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

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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。