XiaoMi-AI文件搜索系统
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1 Genetic Testing: Whole Exome Sequencing and Whole Genome Sequencing
The human genome refers to an individual's complete set of DNA. The exome is a small section (1 to 2 percent) of the genome. It contains DNA sequences (exons) which provide instruction (coding) for making proteins, the building blocks of cells. Whole exome sequencing (WES) sequences only the coding region (1 to 2 percent) of an individual's genome. Whole exome sequencing can be used to identify variations in the protein-coding region of any gene rather than in only a select few genes. Because most known pathogenic variant(s) that cause disease occur in exons, WES is thought to be an efficient method to identify possible disease-causing pathogenic variant(s). Whole genome sequencing (WGS) sequences an individual's entire genome. It determines the order of all the nucleotides (the DNA building blocks) in an individual's DNA and can determine variations in any part of the genome. WES may potentially miss a pathogenic variant(s) in a non-coding region of the genome, therefore WGS may be used in selected cases if initial exome sequencing is not diagnostic. While WGS can accurately achieve copy number variation (CNV) detection, the use of chromosomal microarray analysis (CMA) continues to be the gold standard. Whole exome sequencing (WES) or whole genome sequencing (WGS) may be appropriate when there is no known cause of a patient's symptoms (e.g., prematurity, trauma, environmental, infectious, maternal immune disorder), clinical
基于机器学习的驾驶决策策略...
摘要开发技术为形成，工作，功能化和用于更好地改善人类生活的几种发明或创造而成为了重要阶段。创建自动驾驶汽车的想法是通过取代人类的驾驶和使用人工智能来提高人类驾驶技能，以便更好地使用安全规则；避免发生事故，正确运行良好的交通和道路的道路。当前的自动驾驶汽车通过仅考虑外部因素（行人，道路状况等）来确定其驾驶策略不考虑车辆的内部状况。为了解决该问题，该项目提出了“基于自动驾驶汽车的机器学习的驾驶决策策略（DDS）”，该项目不仅通过分析外部因素，还分析了车辆的内部因素（消耗条件，RPM等级）来确定自动驾驶汽车的最佳策略。DDS使用来自云中存储的车辆的传感器数据来学习遗传算法，并确定自动驾驶汽车的最佳驾驶策略。该项目将DDS与MLP和RF神经网络模型进行了比较，以验证DDS。关键字：外部条件，内部条件，驱动决策策略。1。简介目前，全球公司正在开发第四阶段的高级自动驾驶汽车技术。自动驾驶汽车是根据各种ICT技术开发的，并且可以将操作原则分为三个级别的认可，判断和控制层面。但是，作为自动驾驶的表现识别步骤是通过在GP，相机和雷达等车辆中利用各种传感器来识别和收集有关周围情况的信息。判断步骤根据公认信息确定驾驶策略。然后，此步骤确定并分析了放置车辆的条件，并确定适合驾驶环境和目标的驾驶计划。控制步骤确定速度，方向等。关于驾驶，车辆开始自行驾驶。一辆自动驾驶的车辆执行各种行动以到达目的地，重复自己的认可，判断和控制步骤[1]。
car damage detection using machine learning
Siruganur ,Trichy Abstract – Modern car insurance industries waste a lot of resources due to claim leakages, which determines the amount they pay. Currently,visual Inspections and Validations are done manually,which can delay the claim processes.Previous study have shown that classifying images is possible with a small data set,by transferring and re purposing knowledge from models trained for a different task. Our goal is to build a Car Damage classifier using a deep learning model that is able to detect the different damage types and give an accurate depiction given a car image. However, due to the limiting set of data, it can be result in being a determining factor.Training a Convolutional Network from scratch (with random initialization) is difficult because it is relatively rare to have a large enough dataset.In this project we explore the problem of classifying images containing damaged cars to try and assess the monetary value of the damage. Because of the nature of this problem,classifying this data may prove to be a difficult task since no standardized dataset exists and some of the clases utilized might not be discriminative enough. Utilizing a pretrained YOLOv8 model,we trained a classifier in order to categorize the dataset,testing 3 different cases: damaged or not (damage vs whole),damage location (front vs rear vs side),damage level (minor vs moderate vs severe). Index Terms - YOLO model,CNN