The authors identify disinfectant-resistant bacterial strains of infection-causing bacteria from samples collected at a hospital setting.
Read More...Varying levels of disinfectant resistance among invasive Klebsiella pneumoniae isolates
The authors identify disinfectant-resistant bacterial strains of infection-causing bacteria from samples collected at a hospital setting.
Read More...Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease
Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Using existing voice data from patients with PD and healthy controls, the authors created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN).
Read More...A novel CNN-based machine learning approach to identify skin cancers
In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.
Read More...Bacterial Richness of Soil Samples from Southern New Hampshire
Advancement in DNA sequencing technology has greatly increased our understanding about the role of bacteria in soil. The authors of this study examine the microbial content of soil samples taken from three locations in southern New Hampshire with varying pH and plant composition.
Read More...The effect of activation function choice on the performance of convolutional neural networks
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.
Read More...Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Networks
In this study the authors develop an app for faster chess game entry method to help chess learners improve their game. This culminated in the Augmented Reality Chess Analyzer (ARChessAnalyzer) which uses traditional image and vision techniques for chess board recognition and Convolutional Neural Networks (CNN) for chess piece recognition.
Read More...Differential privacy in machine learning for traffic forecasting
In this paper, we measured the privacy budgets and utilities of different differentially private mechanisms combined with different machine learning models that forecast traffic congestion at future timestamps. We expected the ANNs combined with the Staircase mechanism to perform the best with every value in the privacy budget range, especially with the medium high values of the privacy budget. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and neural network models to forecast and then added differentially private Laplacian, Gaussian, and Staircase noise to our datasets. We tested two real traffic congestion datasets, experimented with the different models, and examined their utility for different privacy budgets. We found that a favorable combination for this application was neural networks with the Staircase mechanism. Our findings identify the optimal models when dealing with tricky time series forecasting and can be used in non-traffic applications like disease tracking and population growth.
Read More...Tomato disease identification with shallow convolutional neural networks
Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.
Read More...Predicting the Instance of Breast Cancer within Patients using a Convolutional Neural Network
Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.
Read More...Identifying Neural Networks that Implement a Simple Spatial Concept
Modern artificial neural networks have been remarkably successful in various applications, from speech recognition to computer vision. However, it remains less clear whether they can implement abstract concepts, which are essential to generalization and understanding. To address this problem, the authors investigated the above vs. below task, a simple concept-based task that honeybees can solve, using a conventional neural network. They found that networks achieved 100% test accuracy when a visual target was presented below a black bar, however only 50% test accuracy when a visual target was presented below a reference shape.
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