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Do Initial Strategies or Choice of Piece Color Lead to Advantages in Chess Games?

Ponnaluri et al. | Feb 07, 2017

Do Initial Strategies or Choice of Piece Color Lead to Advantages in Chess Games?

White pieces make the first move in chess games, and there are several opening strategies and consequent defense strategies that white and black pieces, respectively, can take . The author of this paper investigated whether taking a specific opening and defense strategy, as well as playing as white vs. black, can increase the chances of winning the game, by playing against various human and computer opponents.

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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.

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Does technology help or hurt learning? Evidence from middle school and high school students

Lu et al. | Oct 02, 2022

Does technology help or hurt learning? Evidence from middle school and high school students

Here, recognizing the vastly different opinion held regarding device usage, the authors considered the effects of technology use on middle and high school students' learning effectiveness. Using an anonymous online survey they found partial support that device use at school increases learning effectiveness, but found strong support for a negative effect of technology use at home on learning effectiveness. Based on their findings they suggest that the efficacy of technology depends on environmental context along with other important factors that need consideration.

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Propagation of representation bias in machine learning

Dass-Vattam et al. | Jun 10, 2021

Propagation of representation bias in machine learning

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

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The Effects of Confinement on the Associative Learning of Gallus gallus domesticus

Jaworsky et al. | Dec 23, 2019

The Effects of Confinement on the Associative Learning of <em>Gallus gallus domesticus</em>

This study aimed to determine if confinement affects associative learning in chickens. The research found that the difference in time lapsed before chickens began to consume cottage cheese before and after confinement was significant. These results suggest that confinement distresses chickens, as it impairs associative learning without inducing confusion.

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