# 线性代数网课代修|机器学习代写 machine learning代考|LSML22

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• 数值分析
• 高等线性代数
• 矩阵论
• 优化理论
• 线性规划
• 逼近论

In each AI paradigm, datasets were an important component. The accuracy of the related data leads to analytical growth, preparation, and progress. For this development to be helpful, the evidence obtained should be justified and labelled by experts in computer vision. This segment includes information on the artificial intelligence of early work for the identification of lung cancer [Wang et al., 2019]. One of the most serious ailments in the globe is lung cancer. Researchers are taking action in different directions to boost lung cancer. CT scans through machine learning techniques and lung nodular identification from a variety of defects. A deeper neural network with progeny [Taher et al., 2016] extracted images of information. The technique by the author here shows that the larger the nodule, the higher the risk of cancer growth. Compared to the class labels, also known as the pattern classification, the test set includes features in the linear SVM that increase the quality of the tests. It gets CT images using compressed technology ROI segmentation [Deep Prakash, et al. 2017]. Each ROI image is broken into a different DWT (Discrete View Switch) strategy and also some SVM-detectable GLCM classification bands [Gupta et al., 2020]. A method based on nodule size is also suggested below [Kim et al., 2016], which shows excellent performance compared with ROI isolation for the detection of cancer cell types. The feature extraction [Kumar et al., 2015], including the Otsu division thresholds and gray-level repeat matrix (GLCM), is defined as the physical dimension level. It identifies cancer nodules from its view of the effect of such traits. It also grades initial stages to prevent cancer. The median filter is a method of pre-treatment for filtering salt and pepper image noise through [Murillo, 2018] and it is measured in a numerical format [MyaTun et al., 2014] via arranging the value of the entire pixel round. Some intersecting processes covering the Otsu threshold subsequently.

An analysis of the above concepts indicates that its GLCM or SVM helps in improved classifications of images of lung cancer [Kim et al., 2016]. Using these images in a CAD system, the critical characteristics are just different from several other definitions since that model is based on characteristics like NC ratio, circularity, and so forth and establishes an estimation threshold for cells coinciding only with rule-based classification.

## 线性代数作业代写linear algebra代考|DATASET (LIDC-IDRI)

In each artificial intelligence paradigm, datasets were an important component. The accuracy of the related data leads to analytical growth, preparation, and progress. For this development to be helpful, the evidence obtained should be justified and labelled by experts in computer vision. This segment includes information on the artificial intelligence of early work for the identification of lung cancer [Rabbani et al., 2018]. The dataset contains 1018 cases from seven research centers and eight medical diagnostic firms. An XML document of CT-scan annotations is given for each case [Chen et al., 2020]. Four skilled thoracic radiologists carry out certain annotations in a two-stage process. Every radiologist classifies the results separately into three groups (“nodule $\geq 3 \mathrm{~mm}$,” “nodule $<3 \mathrm{~mm}$,” and “nodule $\geq 3 \mathrm{~mm}$ “) [Kim et al., 2018]. Then any radiologist investigates anonymously in the second step, their classification, and the classifications of other radiologists. The four radiologists separately analyze every nodule annotation. Average values above three are classed as malignant nodules and below three as benign nodules. One of four radiologists validated an overall grade of three with certain inconsistencies and their identities, and also as some nodules were omitted from its analysis. A DCM file can also be interpreted more easily through screening, and visualizing, as seen in Figure 3.3(a), by three or five radiologists, presenting a malignant or benign stage of cancer. Browsing using a Pylidctool [Loyman et al., 2020], as seen in Figure 3.3(b), shows nodules in the CT image of LIDC-IDRI-0082 as detailed: CT Slice thickness: $1.250 \mathrm{~mm}$, pixel spacing: $0.703$, no of nodule: 1, three annotations near slice 173 and annotation info (Subtlety-4, Internal structure-1, Calcification-6, Sphericity-3, Margin-4, Lobulation-2, Speculation-5, artificial intelligence Texture-5, Malignancy-5) [Wu et al., 2019].

# 计量经济学代写

## 在这种情况下，如何学好线性代数？如何保证线性代数能获得高分呢？

1.1 mark on book

【重点的误解】划重点不是书上粗体，更不是每个定义，线代概念这么多，很多朋友强迫症似的把每个定义整整齐齐用荧光笔标出来，然后整本书都是重点，那期末怎么复习呀。我认为需要标出的重点为

A. 不懂，或是生涩，或是不熟悉的部分。这点很重要，有的定义浅显，但证明方法很奇怪。我会将晦涩的定义，证明方法标出。在看书时，所有例题将答案遮住，自己做，卡住了就说明不熟悉这个例题的方法，也标出。

B. 老师课上总结或强调的部分。这个没啥好讲的，跟着老师走就对了

C. 你自己做题过程中，发现模糊的知识点

1.2 take note

1.3 understand the relation between definitions