Machine Learning

Clean energy technologies, encompassing renewable resources like solar, wind, and hydropower, are essential in the global effort to reduce greenhouse gas emissions and combat climate change. As the globe prepares to transition away from fossil fuels, understanding the factors and parameters influencing the penetration of clean energy into existing energy markets has become a critical step. Controversies surrounding the environmental impacts of renewable technologies, variability in market structures, and economic pressures on clean energy companies can complicate this transition.

This dataset provides electromagnetic spectrum feature data for target recognition in combat formations, supporting both closed and open set scenarios. It includes three subsets: a closed set with known target types, open set 1 with one unknown target type, and open set 2 with multiple unknown target types. Each dataset contains extracted target features, adjacency matrices representing communication links, and ground truth labels. The dataset covers radar and communication attributes, including carrier frequency, pulse characteristics, modulation types, power, and movement parameters.

An automatic waste classification system embedded with higher accuracy and precision of convolution neural network (CNN) model can significantly the reduce manual labor involved in recycling. The ConvNeXt architecture has gained remarkable improvements in image recognition. A larger dataset, called TrashNeXt, comprising 23,625 images across nine categories has been introduced in this study by combining and thoroughly analyzing various pre-existing datasets.

This dataset relates to 'Deep Learning Binomial Expansions' paper. A Computer Vision model with 5 concurrent Softmax layers identifies the coefficients, exponent and whether X and Y coefficients are positive or negative.

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

This repository contains the code and documentation for a computational framework that leverages machine learning techniques to enable accurate classification of bacterial species, even closely related strains.

The framework integrates genomic analysis methods, such as motif screening and single nucleotide polymorphism (SNP) extraction, to derive informative features from bacterial genomes. These genomic insights are then fed into machine learning models, which are trained to reliably differentiate between bacterial species based on their distinctive patterns and characteristics.