With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.
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Understanding the battleground of identity fraud
The authors looked at variables associated with identity fraud in the US. They found that national unemployment rate and online banking usage are among significant variables that explain identity fraud.
Read More...A comparative study of dynamic scoring formulas for capture-the-flag competitions
The use of gamification in cybersecurity education, particularly through capture-the-flag competitions, involves scoring challenges based on their difficulty and the number of teams that solve them. The study investigated how changing the scoring formulas affects competition outcomes, predicting that different formulas would alter score distributions.
Read More...Can the attributes of an app predict its rating?
In this article the authors looked at different attributes of apps within the Google Play store to determine how those may impact the overall app rating out of five stars. They found that review count, amount of storage needed and when the app was last updated to be the most influential factors on an app's rating.
Read More...Evaluating the predicted eruption times of geysers in Yellowstone National Park
The authors compare the predicted versus actual geyser eruption times for the Old Faithful and Beehive Geysers at Yellowstone National Park.
Read More...Addressing and Resolving Biases in Artificial Intelligence
The authors explore how diversity in data sets contributes to bias in artificial intelligence.
Read More...Effects of material advantage and space advantage on the Komodo and Stockfish chess engines
Chess engines, or computer programs built to play chess, outperform even the best human players. Kaushikan and Park investigate the inner workings of these chess engines by studying popular chess engines' evaluations of which side of a chess match is most likely to win, and how this is affected by the number of pieces and controlled squares on each side.
Read More...Prediction of diabetes using supervised classification
The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.
Read More...Genetic algorithm based features selection for predicting the unemployment rate of India
The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.
Read More...Predicting baseball pitcher efficacy using physical pitch characteristics
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.
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