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Support Vector Machines


Galactic Component Mapping of Galaxy UGC 2885 by Machine Learning Classification

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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.


La veille de la cybersécurité

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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."


Cotton Yield Estimation From Aerial Imagery Using Machine Learning Approaches

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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.


Support Vector Machines Tutorial - Learn to implement SVM in Python - DataFlair

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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.


Fast Fine-Tuning of AI Transformers Using RAPIDS Machine Learning

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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.


Machine Learning Bootcamp: SVM,Kmeans,KNN,LinReg,PCA,DBS

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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.


A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification

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COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples' death is not only linked to its infection but also to peoples' mental states and sentiments triggered by the fear of the virus. People's sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples' sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tree classifier, AdaBoost, and Multilayer Perceptron), based on the three feature representation methods: TF-IDF, FastText, and Hybrid. To evaluate our methods, we use a publicly available Nepali-COVID-19 tweets dataset, NepCov19Tweets, which consists of Nepali tweets categorized into three classes (Positive, Negative, and Neutral). The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods. Natural language processing (NLP) techniques have been developed to assess peoples' sentiments on various topics.


Baseball Pitch Prediction

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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.


Data Mining with Rattle

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Rattle and R deliver a very sophisticated data mining environment. Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the'Rattle' package in R software. Rattle is a popular GUI-based software tool which'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package.


Machine Learning Distinguishes Gait Patterns in MS

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The gait patterns of patients with multiple sclerosis (MS) were found to be discriminable from those of healthy controls using machine learning methods, according to an article published in BioMedical Engineering OnLine. The study utilized a standard set of gait features with a support vector machine learning model to differentiate the gaits of patients with MS from those of healthy controls with an accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%. When an additional set of novel gait features (toe direction, hull area, base of support area, foot length, foot width, and foot area) were added to the support vector machine, the accuracy increased to 88%, recall to 90%, and F1-score to 93%. The precision level was unchanged. "These results demonstrate that machine learning models trained on new features from raw walkway data can more effectively separate patient and control targets and could potentially be served as an alternative method for identifying gait abnormalities in MS," the authors said.