
The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.
Read More...Epileptic seizure detection using machine learning on electroencephalogram data
The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.
Read More...Analysis of Technology Usage of Teens: Correlating Social Media, Technology Use, Participation in Sports, and Popularity
Social media usage is predicted to impact teen well-being and emotional status. This study sought to assess the impact of teen technology usage on their social lives. Surveys of 8th and 9th graders were used to assess compare technology usage between males and females as well as and how social media usage impacts the perception of social environment at school.
Read More...The Effects of Knowledge, Lack of Knowledge, and Deception on Rate of Perceived Exertion and Performance During Workouts
In this study, the authors examine how knowledge, lack of knowledge, and deception affect the rate of perceived exertion and actual performance of teenagers in sprint training. Their results suggest that fully informing athletes about workout duration yields the fastest and most consistent speeds.
Read More...Comparing Consumer Personality and Brand Personality: Do Fashion Styles Speak of Who You Are?
This study investigated how fashion brand personalities are similar to people’s personalities and whether people may prefer a particular clothing brand based on their own personal traits. All together, Stevenson and Scott found that the Big Five Personality Factors are generally not related to participants’ preferred brand personalities. Generally, brands should consider different factors besides the Big Five Personality Factors for identifying potential customers.
Read More...Testing Simarouba amara’s therapeutic effects against weedicide-induced tumor-like morphology in planarians
According to the World Health Organization, cancer is a leading cause of death globally. The disease’s prevalence is rapidly increasing in association with factors including the increased use of pesticides and herbicides, such as glyphosate, which is one of the most widely used herbicide ingredients. Natural antioxidants and phytochemicals are being tested as anti-cancer agents due to their antiproliferative, antioxidative, and pro-apoptotic properties. Thus, we aimed to investigate the potential role of S. amara extract as a therapeutic agent against glyphosate-induced toxicity and tumor-like morphologies in regenerating and homeostatic planaria (Dugesia dorotocephala).
Read More...Exploring the effects of diverse historical stock price data on the accuracy of stock price prediction 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.
Read More...Determining the relationship between unemployment and minimum wage in Turkey
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.
Read More...Transfer learning and data augmentation in osteosarcoma cancer detection
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.
Read More...Demographic indicators of voter shift between 2016 and 2020 presidential elections
In this study, the authors investigate the demographic indicators for voter shift between the 2016 and 2020 presidential elections based on demographic data put through a K-nearest neighbors classification algorithm and Principal Component Analysis.
Read More...A Novel Model to Predict a Book's Success in the New York Times Best Sellers List
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.
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