Browse Articles

The authors looked at the ability of a convolutional neural network (CNN) to sort contaminated recycling, with varying levels of contamination. They found as contamination levels increased, the CNN faced more difficulty correctly classifying items.

The authors looked at the ability subjects to rotation a 4D cube and how the ability to practice cube rotation impact their ability to understand 4D space.

The author looked at how dance styles can vary by individuals, even between a student and their teacher.

The authors looked at what conditions with host stars favor development of exoplanets.

Analysis of the Sloan green and red photometry of the Type Ia supernova 2024neh

The authors looked at the ability of different machine learning algorithms to predict the level of financial corruption in different countries.

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

Algorithmic trading has been increasingly used by Americans. In this work, we tested whether including the opening, closing, and highest prices in three supervised learning models affected their performance. Indeed, we found that including all three prices decreased the error of the prediction significantly.

The authors looked at the relationship between unemployment and minimum wage in Turkey (Türkiye). They found that there is a positive correlation between minimum wage and unemployment.

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.