Osteoporosis, a prevalent condition among the aging population worldwide, is characterized by diminished bone mass and altered bone structure, increasing susceptibility to fractures. It poses a significant and growing global public health challenge over the next decade. Diagnosis typically involves Dual-energy X-ray absorptiometry to measure bone mineral density, yet its mass screening utility is limited. The Singh Index (SI) provides a straightforward, semi-quantitative means of osteoporosis diagnosis through plain hip radiographs, assessing trabecular patterns in the proximal femur. Although cost-effective and accessible, manual SI calculation is time-intensive and requires expertise. This study aims to automate SI identification from radiographs using machine learning algorithms. An unlabelled dataset of 838 hip X-ray images from Indian adults aged 20-70 was utilized. A custom convolutional neural network architecture was developed for feature extraction, demonstrating superior performance in cluster homogeneity and heterogeneity compared to established models. Various clustering algorithms categorized images into six SI grade clusters, with comparative analysis revealing only two clusters with high Silhouette Scores for promising classification. Further scrutiny highlighted dataset imbalance and emphasized the importance of image quality and additional clinical data availability. The study suggests augmenting X-ray images with patient clinical data and reference images, alongside image pre-processing techniques, to enhance diagnostic accuracy. Additionally, exploring semi-supervised and self-supervised learning methods may mitigate labelling challenges associated with large datasets.

The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying infrastructure resilience. However, the theoretical model merely provides a one-size-fits-all framework for all infrastructure systems. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. Limited empirical studies hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined over 200 resilience curves related to power outages in three major extreme weather events. Using unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power system resilience curves, triangular, and trapezoidal curves. Triangular curves characterize resilience behavior based on 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructures.

As the adoption of machine learning models increases, ensuring robust models against adversarial attacks is increasingly important. With unsupervised machine learning gaining more attention, ensuring it is robust against attacks is vital. This paper conducts a systematic literature review on the robustness of unsupervised learning, collecting 86 papers. Our results show that most research focuses on privacy attacks, which have effective defenses; however, many attacks lack effective and general defensive measures. Based on the results, we formulate a model on the properties of an attack on unsupervised learning, contributing to future research by providing a model to use.

Too Long; Didn't Read Unsupervised Machine Learning can help in data preparation for machine learning projects and how it helps to get more accurate business insights. Data scientists spend up to 80% of their time cleaning the gathered data before training the ML model, which is not a guarantee of the entire absence of errors and bias. The algorithm is commonly chosen according to business goals, and if performed correctly, the business solution is tenfold more powerful than a supervised learning-based one. The most commonly used approach is the most often used approach to reduce the number of input variables in a dataset.

In supervised machine learning, we have a labeled dataset that is used to train the model. For example, we train a model to predict the prices of houses based on features like area, number of bedrooms, and location, etc. In unsupervised machine learning, we do not have a labeled dataset. The goal of unsupervised machine learning is to find patterns and relationships in data. Clustering is one of the most popular techniques used in unsupervised machine learning.

Unsupervised machine learning is a type of machine learning where the model is trained on a dataset without any labeled output. The goal of unsupervised learning is to uncover hidden patterns or relationships in the data. Unsupervised learning is useful when labeled data is not available or when the goal is to discover new relationships in the data. However, it can be more challenging to evaluate the results of unsupervised learning compared to supervised learning, as there is no clear metric to assess the performance of the model. In conclusion, unsupervised learning is a powerful tool for understanding and extracting information from complex and unlabeled data.

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course?

Machine learning, a branch of artificial intelligence and computer science, allows computers to mimic human learning by steadily improving their accuracy without explicit programming. Machine learning is the most crucial element in the developing field of data science since it makes use of statistical techniques. Machine learning is significant because it facilitates the development of new goods and helps organizations comprehend trends in customer behavior and functional business processes. Machine learning plays a crucial role in the daily operations of many of the world's most successful companies today, including Facebook, Google, and Uber. Machine learning has become a significant competitive advantage for many businesses.

Cyber threats are real and constantly evolving, and responsible cybersecurity is looming.[1] This confluence of factors makes cybersecurity more important than ever. However, this article is not a detailed account of cyber threats or the necessity of cybersecurity. It is 2022, and I will assume you already know these realities. Instead, this article is about research, specifically how to pursue research properly, based on my experience with two different research programs at the Defense Advanced Research Projects Agency (DARPA).

Unsupervised machine learning algorithms analyze and cluster unlabeled datasets. Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There's an endless supply of industries and applications that machine learning can make more efficient and intelligent. You have probably come across Google News, which automatically groups similar news articles under a topic. Have you ever wondered what process runs in the background to arrive at these groups?