The authors compare current machine learning algorithms with a new Explainable AI algorithm that produces a human-comprehensible decision tree alongside predictions.
Read More...Explainable AI tools provide meaningful insight into rationale for prediction in machine learning models
The authors compare current machine learning algorithms with a new Explainable AI algorithm that produces a human-comprehensible decision tree alongside predictions.
Read More...Chemoreception in Aurelia aurita studied by AI-enhanced image analysis
The authors studied the chemoreception of moon jellyfish in response to food, and developed an AI tool to identify track and quantify the pulsation of swimming jellyfish.
Read More...Mismatch repair is not correlated with genomic alterations in glioblastoma patients
The authors looked at biomarkers in glioblastoma patients they hypothesized to be correlated with survival rate. Ultimately they did not find hMSH2 or hMSH6, genes involved in mismatch repair, to be significantly associated with outcomes related to increased survival.
Read More...An efficient approach to automated geometry diagram parsing
Here, beginning from an initial interest in the possibility to use a computer to automatically solve a geometry diagram parser, the authors developed their own Fast Geometry Diagram Parser (FastGDP) that uses clustering and corner information. They compared their own methods to a more widely available, method, GeoSolver, finding their own to be an order of magnitude faster in most cases that they considered.
Read More...Fractals: Exploring Mandelbrot Coordinates and qualitative characteristics of the corresponding Julia Set
Here based on an interest in fractals, the authors used a Julia Set Generator to consider a specific point on the Mandelbrot set with an associated coordinate. In this manner, they found that the complexity of the Mandelbrot and Julia Sets are governed by relatively simple rules, revealing that the intricate patterns of fractals can be defined by defined by simple rules and patterns.
Read More...Examining effects of E. muscae on olfactory function in D. melanogaster
In this article, the authors investigate the effects of fungus E. muscae on fruit fly behavior. More specifically, they investigate whether this fungus affects olfaction. Their findings contribute to a broader set of studies seeking to understand how host's central nervous systems can be affected by infections.
Read More...The effects of stress on the bacterial community associated with the sea anemone Diadumene lineata
In healthy ecosystems, organisms interact in a relationship that helps maintain one another's existence. Stress can disrupt this interaction, compromising the survival of some of the members of such relationships. Here, the authors investigate the effect of stress on the interaction between anemones and their microbiome. Their study suggests that stress changes the composition of the surface microbiome of the anemone D. lineata, which is accompanied by an increase in mucus secretion. Future research into the composition of this stress-induced mucus might reveal useful antimicrobial properties.
Read More...Observing how the distance from the mouth of a Bahamian mangrove affects biodiversity
In this study, the biodiversity of marine animals was studied at different locations along a mangrove, which is a salt-tolerant shrub with elaborate root structures that is found on tropical coastlines.
Read More...Lung region complexity affects Grad-CAM and Deep Taylor outcomes in pneumonia predictions
The authors studied the accuracy of two commonly used explainable AI techniques for detecting pneumonia in chest x-rays.
Read More...Comparative study on three machine learning models in novel autonomous drone-based detection of invasive plant Brassica nigra
Autonomous drone imaging combined with machine learning offers a promising approach for early detection of invasive species. In this study, students built an autonomous drone and compared three models: CNN, SGDC, and XGBoost, to identify Brassica nigra from aerial footage. Their results show that CNNs most effectively recognize key visual features, demonstrating strong potential for supporting conservation and invasive plant management.
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