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Machine learning is a field that has been around for decades whose impact and presence continues to increase across scientific and commercial communities. However, until recently, machine learning has not been thought of as a viable methodology that could significantly aid novel material discovery and design. That is, machine learning-aided material design and/or discovery is an emerging research topic, but one that holds immense potential. Such a system could, theoretically, be used to discover novel materials or surfaces that possess desirable properties across the electromagnetic spectrum under specific conditions. Herein, we present our preliminary machine learning- based framework for novel material design and discovery. We emphasize that our proposed framework is in its infancy; however, it is laying the foundation for the discovery of fundamental theories and knowledge for this novel technology. Baseline elementary experiments are presented as a proof-of-concept to show the feasibility of our proposed framework for the task of material design. Specifically, we put forth a multi-stage machine learning framework for new material discovery considering material surface geometries for predicting object signatures in the X band. Our proposed multi-stage framework is structured as follows: 1) a deep neural network (NN) is trained for predicting the time response scattered from an object based upon surface geometries (micro-feature spacing, height, etc.); and 2) a genetic algorithm is used to search for the optimal surface geometry configuration whose predicted scattered response (closely) matches that of a desired object response in the X band.

In recent years, many financial institutions have implemented machine learning applications to better predict stock prices. Computational methods such as regression or classification learners give financial analysts the ability to predict a stock’s future price using historical data. More recently, sentiment analysis has been used to gather public opinion in hopes that it will provide meaningful information which would allow more accurate prediction models. In this work, we use both historical data and sentiment analysis scores to investigate whether sentiment analysis can improve performance of stock forecasting.

Feature extraction is widely considered one of the most critical components to classification performance in computer vision. In the past, human-designed features, such as the histogram of oriented gradients, were used for extracting statistically rich features. Recently, there has been a movement away from human-designed features to machine-learned features. Herein, we propose a novel genetic programming (GP) approach, coined GOOFeD, to automatically generate discriminative-rich features for image classification. This is achieved by greatly advancing GP in three ways: (1) promoting population diversity and redundancy removal, (2) introducing a unique adaptive mutation approach, and (3) controlling tree bloat through a new crossover technique. These improvements also lead to a population size required for learning that is smaller than that commonly used in the literature. To assess performance, GOOFeD is tested on the MIT urban and nature scene data set and a real-world buried explosive hazard data set. Experiments verify that, in terms of classification accuracy, GOOFeD outperforms many of the state-of-the-art human-designed features and feature learning techniques.

Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising., Comment: 6 pages, 4 figures, published in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)