In this study the authors looked at developing a more efficient particle collision classification method with the goal of being able to more efficiently analyze particle trajectories from large-scale particle collisions without loss of accuracy.

Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. This work presents federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. Results showed that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.

The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.

Here recognizing the growing amount of plastic waste in the oceans, the authors sought to develop and test laser imaging for the identification of waste in water. They found that while possible, limitations such as increasing depth and water turbidity result in increasing blurriness in laser images. While their image processing methods were somewhat insufficient they identified recent methods to use deep learning-based techniques as a potential avenue to viability for this method.

In this study, the authors hypothesized that closed-circuit television images could be stored with improved resolution by using enhanced deep residual (EDSR) networks.

Overwatering and underwatering grass are widespread issues with environmental and financial consequences. This study developed an accessible method to assess grass water use efficiency (WUE) combining smartphone imaging with open access color unmixing analysis. The method can be applied in automated irrigation systems or apps, providing grass WUE assessment for regular consumer use.

Reimagize, a role-playing with decision-making, was conjured, implementing social psychological concepts like counter-stereotyping and perspective-taking. As the game works implicitly to influence body image, it even counters image issues beyond personal body dissatisfaction. This study explored whether a digital role-playing card game, incorporating some of the most common prejudices of body image (like size prejudice, prejudices from the media, etc.) as identified by a digital survey/questionnaire completed by Indian girls aged 11-21, could counter these issues and reduce personal body dissatisfaction.

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. This research establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.

In this study, the authors explore contrast-enhanced magnetic resonance imaging at Earth's field.

In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.