Browse Articles

Transfer learning and data augmentation in osteosarcoma cancer detection

Chu et al. | Jun 03, 2023

Transfer learning and data augmentation in osteosarcoma cancer detection
Image credit: Chu and Khan 2023

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...

Modeling and optimization of epidemiological control policies through reinforcement learning

Rao et al. | May 23, 2023

Modeling and optimization of epidemiological control policies through reinforcement learning

Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. Epidemiological models help propose pandemic control strategies based on non-pharmaceutical interventions such as social distancing, curfews, and lockdowns, reducing the economic impact of these restrictions. In this research, we utilized an epidemiological Susceptible, Exposed, Infected, Recovered, Deceased (SEIRD) model – a compartmental model for virtually simulating a pandemic day by day.

Read More...

The effect of floating plant on water purification: Comparison of the water purification capability of Water Hyacinth, Duckweed, and Azolla

Park et al. | Nov 21, 2020

The effect of floating plant on water purification: Comparison of the water purification capability of Water Hyacinth, Duckweed, and Azolla

Clean water is a necessity for every household, yet water pollution is a serious problem in many parts of the world and plays a major role in compromising water security in the 21st century. In this paper, the authors address the utility of several plants as natural water purifiers. They estimate the effectiveness of duckweed, hyacinth, and azolla in improving the quality of water from the Mithi river in India by measuring several metrics. They conclude that all three plants are effective in improving water quality, suggesting that these plants as eco-friendly options for water treatment.

Read More...

A comparative analysis of machine learning approaches for prediction of breast cancer

Nag et al. | May 11, 2021

A comparative analysis of machine learning approaches for prediction of breast cancer

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.

Read More...

Analysis of complement system gene expression and outcome across the subtypes of glioma

Mudda et al. | May 17, 2023

Analysis of complement system gene expression and outcome across the subtypes of glioma
Image credit: National Cancer Institute

Here the authors sought to better understand glioma, cancer that occurs in the glial cells of the brain with gene expression profile analysis. They considered the expression of complement system genes across the transcriptional and IDH-mutational subtypes of low-grade glioma and glioblastoma. Based on their results of their differential gene expression analysis, they found that outcomes vary across different glioma subtypes, with evidence suggesting that categorization of the transcriptional subtypes could help inform treatment by providing an expectation for treatment responses.

Read More...

Interaction of light with water under clear and algal bloom conditions

Ramesh et al. | Feb 01, 2024

Interaction of light with water under clear and algal bloom conditions
Image credit: Liz Harrell

Here, recognizing the potential harmful effects of algal blooms, the authors used satellite images to detect algal blooms in water bodies in Wyoming based on their reflectance of near infrared light. They found that remote monitoring in this way may provide a useful tool in providing early warning and advisories to people who may live in close proximity.

Read More...

Predicting baseball pitcher efficacy using physical pitch characteristics

Oberoi et al. | Jan 11, 2024

Predicting baseball pitcher efficacy using physical pitch characteristics
Image credit: Antoine Schibler

Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.

Read More...

Correlation between shutdowns and CO levels across the United States.

Gupta et al. | Dec 05, 2021

Correlation between shutdowns and CO levels across the United States.

Concerns regarding the rapid spread of Sars-CoV2 in early 2020 led company and local governmental officials in many states to ask people to work from home and avoid leaving their homes; measures commonly referred to as shutdowns. Here, the authors investigate how shutdowns affected carbon monoxide (CO) levels in 15 US states using publicly available data. Their results suggest that CO levels decreased as a result of these measures over the course of 2020, a trend which started to reverse after shutdowns ended.

Read More...

Anonymity Reduces Generosity in High School Students

Vargas-Guerrero et al. | Nov 25, 2019

Anonymity Reduces Generosity in High School Students

The disinterested willingness a person has for helping others is known as altruism. But is this willingness to help others dependent on external factors that make you more or less inclined to be generous? We hypothesized that generosity in adolescents would depend on external factors and that these factors would change the amount of help given. To evaluate altruism and generosity, we conducted non-anonymous and anonymous variations of the dictator game and ultimatum game experiments and explored the role of anonymity, fairness, and reciprocity in high school students.

Read More...

Search Articles

Search articles by title, author name, or tags

Clear all filters

Popular Tags

Browse by school level