The authors compare nutritional content of foods found in Western versus Asian grocery stores to determine whether one cultural diet is healthier than the other.
Read More...Are Asian foods healthier than Western foods: Evidence collected from St. Louis area grocery stores
The authors compare nutritional content of foods found in Western versus Asian grocery stores to determine whether one cultural diet is healthier than the other.
Read More...The effects of different modes of vocalization and food consumption on the level of droplet transmission of bacteria
Microbial agents reposnsible for respiratory infections are often carried in spittle, which means they can be easily transmitted. Here, the authors investigate how likely certain activities are to spread microbes carried in spittle. They also investigate whether eating certain types of food might reduce the spread of spittle-borne bacteria too.
Read More...Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease
Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Using existing voice data from patients with PD and healthy controls, the authors created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN).
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...Nintendo Da Vinci: A Novel Control System to Improve Performance in Robotic-Assisted Surgery
Complications of robotic-assisted surgery are on the rise, partly due to surgeons not receiving proper training. Al-Akash and Al-Akash hypothesized Nintendo JoyCon controls would improve surgical performance compared to the FDA-approved Da Vinci Surgical System with two user groups (doctor and gamer). Their results show that implementing a Nintendo JoyCon control system is associated with improved surgical performance and learning rate compared to the Da Vinci Surgical System.
Read More...Propagation of representation bias in machine learning
Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.
Read More...Heat conduction: Mathematical modeling and experimental data
In this experiment, the authors modify the heat equation to account for imperfect insulation during heat transfer and compare it to experimental data to determine which is more accurate.
Read More...Caffeine: Does Drinking Coffee Alter Performance and RPE Levels of a Teenage Athlete in both Aerobic and Anaerobic Exercises?
Caffeine is widely consumed across the globe and is most appreciated for its effects as a stimulant. Here the authors investigate whether caffeine consumption affects performance during endurance or strength training. Their results suggest that caffeine consumption enhances endurance training, but not strength training.
Read More...Recognition of animal body parts via supervised learning
The application of machine learning techniques has facilitated the automatic annotation of behavior in video sequences, offering a promising approach for ethological studies by reducing the manual effort required for annotating each video frame. Nevertheless, before solely relying on machine-generated annotations, it is essential to evaluate the accuracy of these annotations to ensure their reliability and applicability. While it is conventionally accepted that there cannot be a perfect annotation, the degree of error associated with machine-generated annotations should be commensurate with the error between different human annotators. We hypothesized that machine learning supervised with adequate human annotations would be able to accurately predict body parts from video sequences. Here, we conducted a comparative analysis of the quality of annotations generated by humans and machines for the body parts of sheep during treadmill walking. For human annotation, two annotators manually labeled six body parts of sheep in 300 frames. To generate machine annotations, we employed the state-of-the-art pose-estimating library, DeepLabCut, which was trained using the frames annotated by human annotators. As expected, the human annotations demonstrated high consistency between annotators. Notably, the machine learning algorithm also generated accurate predictions, with errors comparable to those between humans. We also observed that abnormal annotations with a high error could be revised by introducing Kalman Filtering, which interpolates the trajectory of body parts over the time series, enhancing robustness. Our results suggest that conventional transfer learning methods can generate behavior annotations as accurate as those made by humans, presenting great potential for further research.
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...Search articles by title, author name, or tags