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Here the authors investigate the potential of the bioluminescent fungus Panellus stipticus to serve as a sustainable bioindicator for environmental lead contamination. Their findings demonstrate that higher lead concentrations cause a measurable decrease in fungal bioluminescence intensity over time suggesting that the fungus could be an effective tool for detecting lead in an environment.

The authors looked at how vocabulary is impacted in Alzheimer's disease and whether it could be used a predictor of disease onset.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

The authors propose a method to help first responders find the location of a person within a high-rise building in densely populated areas.

In this study, the authors investigate the effects of acetone on the color of copper chloride (CuCl₂) solution, which has important implications for detecting copper in the environment.

In order to determine whether unmarked soybeans in the market were genetically modified crops, the authors developed a polymerase chain reaction (PCR) screen for DNA lectin.

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 develop a method for detecting fake AI-generated images from real images.

The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.

Zoos offer educational and scientific advantages but face high maintenance costs and challenges in animal care due to diverse species' habits. Challenges include tracking animals, detecting illnesses, and creating suitable habitats. We developed a deep learning framework called SmartZoo to address these issues and enable efficient animal monitoring, condition alerts, and data aggregation. We discovered that the data generated by our model is closer to real data than random data, and we were able to demonstrate that the model excels at generating data that resembles real-world data.