Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.
The lexical decision task is designed to test aspects of vocabulary retrieval from short-term and long-term memory by prompting the subject to differentiate between words and non-words. From this task, researchers can determine the effects of certain stimuli on linguistic processing. Numerous studies have investigated the effects of music on various cognitive capacities, like memory and vocabulary. In the current study, we hypothesized that participants would show greater accuracy rates on the lexical decision task when exposed to a selected piece of classical music while completing the task, as compared to completing the task in silence. We tested this hypothesis on a group of 25 participants who completed the lexical decision task once in silence and once while listening to Beethoven's “Moonlight Sonata, 1st Movement”. The results suggest a positive association between the effects of classical background music and improved accuracy. Our results indicate that listening to certain types of music may enhance linguistic processes such as reading and writing. Further research with a larger group of participants is necessary to better understand the association between music and linguistic processing abilities.
In this article, the authors looked at developing a strategy that would allow for earlier diagnosis of Diabetes as that improves long-term outcomes. They were able to find that BMI, tricep skin fold thickness, and blood pressure are the risk factors with the highest accuracy in predicting diabetes risk.
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.
The diagnosis of malaria remains one of the major hurdles to eradicating the disease, especially among poorer populations. Here, the authors use machine learning to improve the accuracy of deep learning algorithms that automate the diagnosis of malaria using images of blood smears from patients, which could make diagnosis easier and faster for many.
Here the authors sought to use three machine learning models to predict poverty levels in Cambodia based on available household data. They found teat multilayer perceptron outperformed the other models, with an accuracy of 87 %. They suggest that data-driven approaches such as these could be used more effectively target and alleviate poverty.
Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. This work presents federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. Results showed that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.
In this work, the authors sought to provide an original experiment to investigate the conflict over whether males or females tend to exhibit greater accuracy or confidence in their memories. By using an online portal to obtain a convenience sample, the authors found that their results suggest that though males tend to be more confident regarding their memories, they may in fact remember fewer details. The authors suggest that these findings merit further research before making systematic changes regarding crime scene recall settings.
