The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.
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Pruning replay buffer for efficient training of deep reinforcement learning
Reinforcement learning (RL) is a form of machine learning that can be harnessed to develop artificial intelligence by exposing the intelligence to multiple generations of data. The study demonstrates how reply buffer reward mechanics can inform the creation of new pruning methods to improve RL efficiency.
Read More...Utilizing a novel T1rho method to detect spinal degeneration via magnetic resonance imaging
Spinal degeneration has been linked to critical conditions such as osteoarthritis in adults aged 40+; while this condition is considered to be irreversible, we took interest in magnetic resonance imaging (MRI) for early detection of the condition. Ultimately, our purpose was to determine the effectiveness of a relatively novel T1rho method in the early detection of spinal degeneration, and we hypothesized that the early to mild progression of spinal degeneration would affect T1rho values following an MRI scan.
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...Implementing machine learning algorithms on criminal databases to develop a criminal activity index
The authors look at using publicly available data and machine learning to see if they can develop a criminal activity index for counties within the state of California.
Read More...Evaluating TensorFlow image classification in classifying proton collision images for particle colliders
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.
Read More...Relating socioeconomic position (SEP) and vaccination with Covid-19 rates in select populations
This article describes the relationship between socioeconomic factors and the extent of how the COVID-19 Pandemic affected communities. Factors such as infection rate, vaccination rate, and economic status were all evaluated within the context of this article.
Read More...Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste
About 25% of the food grown never reaches consumers due to spoilage, and 11.5 billion pounds of produce from gardens are wasted every year. Current solutions involve farmers manually looking for and treating diseased crops. These methods of tending crops are neither time-efficient nor feasible. I used a convolutional neural network to identify signs of plant disease on leaves for garden owners and farmers.
Read More...Deep residual neural networks for increasing the resolution of CCTV images
In this study, the authors hypothesized that closed-circuit television images could be stored with improved resolution by using enhanced deep residual (EDSR) networks.
Read More...Heat impact to food’s shelf life - An example of milk
Food spoilage happens when food is not kept in a good storage condition. Qualitatively estimating the shortened shelf life of food could reduce food waste. In this study, we tested the impact of heat on milk shelf life. Our results showed that an exposure at room temperature (25°C) for 3.2 hours will decrease the shelf life of milk by one day.
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