Wavelet neural network for big data analytics in banking via GPU

2021 ◽  
pp. 273-284
Author(s):  
Satish Doppalapudi ◽  
Vadlamani Ravi
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Wavelet Neural Network

scholarly journals Crime Data Forecasting Using Machine Learning and Big Data Analytics

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 591-606
Author(s):  
R. Brindha ◽  
Dr.M. Thillaikarasi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Crime Data

Big data analytics (BDA) is a system based method with an aim to recognize and examine different designs, patterns and trends under the big dataset. In this paper, BDA is used to visualize and trends the prediction where exploratory data analysis examines the crime data. “A successive facts and patterns have been taken in following cities of California, Washington and Florida by using statistical analysis and visualization”. The predictive result gives the performance using Keras Prophet Model, LSTM and neural network models followed by prophet model which are the existing methods used to find the crime data under BDA technique. But the crime actions increases day by day which is greater task for the people to overcome the challenging crime activities. Some ignored the essential rate of influential aspects. To overcome these challenging problems of big data, many studies have been developed with limited one or two features. “This paper introduces a big data introduces to analyze the influential aspects about the crime incidents, and examine it on New York City. The proposed structure relates the dynamic machine learning algorithms and geographical information system (GIS) to consider the contiguous reasons of crime data. Recursive feature elimination (RFE) is used to select the optimum characteristic data. Exploitation of gradient boost decision tree (GBDT), logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) are related to develop the optimum data model. Significant impact features were then reviewed by applying GBDT and GIS”. The experimental results illustrates that GBDT along with GIS model combination can identify the crime ranking with high performance and accuracy compared to existing method.”

scholarly journals A Hybrid Big Data Analytics and Industry 4.0 Approach for Projecting The Cycle Time Range

2021 ◽  
Author(s):  
Toly Chen ◽  
Yu Cheng Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Cycle Time ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Deep Neural Network ◽  
Big Data Analytics ◽  
Time Range ◽  
Joint Application ◽  
Multiple Experts

Abstract To enhance the effectiveness of projecting the cycle time range of a job in a factory, a hybrid big data analytics and Industry 4.0 (BD-I4) approach is proposed in this study. As a joint application of big data analytics and Industry 4.0, the BD-I4 approach is distinct from existing methods in this field. In the BD-I4 approach, first, each expert constructs a fuzzy deep neural network (FDNN) to project the cycle time range of a job, which is an application of big data analytics (i.e., deep learning). Subsequently, fuzzy weighted intersection (FWI) is applied to aggregate the cycle time ranges projected by experts to consider their unequal authority levels, which is an application of Industry 4.0 (i.e., artificial intelligence). After applying the BD-I4 approach to a real case, the experimental results showed that the proposed methodology improved the projection precision by up to 72%. This result implied that instead of relying on a single expert, seeking the collaboration among multiple experts may be more effective and efficient.

Back propagation neural network based big data analytics for a stock market challenge

2018 ◽  
Vol 48 (14) ◽  
pp. 3622-3642 ◽  
Author(s):  
V. P. Ramesh ◽  
Priyanga Baskaran ◽  
Aarthika Krishnamoorthy ◽  
Divya Damodaran ◽  
Preethi Sadasivam
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Stock Market ◽  
Data Analytics ◽  
Back Propagation ◽  
Big Data Analytics ◽  
Back Propagation Neural Network

scholarly journals An effective classification approach for big data with parallel generalized Hebbian algorithm

2021 ◽  
Vol 10 (6) ◽  
pp. 3393-3402
Author(s):  
Ahmed Hussein Ali ◽  
Royida A. Ibrahem Alhayali ◽  
Mostafa Abdulghafoor Mohammed ◽  
Tole Sutikno
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Principal Component ◽  
Reduction Technique ◽  
Data Generation ◽  
Classification Approach ◽  
Processing Techniques ◽  
Existing Data

Advancements in information technology is contributing to the excessive rate of big data generation recently. Big data refers to datasets that are huge in volume and consumes much time and space to process and transmit using the available resources. Big data also covers data with unstructured and structured formats. Many agencies are currently subscribing to research on big data analytics owing to the failure of the existing data processing techniques to handle the rate at which big data is generated. This paper presents an efficient classification and reduction technique for big data based on parallel generalized Hebbian algorithm (GHA) which is one of the commonly used principal component analysis (PCA) neural network (NN) learning algorithms. The new method proposed in this study was compared to the existing methods to demonstrate its capabilities in reducing the dimensionality of big data. The proposed method in this paper is implemented using Spark Radoop platform.

scholarly journals The Prediction of Diseases using Rough Set Theory with Recurrent Neural Network in Big Data Analytics

2020 ◽  
Vol 13 (5) ◽  
pp. 10-18
Author(s):  
Vamsidhar Talasila ◽  
◽  
Kotakonda Madhubabu ◽  
Meghana Mahadasyam ◽  
Naga Atchala ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Set Theory ◽  
Rough Set ◽  
Recurrent Neural Network ◽  
Data Analytics ◽  
Rough Set Theory ◽  
Big Data Analytics

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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