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Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. Fountain and Rasmus designed a convolutional neural network and ran it with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. They found that training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.

This manuscript evaluates peak detection algorithms for feature extraction in EMG-based hand gesture recognition using a random forest classifier. The study demonstrates that wavelet-based peak detection features achieve the highest classification accuracy (96.5%), outperforming other methods. The results highlight the potential of peak features to improve EMG-based prosthetic control systems.

In this article the authors looked at different attributes of apps within the Google Play store to determine how those may impact the overall app rating out of five stars. They found that review count, amount of storage needed and when the app was last updated to be the most influential factors on an app's rating.

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.

The Tor network allows individuals to secure their online identities by encrypting their traffic, however it is vulnerable to fingerprinting attacks that threaten users' online privacy. In this paper, the authors develop a new video fingerprinting model to explore how well video streaming can be fingerprinted in Tor. They found that their model could distinguish which one of 50 videos a user was hypothetically watching on the Tor network with 85% accuracy, demonstrating that video fingerprinting is a serious threat to the privacy of Tor users.

Droughts kill over 45,000 people yearly and affect the livelihoods of 55 million others worldwide, with climate change likely to worsen these effects. However, unlike other natural disasters (hurricanes, etc.), there is no early detection system that can predict droughts far enough in advance to be useful. Bora, Caulkins, and Joycutty tackle this issue by creating a drought prediction model.

The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.

Here, the authors wanted to explore mathematical paradoxes in which there are multiple contradictory interpretations or analyses for a problem. They used ChatGPT to generate a novel dataset of fairy tales. They found statistical differences between the artificially generated text and human produced text based on the distribution of parts of speech elements.

Modern artificial neural networks have been remarkably successful in various applications, from speech recognition to computer vision. However, it remains less clear whether they can implement abstract concepts, which are essential to generalization and understanding. To address this problem, the authors investigated the above vs. below task, a simple concept-based task that honeybees can solve, using a conventional neural network. They found that networks achieved 100% test accuracy when a visual target was presented below a black bar, however only 50% test accuracy when a visual target was presented below a reference shape.

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.