Multi-product Newsvendor Model in Multi-task Deep Neural Network with Norm Regularization for Big Data

2019 ◽  
pp. 183-205
Author(s):  
Yanfei Zhang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Neural Network ◽  
Newsvendor Model

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

Parallel Deep Neural Network Training for Big Data on Blue Gene/Q

2017 ◽  
Vol 28 (6) ◽  
pp. 1703-1714 ◽  
Author(s):  
I-Hsin Chung ◽  
Tara N. Sainath ◽  
Bhuvana Ramabhadran ◽  
Michael Picheny ◽  
John Gunnels ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Neural Network ◽  
Neural Network Training ◽  
Network Training ◽  
Blue Gene

scholarly journals Deep learning-assisted comparative analysis of animal trajectories with DeepHL

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Takuya Maekawa ◽  
Kazuya Ohara ◽  
Yizhe Zhang ◽  
Matasaburo Fukutomi ◽  
Sakiko Matsumoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Animal Behavior ◽  
Deep Neural Network ◽  
Animal Movement ◽  
Group Differences ◽  
Movement Data ◽  
Contrast Group

Abstract A comparative analysis of animal behavior (e.g., male vs. female groups) has been widely used to elucidate behavior specific to one group since pre-Darwinian times. However, big data generated by new sensing technologies, e.g., GPS, makes it difficult for them to contrast group differences manually. This study introduces DeepHL, a deep learning-assisted platform for the comparative analysis of animal movement data, i.e., trajectories. This software uses a deep neural network based on an attention mechanism to automatically detect segments in trajectories that are characteristic of one group. It then highlights these segments in visualized trajectories, enabling biologists to focus on these segments, and helps them reveal the underlying meaning of the highlighted segments to facilitate formulating new hypotheses. We tested the platform on a variety of trajectories of worms, insects, mice, bears, and seabirds across a scale from millimeters to hundreds of kilometers, revealing new movement features of these animals.

Machine Intelligence to Assist Visually Impaired People: A Big Data Deep Neural Network Adventure

2018 ◽  
Author(s):  
Fereshteh S. Bashiri ◽  
Eric Larose ◽  
Jonathan Badger ◽  
Zeyun Yu ◽  
Peggy Peissig ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Visually Impaired ◽  
Deep Neural Network ◽  
Machine Intelligence ◽  
Visually Impaired People ◽  
Impaired People

scholarly journals A Novel DBSCAN Clustering Algorithm via Edge Computing-Based Deep Neural Network Model for Targeted Poverty Alleviation Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Liu ◽  
Yang Liu ◽  
Zhenquan Qin ◽  
Ran Zhang ◽  
Zheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Poverty Alleviation ◽  
Deep Neural Network ◽  
Clustering Algorithm ◽  
Edge Computing ◽  
Dbscan Clustering ◽  
The Poor ◽  
Poor Households ◽  
Big Data Technology

Big data technology has been developed rapidly in recent years. The performance improvement mechanism of targeted poverty alleviation is studied through the big data technology to further promote the comprehensive application of big data technology in poverty alleviation and development. Using the data mining knowledge to accurately identify the poor population under the framework of big data, compared with the traditional identification method, it is obviously more accurate and persuasive, which is also helpful to find out the real causes of poverty and assist the poor residents in the future. In the current targeted poverty alleviation work, the identification of poor households and the matching of assistance measures are mainly through the visiting of village cadres and the establishment of documents. Traditional methods are time-consuming, laborious, and difficult to manage. It always omits lots of useful family information. Therefore, new technologies need to be introduced to realize intelligent identification of poverty-stricken households and reduce labor costs. In this paper, we introduce a novel DBSCAN clustering algorithm via the edge computing-based deep neural network model for targeted poverty alleviation. First, we deploy an edge computing-based deep neural network model. Then, in this constructed model, we execute data mining for the poverty-stricken family. In this paper, the DBSCAN clustering algorithm is used to excavate the poverty features of the poor households and complete the intelligent identification of the poor households. In view of the current situation of high-dimensional and large-volume poverty alleviation data, the algorithm uses the relative density difference of grid to divide the data space into regions with different densities and adopts the DBSCAN algorithm to cluster the above result, which improves the accuracy of DBSCAN. This avoids the need for DBSCAN to traverse all data when searching for density connections. Finally, the proposed method is utilized for analyzing and mining the poverty alleviation data. The average accuracy is more than 96%. The average F -measure, NMI, and PRE values exceed 90%. The results show that it provides decision support for precise matching and intelligent pairing of village cadres in poverty alleviation work.

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