Financial Market Prediction and Improving the Performance Based on Large-scale Exogenous Variables and Deep Neural Networks

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

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Forecasting Reservoir Water Levels Using Deep Neural Networks: A Case Study of Angat Dam in the Philippines

Water ◽

10.3390/w14010034 ◽

2021 ◽

Vol 14 (1) ◽

pp. 34

Author(s):

Sebastian C. Ibañez ◽

Carlo Vincienzo G. Dajac ◽

Marissa P. Liponhay ◽

Erika Fille T. Legara ◽

Jon Michael H. Esteban ◽

...

Keyword(s):

Neural Networks ◽

Time Series ◽

Deep Neural Networks ◽

Cross Validation ◽

The Philippines ◽

Water Levels ◽

Short Term ◽

Exogenous Variables ◽

Reservoir Water ◽

Forecasting Performance

Forecasting reservoir water levels is essential in water supply management, impacting both operations and intervention strategies. This paper examines the short-term and long-term forecasting performance of several statistical and machine learning-based methods for predicting the water levels of the Angat Dam in the Philippines. A total of six forecasting methods are compared: naïve/persistence; seasonal mean; autoregressive integrated moving average (ARIMA); gradient boosting machines (GBM); and two deep neural networks (DNN) using a long short-term memory-based (LSTM) encoder-decoder architecture: a univariate model (DNN-U) and a multivariate model (DNN-M). Daily historical water levels from 2001 to 2021 are used in predicting future water levels. In addition, we include meteorological data (rainfall and the Oceanic Niño Index) and irrigation data as exogenous variables. To evaluate the forecast accuracy of our methods, we use a time series cross-validation approach to establish a more robust estimate of the error statistics. Our results show that our DNN-U model has the best accuracy in the 1-day-ahead scenario with a mean absolute error (MAE) and root mean square error (RMSE) of 0.2 m. In the 30-day-, 90-day-, and 180-day-ahead scenarios, the DNN-M shows the best performance with MAE (RMSE) scores of 2.9 (3.3), 5.1 (6.0), and 6.7 (8.1) meters, respectively. Additionally, we demonstrate that further improvements in performance are possible by scanning over all possible combinations of the exogenous variables and only using a subset of them as features. In summary, we provide a comprehensive framework for evaluating water level forecasting by defining a baseline accuracy, analyzing performance across multiple prediction horizons, using time series cross-validation to assess accuracy and uncertainty, and examining the effects of exogenous variables on forecasting performance. In the process, our work addresses several notable gaps in the methodologies of previous works.

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Extensive deep neural networks for transferring small scale learning to large scale systems

Chemical Science ◽

10.1039/c8sc04578j ◽

2019 ◽

Vol 10 (15) ◽

pp. 4129-4140 ◽

Cited By ~ 9

Author(s):

Kyle Mills ◽

Kevin Ryczko ◽

Iryna Luchak ◽

Adam Domurad ◽

Chris Beeler ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Large Scale ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Small Scale ◽

Large Scale Systems ◽

Energy Entropy ◽

Large Systems ◽

Number Of Particles

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with scaling.

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Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2019.108311 ◽

2019 ◽

Vol 324 ◽

pp. 108311 ◽

Cited By ~ 2

Author(s):

Prasanna Parvathaneni ◽

Vishwesh Nath ◽

Maureen McHugo ◽

Yuankai Huo ◽

Susan M. Resnick ◽

...

Keyword(s):

Neural Networks ◽

Large Scale ◽

Deep Neural Networks ◽

Cross Sectional

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MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks

BMC Bioinformatics ◽

10.1186/s12859-020-03783-0 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Shisheng Wang ◽

Hongwen Zhu ◽

Hu Zhou ◽

Jingqiu Cheng ◽

Hao Yang

Keyword(s):

Mass Spectrometry ◽

Neural Networks ◽

Large Scale ◽

Deep Neural Networks ◽

Spectral Feature ◽

Mass Spectrometry Data ◽

Learning Approaches ◽

Proteomics Data ◽

Proteome Profiling ◽

Analytical Technique

Abstract Background Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algorithms by using partial MS spectra to compare with the sequence database, while the pattern recognition and classification of raw mass-spectrometric data remain unresolved. Results We developed an open-source and comprehensive platform, named MSpectraAI, for analyzing large-scale MS data through deep neural networks (DNNs); this system involves spectral-feature swath extraction, classification, and visualization. Moreover, this platform allows users to create their own DNN model by using Keras. To evaluate this tool, we collected the publicly available proteomics datasets of six tumor types (a total of 7,997,805 mass spectra) from the ProteomeXchange consortium and classified the samples based on the spectra profiling. The results suggest that MSpectraAI can distinguish different types of samples based on the fingerprint spectrum and achieve better prediction accuracy in MS1 level (average 0.967). Conclusion This study deciphers proteome profiling of raw mass spectrometry data and broadens the promising application of the classification and prediction of proteomics data from multi-tumor samples using deep learning methods. MSpectraAI also shows a better performance compared to the other classical machine learning approaches.

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