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The authors looked at how students performed on standardized tests when they were taught material via memorization vs. conceptual based approaches.

Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.

The authors look at how different food types impact the ability of bacteria to produce electricity.

The global issue of water quality has led to the use of machine learning models, like ANN and SVM, to predict water potability. However, these models can be complex and resource-intensive. This research aimed to find a simpler, more efficient model for water quality prediction.

Although the 5-year survival rate for colorectal cancer is below 10%, it increases to greater than 90% if it is diagnosed early. We hypothesized from our research that analyzing non-synonymous single nucleotide variants (SNVs) in a patient's exome sequence would be an indicator for high genetic risk of developing colorectal cancer.

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.

Here, the authors investigated the ability to use fruit peels to help soil retain moisture, a property that is essential to agriculture. Across a 96-hour observation period, orange, banana, and kiwi peel water emulsions were evaluated for their effects on soil moisture. They found that orange peels retained the most moisture, but banana and kiwi peels also offered improvements over their control sample.

The authors developed and tested machine learning methods to diagnose tuberculosis from pulmonary X-ray scans.

This study is centered around developing biofortification methods: the authors test whether the amount of calcium available to growing crops translates into more calcium present in the crops.

The authors use computational methods to compare tau acetylation to the better studied tau phosphorylation in Alzheimer's disease and then design and computationally test a new drug to prevent abnormal post-translational modifications of tau.