The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.
Read More...Prediction of diabetes using supervised classification
The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.
Read More...A novel CNN-based machine learning approach to identify skin cancers
In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.
Read More...The Effects of Antioxidants on the Climbing Abilities of Drosophila melanogaster Exposed to Dental Resin
Dental resins can be a source of reactive oxygen species (ROS) which in unruly amounts can be toxic to cellular and overall health. In this report, the authors test whether the consumption of antioxidant rich foods like avocado and asparagus can protect against the effect of dental resin-derived ROS. However, rather than testing humans, they use fruit flies and their climbing abilities as an experimental readout.
Read More...The Effects of Altered Microbiome on Caenorhabditis elegans Egg Laying Behavior
Since the discovery that thousands of different bacteria colonize our gut, many of which are important for human wellbeing, understanding the significance of balancing the different species on the human body has been intensely researched. Untangling the complexity of the gut microbiome and establishing the effect of the various strains on human health is a challenge in many circumstances, and the need for simpler systems to improve our basic understanding of microbe-host interactions seems necessary. C. elegans are a well-established laboratory animal that feed on bacteria and can thus serve as a less complex system for studying microbe-host interactions. Here the authors investigate how the choice of bacterial diet affects worm fertility. The same approach could be applied to many different outcomes, and facilitate our understanding of how the microbes colonizing our guts affect various bodily functions.
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...Trajectories Between Cigarette Smoking and Electronic Nicotine Delivery System Use Among Adults in the U.S.
In this study, the authors characterized the trends of cigarette use amongst people who do and don't use electronic nicotine delivery systems (or ENDS). This was done to help determine if the use of ENDS is aiding in helping smokers quit, as the data on this has been controversial. They found that use of ENDS among people either with or without previous cigarette usage were more likely to continue using cigarettes in the future. This is important information contributing to our understanding of ways to effectively (and not effectively) reduce cigarette use.
Read More...Comparison of the ease of use and accuracy of two machine learning algorithms – forestry case study
Machine learning algorithms are becoming increasingly popular for data crunching across a vast area of scientific disciplines. Here, the authors compare two machine learning algorithms with respect to accuracy and user-friendliness and find that random forest algorithms outperform logistic regression when applied to the same dataset.
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...Exponential regression analysis of the Canadian Zero Emission Vehicle market’s effects on climate emissions in 2030
Here, the authors explored how the sale and use of electric vehicles could reduce emissions from the transport industry in Canada. By fitting the sale of total of electric vehicles with an exponential model, the authors predicted the number of electric vehicle sales through 2030 and related that to the average emission for such vehicles. Ultimately, they found that the sale and use of electric vehicles alone would likely not meet the 45% reduction in emissions from the transport industry suggested by the Canadian government
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.
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