Deep learning multilayer perceptron (MLP) for flood prediction model using wireless sensor network based hydrology time series data mining

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

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Context-based Hydrology Time Series Data for A Flood Prediction Model Using LSTM

2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) ◽

10.1109/icitacee.2018.8576900 ◽

2018 ◽

Cited By ~ 3

Author(s):

Indrastanti R. Widiasari ◽

Lukito Edi Nugoho ◽

Widyawan ◽

Rissal Efendi

Keyword(s):

Time Series ◽

Prediction Model ◽

Time Series Data ◽

Series Data ◽

Flood Prediction

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Flood prediction using Time Series Data Mining

Journal of Hydrology ◽

10.1016/j.jhydrol.2006.09.001 ◽

2007 ◽

Vol 333 (2-4) ◽

pp. 305-316 ◽

Cited By ~ 34

Author(s):

Chaitanya Damle ◽

Ali Yalcin

Keyword(s):

Data Mining ◽

Time Series ◽

Time Series Data ◽

Series Data ◽

Flood Prediction ◽

Time Series Data Mining

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A deep-learning prediction model for imbalanced time series data forecasting

Big Data Mining and Analytics ◽

10.26599/bdma.2021.9020011 ◽

2021 ◽

Vol 4 (4) ◽

pp. 266-278

Author(s):

Chenyu Hou ◽

Jiawei Wu ◽

Bin Cao ◽

Jing Fan

Keyword(s):

Time Series ◽

Deep Learning ◽

Prediction Model ◽

Time Series Data ◽

Series Data

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Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature

Sensors ◽

10.3390/s17061221 ◽

2017 ◽

Vol 17 (6) ◽

pp. 1221 ◽

Cited By ~ 10

Author(s):

Siddhartha Bhandari ◽

Neil Bergmann ◽

Raja Jurdak ◽

Branislav Kusy

Keyword(s):

Time Series ◽

Data Analysis ◽

Wireless Sensor Network ◽

Sensor Network ◽

Time Series Data ◽

Wireless Sensor ◽

Series Data ◽

Network Measurements ◽

Time Series Data Analysis

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Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽

10.1515/phys-2021-0043 ◽

2021 ◽

Vol 19 (1) ◽

pp. 360-374

Author(s):

Yuan Pei ◽

Lei Zhenglin ◽

Zeng Qinghui ◽

Wu Yixiao ◽

Lu Yanli ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Load Forecasting ◽

Series Data ◽

Forecasting Model ◽

Combined Model ◽

Forecasting Accuracy ◽

Forecasting Method ◽

Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1582-2 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 2

Author(s):

Shadi Aljawarneh ◽

Aurea Anguera ◽

John William Atwood ◽

Juan A. Lara ◽

David Lizcano

Keyword(s):

Data Mining ◽

Time Series ◽

Knowledge Discovery ◽

Time Series Data ◽

Medical Patient ◽

Lessons Learned ◽

Physiological Signals ◽

Knowledge Discovery In Databases ◽

Series Data ◽

Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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