Automating classification of galaxy components is important for understanding the formation and evolution of galaxies. Traditionally, only the larger galaxy structures such as the spiral arms, bulge, and disc are classified. Here we use machine learning (ML) pixel-by-pixel classification to automatically classify all galaxy components within digital imagery of massive spiral galaxy UGC 2885. Galaxy components include young stellar population, old stellar population, dust lanes, galaxy center, outer disc, and celestial background. We test three ML models: maximum likelihood classifier (MLC), random forest (RF), and support vector machine (SVM). We use high-resolution Hubble Space Telescope (HST) digital imagery along with textural features derived from HST imagery, band ratios derived from HST imagery, and distance layers. Textural features are typically used in remote sensing studies and are useful for identifying patterns within digital imagery. We run ML classification models with different combinations of HST digital imagery, textural features, band ratios, and distance layers to determine the most useful information for galaxy component classification. Textural features and distance layers are most useful for galaxy component identification, with the SVM and RF models performing the best. The MLC model performs worse overall but has comparable performance to SVM and RF in some circumstances. Overall, the models are best at classifying the most spectrally unique galaxy components including the galaxy center, outer disc, and celestial background. The most confusion occurs between the young stellar population, old stellar population, and dust lanes. We suggest further experimentation with textural features for astronomical research on small-scale galactic structures.

April 28, 2022 – Researchers have developed a convolutional neural network (CNN) model, a type of deep learning model, for classifying epileptic seizures that is designed to provide maximum accuracy and minor computational complexity, according to a study published in Soft Computing. The researchers developed their algorithm by integrating CNN architecture with a hierarchical attention mechanism, which was expected to enhance the model's performance. The model comprises three parts: a feature extraction layer, a hierarchical attention layer, and a classification layer. The model, which also uses a support vector machine (SVM) algorithm, analyzes a feature map obtained from the raw EEG signal and determines whether the EEGs it was taken from are "healthy" or "seizure."

Estimation of cotton yield before harvest offers many benefits to breeding programs, researchers and producers. Remote sensing enables efficient and consistent estimation of cotton yields, as opposed to traditional field measurements and surveys. The overall goal of this study was to develop a data processing pipeline to perform fast and accurate pre-harvest yield predictions of cotton breeding fields from aerial imagery using machine learning techniques. By using only a single plot image extracted from an orthomosaic map, a Support Vector Machine (SVM) classifier with four selected features was trained to identify the cotton pixels present in each plot image. The SVM classifier achieved an accuracy of 89%, a precision of 86%, a recall of 75%, and an F1-score of 80% at recognizing cotton pixels. After performing morphological image processing operations and applying a connected components algorithm, the classified cotton pixels were clustered to predict the number of cotton bolls at the plot level. Our model fitted the ground truth counts with an R2 value of 0.93, a normalized root mean squared error of 0.07, and a mean absolute percentage error of 13.7%. This study demonstrates that aerial imagery with machine learning techniques can be a reliable, efficient, and effective tool for pre-harvest cotton yield prediction.

We offer you a brighter future with FREE online courses Start Now!! Support Vector Machines Tutorial – I am trying to make it a comprehensive plus interactive tutorial, so that you can understand the concepts of SVM easily. A few days ago, I met a child whose father was buying fruits from a fruitseller. That child wanted to eat strawberry but got confused between the two same looking fruits. After noticing for a while he understands which one is Strawberry and picks one from the basket. Same as that child, support vector machines work.

Hyperspectral images often have hundreds of spectral bands of different wavelengths captured by aircraft or satellites that record land coverage. Identifying detailed classes of pixels becomes feasible due to the enhancement in spectral and spatial resolution of hyperspectral images. In this work, we propose a novel framework that utilizes both spatial and spectral information for classifying pixels in hyperspectral images. The method consists of three stages. In the first stage, the pre-processing stage, Nested Sliding Window algorithm is used to reconstruct the original data by {enhancing the consistency of neighboring pixels} and then Principal Component Analysis is used to reduce the dimension of data. In the second stage, Support Vector Machines are trained to estimate the pixel-wise probability map of each class using the spectral information from the images. Finally, a smoothed total variation model is applied to smooth the class probability vectors by {ensuring spatial connectivity} in the images. We demonstrate the superiority of our method against three state-of-the-art algorithms on six benchmark hyperspectral data sets with 10 to 50 training labels for each class. The results show that our method gives the overall best performance in accuracy. Especially, our gain in accuracy increases when the number of labeled pixels decreases and therefore our method is more advantageous to be applied to problems with small training set. Hence it is of great practical significance since expert annotations are often expensive and difficult to collect.

In recent years, transformers have emerged as a powerful deep neural network architecture that has been proven to beat the state of the art in many application domains, such as natural language processing (NLP) and computer vision. This post uncovers how you can achieve maximum accuracy with the fastest training time possible when fine-tuning transformers. We demonstrate how the cuML support vector machine (SVM) algorithm, from the RAPIDS Machine Learning library, can dramatically accelerate this process. CuML SVM on GPU is 500x faster than the CPU-based implementation. This approach uses SVM heads instead of the conventional multi-layer perceptron (MLP) head, making it possible to fine-tune with precision and ease.

In this work, a novel algorithm called SVM with Shape-adaptive Reconstruction and Smoothed Total Variation (SaR-SVM-STV) is introduced to classify hyperspectral images, which makes full use of spatial and spectral information. The Shape-adaptive Reconstruction (SaR) is introduced to preprocess each pixel based on the Pearson Correlation between pixels in its shape-adaptive (SA) region. Support Vector Machines (SVMs) are trained to estimate the pixel-wise probability maps of each class. Then the Smoothed Total Variation (STV) model is applied to denoise and generate the final classification map. Experiments show that SaR-SVM-STV outperforms the SVM-STV method with a few training labels, demonstrating the significance of reconstructing hyperspectral images before classification.

The course covers Machine Learning in exhaustive way. The presentations and hands-on practical are made such that it's made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios. UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data.

The data I used can be found on Kaggle. The overall dataset contains eight comma-separated value(CSV) files, containing data from the MLB seasons 2015–2018. However, I focused on two of the files, pitches and at-bats. The pitches CSV file contained 40 data columns, and the at-bats had 11. Both of them had data values that I would need, so I decided to merge the files.

This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21st century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them in Python from scratch.