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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
Editorial: Predictive and diagnostic approaches for systemic disorders using ocular assessment
Systemic diseases often manifest in the eye due to their unique vasculature and neural composition. The retina, for instance, shares similar embryological origins with the brain and is supplied by a rich vascular network. This makes it an ideal site for detecting vascular and neurological changes that reflect systemic conditions. Conditions such as diabetes, hypertension, and autoimmune diseases frequently display characteristic ocular signs, which, when detected early, can facilitate timely interventions. For example, diabetic retinopathy remains a prominent example of how ophthalmic examinations can reveal the severity and progression of systemic diabetes. Retinal imaging enables the identification of microaneurysms, hemorrhages, and neovascularization, all hallmark features of the disease ( 1 ). The presence of these signs not only confirms the diagnosis but can also predict the potential for systemic complications ( 2 ).
Understanding Consumer Preferences for Movie Trailers from EEG using Machine Learning
Abstract —Neuromarketing aims to understand consumer be- havior using neuroscience. Brain imaging tools such as EEG have been used to better understand consumer behavior that goes beyond self-report measures which can be a more accurate measure to understand how and why consumers prefer choosing one product over another. Previous studies have shown that consumer preferences can be effectively predicted by under- standing changes in evoked responses as captured by EEG. However, understanding ordered preference of choices was not studied earlier. In this study, we try to decipher the evoked responses using EEG while participants were presented with naturalistic stimuli i.e. movie trailers. Using Machine Learning techniques to mine the patterns in EEG signals, we predicted the movie rating with more than above-chance, 72% accuracy. Our research shows that neural correlates can be an effective predictor of consumer choices and can significantly enhance our understanding of consumer behavior. Index Terms —Neuromarketing, EEG, Machine Learning, Dis- crete Wavelet Decomposition
利用人工智能通过土壤和气候数据预测产量
为了预测作物产量，我们需要建立一个模型，该模型将考虑土壤类型、土壤质量、气候条件等各种因素，并预测特定地区特定作物的产量。使用以下步骤：步骤 1：收集数据收集数据是解决任何监督机器学习问题的最重要步骤。我们将收集马哈拉施特拉邦前几年的作物产量、气候条件和土壤类型的数据。步骤 2.1：探索数据建立和训练模型只是工作流程的一部分。事先了解数据的主要特征，我们可以建立一个更好的模型。这可能意味着获得更高的准确性。它也可能意味着需要更少的数据进行训练，或者更少的计算资源。各种可视化技术可用于检测训练数据中的各种相关性和模式，这可以进一步帮助创建一个准确的预测系统。
使用实时卷积神经网络的面部识别
近年来，面部识别的兴起是一种重要的技术进步，在该领域中有多种应用，包括安全，监视，身份验证系统和人类计算机界面。许多部门由于能够根据面部特征自动识别和验证人们的能力而进行了根本性的变化，从而为创新开辟了新的创新大门。面部识别的主要目的是创建可以正确识别和从图片或视频中验证人员的自动化系统。传统方法捕获复杂和歧视性面部模式的局限性包括对手工特征和浅学习技术的依赖。然而，自引入深度学习以来，面部识别取得了长足的进步，尤其是卷积神经网络（CNNS）。cnns是捕获精细面部特征的理想工具，因为它们为层次表示的出色能力显示出了惊人的能力，可以直接从未经处理的图像数据中学到。在本文中，作者专注于使用CNN模型的面部识别，旨在提高这种关键技术的准确性和韧性。作者已经采用了完善的CNN模型来应对面部识别的挑战。我们利用深度学习自动从面部图像中识别和提取高级特征，从而实现了更准确和可靠的识别。CNN模型的体系结构是为了利用面部数据中可见的基本空间链接和区域模式的创建。通过利用大量的卷积和合并层，该模型可以成功捕获低级品质，例如边缘和纹理以及高级面部特质，例如面部标志和表达式。
使用 ENVI-met BioMet
所有这些数据都存储在 ENVI-met 大气文件 _AT_ 中，在上面显示的屏幕部分中，您可以将 BioMet 的内部数据与输出文件结构链接起来。一旦您成功找到包含有效模拟输出文件的文件夹（参见步骤 1），BioMet 应该会自动填充上面给出的所有关系。理论上，BioMet 知道自版本 2 以来生成的任何 ENVI-met _AT_ 文件的输出格式。但是，可能总是会出现问题，或者您想使用自己的 .EDI/.EDT 或 .EDX/.EDT 文件来运行 BioMet。在这些情况下，您需要手动链接变量。
