The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.
Read More...Using neural networks to detect and categorize sounds
The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.
Read More...Genetic algorithm based features selection for predicting the unemployment rate of India
The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.
Read More...Comparison of spectral subtraction noise reduction algorithms
Here, the authors investigated methods to reduce noise in audio composed of real-word sounds. They specifically used two spectral subtraction noise reduction algorithms: stationary and non-stationary finding notable differences in noise improvements depending on the noise sources.
Read More...Building deep neural networks to detect candy from photos and estimate nutrient portfolio
The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.
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...An improved video fingerprinting attack on users of the Tor network
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.
Read More...Aggression of Carcharhinus leucas and Carcharhinus amblyrhynchos towards humans
This paper presents findings on Carcharhinus leucas (bull shark) and Carcharhinus amblyrhynchos (grey reef shark) aggression towards humans at Beqa Adventure Divers in Shark Reef Marine Reserve, Fiji. We hypothesized that grey reef sharks would receive more prods than bull sharks because grey reef sharks are typically more aggressive than bull sharks. The results supported our hypothesis, as an individual grey reef shark received 2.44 prods on average per feed, while a bull shark had an average of 0.61. These findings are meaningful not only to the world’s general understanding of shark aggression, but also to human protection against grey reef sharks as well as public education on bull sharks and the conservation of the species.
Read More...Feature extraction from peak detection algorithms for enhanced EMG-based hand gesture recognition models
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.
Read More...Which life is worth saving? Preferences between animal-based and humanitarian charities
The authors looked at preferences of donors to donate between different animal-centered or humanitarian charities.
Read More...Water tubing injury patterns among different demographics: A NEISS study
The authors looked at how injuries sustained during water tubing, that require treatment at an emergency department, differ between males and females.
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