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.

This study examined the relationship between the slope of a terrain and vegetation, measured by the normalized difference vegetation index (NDVI). It was hypothesized that lower slope ranges would be more supportive of vegetation growth than higher slope ranges. Analysis showed that no slope (even as extreme as 85–90°) prohibits the growth of vegetation completely; even the steepest slopes examined contain plant life. Knowing that steep slopes can still support plant life, agriculturalists can begin to explore and start planting additional crops and plants at these extreme slopes.

Reinforcement learning (RL) is a form of machine learning that can be harnessed to develop artificial intelligence by exposing the intelligence to multiple generations of data. The study demonstrates how reply buffer reward mechanics can inform the creation of new pruning methods to improve RL efficiency.

Heating of DNA-containing solutions is a part of many experiment protocols, but it can also cause damage and degradation of the DNA molecules, potentially leading to error in the experimental results. The authors of this paper investigate whether the presence of certain cations during heating can stabilize the DNA polymer and aid the preservation of the molecule.

Chemical pollution can have significant effects on freshwater organisms. In this study, the effect of copper sulfate on the survival of Daphnia pulex and Ostracoda was investigated.

In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.

Here the authors test the ability of the bacterium Bacillus subtilis to degrade the polyethylene from plastic waste in various aquatic environments. They determined that degradation can occur among all samples while it was the highest in fresh water and lowest in ocean water.

In this article, the authors identify the characteristics that make a book a best-seller. Knowing what, besides content, predicts the success of a book can help publishers maximize the success of their print products.

Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO₂ to other useful carbon products. This research used computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO₂. In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO₂ emissions.

Household detergents have surfactants that can potentially harm the soil and broader ecosystems. In this study, the authors investigate whether eco-friendly and less-eco-friendly detergents affect soil pH, phosphorus, nitrogen, and potassium levels.