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

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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

## 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

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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。