Improved Big Data Analytics Solution Using Deep Learning Model and Real-Time Sentiment Data Analysis Approach

With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.

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Product Decision-Making Information Systems, Real-Time Big Data Analytics, and Deep Learning-enabled Smart Process Planning in Sustainable Industry 4.0

Journal of Self-Governance and Management Economics ◽

10.22381/jsme8320202 ◽

2020 ◽

Vol 8 (3) ◽

pp. 16 ◽

Cited By ~ 2

Keyword(s):

Decision Making ◽

Big Data ◽

Deep Learning ◽

Information Systems ◽

Real Time ◽

Process Planning ◽

Industry 4.0 ◽

Data Analytics ◽

Big Data Analytics ◽

Product Decision

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Deep Learning Security Systems

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1347.0986s319 ◽

2019 ◽

Vol 8 (6S3) ◽

pp. 1823-1826

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analysis ◽

Data Analytics ◽

Big Data Analytics ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Security Systems ◽

Abstract Knowledge

Big Data Analytics and Deep Learning are not supposed to be two entirely different concepts. Big Data means extremely huge large data sets that can be analyzed to find patterns, trends. One technique that can be used for data analysis so that able to help us find abstract patterns in Big Data is Deep Learning. If we apply Deep Learning to Big Data, we can find unknown and useful patterns that were impossible so far. With the help of Deep Learning, AI is getting smart. There is a hypothesis in this regard, the more data, the more abstract knowledge. So a handy survey of Big Data, Deep Learning and its application in Big Data is necessary.

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DADEM

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2021010105 ◽

2021 ◽

Vol 12 (1) ◽

pp. 114-139

Author(s):

Hassan I. Ahmed ◽

Abdurrahman A. Nasr ◽

Salah M. Abdel-Mageid ◽

Heba K. Aslan

Keyword(s):

Big Data ◽

Deep Learning ◽

Classification Accuracy ◽

Distributed Processing ◽

Big Data Analytics ◽

Learning Model ◽

Attack Detection ◽

Smart Devices ◽

Detection Model ◽

Deep Learning Model

Nowadays, Internet of Things (IoT) is considered as part our lives and it includes different aspects - from wearable devices to smart devices used in military applications. IoT connects a variety of devices and as such, the generated data is considered as ‘Big Data'. There has however been an increase in attacks in this era of IoT since IoT carries crucial information regarding banking, environmental, geographical, medical, and other aspects of the daily lives of humans. In this paper, a Distributed Attack Detection Model (DADEM) that combines two techniques - Deep Learning and Big Data analytics - is proposed. Sequential Deep Learning model is chosen as a classification engine for the distributed processing model after testing its classification accuracy against other classification algorithms like logistic regression, KNN, ID3 decision tree, CART, and SVM. Results showed that Sequential Deep Learning model outperforms the aforementioned ones. The classification accuracy of DADEM approaches 99.64% and 99.98% for the UNSW-NB15 and BoT-IoT datasets, respectively. Moreover, a plan is proposed for optimizing the proposed model to reduce the overhead of the overall system operation in a constrained environment like IoT.

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