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

2020 ◽  
Vol 8 (3) ◽  
pp. 16 ◽  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Information Systems ◽  
Real Time ◽  
Process Planning ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Product Decision

Survey of Deep-learning Techniques in Big-Data analytics

2021 ◽  
Author(s):  
Shubhashish Goswami ◽  
Abhimanyu Kumar
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Learning Process ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Operational Parameters ◽  
Learning Techniques ◽  
The Impact ◽  
Business Sectors

Abstract The present elaboration of Big-data research studies relying upon Deep-learning methods had revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms' operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. The present enhancements in the Deep-learning approaches in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition to having more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper elaborates the above statements stating the impact of the Deep-learning process utilizing the Big-data to operate in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing on having the impacts of operational parameters, concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors, the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

scholarly journals Deep Learning Techniques to Address Issues in Data Quality and Data Variety

2019 ◽  
Vol 8 (4) ◽  
pp. 5950-5956
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Data Quality ◽  
Data Analytics ◽  
Medical Information ◽  
Big Data Analytics ◽  
Training Data ◽  
Huge Amount ◽  
Learning Techniques

Deep Learning and Big Data Analytics are key focus in current rapidly growing environment. The use of large data has become crucial to different organizations as they collecting huge amount of domain-specific data, which contains critical information about cyber security, theft detection, national resources, business economics, marketing, and medical information. The assessment of this huge amount of data needs advanced and improved analytical techniques for surveying and guessing future courses of action by making advanced decision-making strategies. Deep learning algorithms utilize the collected training data, to create a representation model. This model uses the computer for predictions or decision making about new data without needing to train the machine explicitly to perform user task. These techniques and algorithms infer greater level complicated abstractions as data are represented through tree like structure. A major use of Deep Learning is processing, learning and training from the huge amounts of unsupervised data, analyze patterns from the data and can be used for large Datasets in which the raw data is largely unlabeled and not classified. In this paper, Deep Learning techniques for addressing Data of different variety/formats is analyzed, enabling fast and full processing and integration of large amounts of different variety of information i.e. Data transformation is also addressed. It also addresses the quality of data as the performances of a machine improve depending on the data quality. Further exploration on the deep learning techniques to assist Big Data by focusing on two key topics: (1) is it possible for Deep Learning to assist some of the specific problems like Data Variety and Data Quality in Big Data Analytics, and (2) Whether these techniques can aid in processing the Big Data

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