35,36,37]$.

Several concepts of smart grid development and implementation have been introduced [17]. However, a complete picture of the smart grid ICT architecture and its existing design alternatives is still missing. When building the view in Fig. 2, we studied smart grid implementation from EU and USA. A valuable input for our study was the DISCERN project [18], which resulted into the generally accepted smart grid Reference Architecture (SGAM) model, as well as a usecase approach to smart grid development, where the use cases help to determine the key performance indicator (KPI) for the smart grid architecture [10]. Another project, called Grid4EU, presents six demo architectures. All of them were created with regard to SGAM. One of the demo architectures was proposed by a Czech energy distribution company [18]. This proposed solution focuses on automatic operation within the grid. It is supported by remote-control devices and connections that enable fast communication via regional dispatcher system. Another useful information source about smart grid architecture development in the USA was provided by a study of the California State University of Sacramento [38]. Although its main focus was cyber security and vulnerability of smart grid architecture, this study also brings information about the architecture itself and its stage of development in the USA. Finally, the description of the hardware part of the smart grid infrastructure can be found in [21], where the purpose of all hardware components in Fig. 2 is described.

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。