The authors develop a new method for training machine learning algorithms to differentiate between hate speech and cultural speech in online platforms.
Read More...Battling cultural bias within hate speech detection: An experimental correlation analysis
The authors develop a new method for training machine learning algorithms to differentiate between hate speech and cultural speech in online platforms.
Read More...An explainable model for content moderation
The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.
Read More...Comparing model-centric and data-centric approaches to determine the efficiency of data-centric AI
In this study, three models are used to test the hypothesis that data-centric artificial intelligence (AI) will improve the performance of machine learning.
Read More...The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images
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.
Read More...Prediction of diabetes using supervised classification
The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.
Read More...Predicting the Instance of Breast Cancer within Patients using a Convolutional Neural Network
Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.
Read More...The effect of activation function choice on the performance of convolutional neural networks
With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.
Read More...Predicting baseball pitcher efficacy using physical pitch characteristics
Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.
Read More...A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging
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.
Read More...Association of agenesis of the corpus callosum with epilepsy and anticonvulsant drug treatment
Agenesis of the Corpus Callosum (ACC) is a birth defect where an infant’s corpus callosum, the structure linking the brain’s two hemispheres to allow interhemispheric communication, fails to develop in a typical manner during pregnancy. Existing research on the connection between ACC and epilepsy leaves significant gaps, due to the lack of focused investigation. One important gap is the degree to which ACC may impact the course of epilepsy treatment and outcomes. The present study was conducted to test the hypotheses that epilepsy is highly prevalent among individuals with ACC, and that those with both ACC and epilepsy have a lower response rate to anticonvulsant drugs than other patients treated with anticonvulsant drugs. A weighted average of epilepsy rates was calculated from a review of existing literature, which supported the hypothesis that epilepsy was more common among individuals with ACC (25.11%) than in the general population (1.2%). An empirical survey administered to 57 subjects or parents of subjects showed that rate of intractable epilepsy among study subjects with both ACC and epilepsy was substantially higher than the rate found in the general population, indicating that individuals with both conditions had a lower response rate to the anticonvulsant drugs. This study contributes novel results regarding the potential for concurrence of ACC and epilepsy to interfere with anticonvulsant drug treatment. We also discuss implications for how medical professionals may use the findings of this study to add depth to their treatment decisions.
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