Nowadays, remotely sensed data has increased dramatically. Microwaves and optical images with different spatial and temporal resolutions are available and are used to monitor a variety of ...

Version 8.0 has been released. Get it here or with Docker. This release adds the capability to use pre-trained scikit-learn, Keras or REST API based models with Qlik. More on this here. Qlik's ...

Drone inspections are widely utilized in the detection of insulators in power lines. To address issues with traditional object detection algorithms, such as large parameter counts, low detection ...

Abstract: The research examines the Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) machine learning algorithms with the goal of using machine learning to detect malware and mitigate ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and ...

If you want to learn the math behind data science and machine learning, 3Blue1Brown is the channel for you. Created by Grant Sanderson, 3Blue1Brown uses animation to explain complex mathematical ...

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...

Clustering is usually the first exploratory analysis step in empirical data. When the data set comprises graphs, the most common approaches focus on clustering its vertices. In this work, we are ...

The input data is preprocessed with the Scanpy package in Python. Starting from the raw gene-cell count matrix, library size normalization is performed to scale the total counts of each cell to be ...