Landsat Satellite Image Segmentation Using the Fuzzy ARTMAP Neural Network

This application illustrates how the fuzzy ARTMAP neural network can be used to monitor environmental changes. A benchmark problem seeks to classify regions of a Landsat image into six soil and crop classes based on images from four spectral sensors. Simulations show that fuzzy ARTMAP outperforms fourteen other neural network and machine learning algorithms. Only the k-Nearest-Neighbor algorithm shows better performance (91% vs. 89%) but without any code compression, while fuzzy ARTMAP achieves a code compression ratio of 6:1. Even with a code compression ratio of 50:1 fuzzy ARTMAP still maintains good performance (83%). This example shows how fuzzy ARTMAP can combine accuracy and code compression in real-world applications.Office of Naval Research (N00014-92-J-401J, N00014-91-J-4100, N00014-92-J-4015); National Science Foundation (IRI 90-00530

Landsat Satellite Image Segmentation Using the Fuzzy ARTMAP Neural Network

This application illustrates how the fuzzy ARTMAP neural network can be used to monitor environmental changes. A benchmark problem seeks to classify regions of a Landsat image into six soil and crop classes based on images from four spectral sensors. Simulations show that fuzzy ARTMAP outperforms fourteen other neural network and machine learning algorithms. Only the k-Nearest-Neighbor algorithm shows better performance (91% vs. 89%) but without any code compression, while fuzzy ARTMAP achieves a code compression ratio of 6:1. Even with a code compression ratio of 50:1 fuzzy ARTMAP still maintains good performance (83%). This example shows how fuzzy ARTMAP can combine accuracy and code compression in real-world applications.Office of Naval Research (N00014-92-J-401J, N00014-91-J-4100, N00014-92-J-4015); National Science Foundation (IRI 90-00530

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.Comment: A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell, J.-Y. Nie, J. Gao, B. Dolan. 2015. A Neural Network Approach to Context-Sensitive Generation of Conversational Responses. In Proc. of NAACL-HLT. Pages 196-20

Content Based Image Retrieval by Convolutional Neural Networks

Hamreras S., Benítez-Rochel R., Boucheham B., Molina-Cabello M.A., López-Rubio E. (2019) Content Based Image Retrieval by Convolutional Neural Networks. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer.In this paper, we present a Convolutional Neural Network (CNN) for feature extraction in Content based Image Retrieval (CBIR). The proposed CNN aims at reducing the semantic gap between low level and high-level features. Thus, improving retrieval results. Our CNN is the result of a transfer learning technique using Alexnet pretrained network. It learns how to extract representative features from a learning database and then uses this knowledge in query feature extraction. Experimentations performed on Wang (Corel 1K) database show a significant improvement in terms of precision over the state of the art classic approaches.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Deep learning based pulse shape discrimination for germanium detectors

Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the identification of background events to reduce their background level and increase their sensitivity. We present a novel machine learning based method to recognize one of the most abundant classes of background events in these experiments. By combining a neural network for feature extraction with a smaller classification network, our method can be trained with only a small number of labeled events. To validate our method, we use signals from a broad-energy germanium detector irradiated with a $^{228}$Th gamma source. We find that it matches the performance of state-of-the-art algorithms commonly used for this detector type. However, it requires less tuning and calibration and shows potential to identify certain types of background events missed by other methods.Comment: Published in Eur. Phys. J. C. 9 pages, 10 figures, 3 table

A study of time-dependent CP-violating asymmetries in B0 --> J/psi K0S and B0 --> psi(2S) K0S decays

BABAR has studied the time dependent asymmetries in the the decays B0 -> J/psi K0S and B0 -> psi(2S) K0S in a data set of 9.0 fb^-1 taken at the Y(4S)resonance. In these channels we reconstruct 168 events of which 120 are flavor tagged and used in a likelihood fit where we determine sin2beta. The flavor of the other neutral $B$ mesons is tagged using information primarily from identified leptons and Kaons. A neural network is used to recover events without any clear Kaon or lepton signature. A preliminary result of sin2beta=0.12+/-0.37+/-0.09 is obtained.Comment: 17 pages, presented at the 7th International Conference on B-Physics at Hadron Machine