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The authors looked the ability to predict future stock prices using various machine learning models.

The authors looked at how storage temperature of potatoes can impact the sweetness of potatoes (due to the break down of the starch into simple sugars).

The authors looked at the ability to use graphite from pencils in anodes of lithium-anode batteries.

The authors looked at fertilizer derived from coffee ground tea leaves, measuring the effectiveness by measuring the height, weight, and number of leaves on basil plants.

The authors evaluated the effect of concentration and variant of N-Acetylcysteine in hydrolyzing mucus.

The authors created a 1D model to diagnose hepatomegaly and cirrhosis via ultrasound of the liver.

Using machine learning to predict the risk of algae bloom

Here the authors aimed to compare the effects of artificial Allura Red AC dye and natural cochineal dye on the growth of Escherichia coli and Bacillus coagulans bacteria. Their research found that only Allura Red AC dye significantly affected bacterial growth, specifically amplifying E. coli growth. Based on their results, they suggest that Allura Red AC dye may increase the growth of E. coli bacteria within the human gut.

Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.

Impact of carbon tax and dividend on financial security