scholarly journals Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms

2021 ◽  
Vol 69 (1) ◽  
pp. 24-28
Author(s):  
Swarajya Lakshmi V Papineni ◽  
Snigdha Yarlagadda ◽  
Harita Akkineni ◽  
Mallikarjuna Reddy A
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Multicriteria Decision Making ◽  
Multicriteria Decision ◽  
Fusion Approach

scholarly journals Deep Learning Algorithms and Multicriteria Decision-Making Used in Big Data: A Systematic Literature Review

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Mei Yang ◽  
Shah Nazir ◽  
Qingshan Xu ◽  
Shaukat Ali
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Multicriteria Decision Making ◽  
Literature Study ◽  
Multicriteria Decision ◽  
Challenging Tasks ◽  
Critical Issues ◽  
Different Shapes

The data are ever increasing with the increase in population, communication of different devices in networks, Internet of Things, sensors, actuators, and so on. This increase goes into different shapes such as volume, velocity, variety, veracity, and value extracting meaningful information and insights, all are challenging tasks and burning issues. Decision-making based on multicriteria is one of the most critical issues solving ways to select the most suitable decision among a number of alternatives. Deep learning algorithms and multicriteria-based decision-making have effective applications in big data. Derivations are made based on the use of deep algorithms and multicriteria. Due to its effectiveness and potentiality, it is exploited in several domains such as computer science and information technology, agriculture, and business sector. The aim of the proposed study is to present a systematic literature study in order to show the applications of deep learning algorithms and multicriteria decision approaches for the problems of big data. The research finds novel means to make the decision support system for the problems of big data using multiple criteria in integration with machine learning and artificial intelligence approaches.

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 Fuzzy VIKOR approach for selection of big data analyst in procurement management

2016 ◽  
Vol 10 (1) ◽  
Author(s):  
Surajit Bag
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Big Data Analytics ◽  
Multicriteria Decision Making ◽  
Human Resource Managers ◽  
Multicriteria Decision ◽  
Fuzzy Vikor ◽  
Data Analyst ◽  
Business Acumen ◽  
The Right

Background: Big data and predictive analysis have been hailed as the fourth paradigm of science. Big data and analytics are critical to the future of business sustainability. The demand for data scientists is increasing with the dynamic nature of businesses, thus making it indispensable to manage big data, derive meaningful results and interpret management decisions.Objectives: The purpose of this study was to provide a brief conceptual review of big data and analytics and further illustrate the use of a multicriteria decision-making technique in selecting the right skilled candidate for big data and analytics in procurement management.Method: It is important for firms to select and recruit the right data analyst, both in terms of skills sets and scope of analysis. The nature of such a problem is complex and multicriteria decision-making, which deals with both qualitative and quantitative factors. In the current study, an application of the Fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method was used to solve the big data analyst selection problem.Results: From this study, it was identified that Technical knowledge (C1), Intellectual curiosity (C4) and Business acumen (C5) are the strongest influential criteria and must be present in the candidate for the big data and analytics job.Conclusion: Fuzzy VIKOR is the perfect technique in this kind of multiple criteria decisionmaking problematic scenario. This study will assist human resource managers and procurement managers in selecting the right workforce for big data analytics.

Efficiently Processing Big Data in Real-Time Employing Deep Learning Algorithms

2018 ◽  
pp. 61-78 ◽  
Author(s):  
Murad Khan ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
High Time ◽  
Digital Data ◽  
Learning Mechanisms ◽  
Digital World

Big Data and deep computation are among the buzzwords in the present sophisticated digital world. Big Data has emerged with the expeditious growth of digital data. This chapter addresses the problem of employing deep learning algorithms in Big Data analytics. Unlike the traditional algorithms, this chapter comes up with various solutions to employ advanced deep learning mechanisms with less complexity and finally present a generic solution. The deep learning algorithms require less time to process the big amount of data based on different contexts. However, collecting the accurate feature and classifying the context into patterns using neural networks algorithms require high time and complexity. Therefore, using deep learning algorithms in integration with neural networks can bring optimize solutions. Consequently, the aim of this chapter is to provide an overview of how the advance deep learning algorithms can be used to solve various existing challenges in Big Data analytics.

Efficiently Processing Big Data in Real-Time Employing Deep Learning Algorithms

2020 ◽  
pp. 1344-1357
Author(s):  
Murad Khan ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
High Time ◽  
Digital Data ◽  
Learning Mechanisms ◽  
Digital World

Big Data and deep computation are among the buzzwords in the present sophisticated digital world. Big Data has emerged with the expeditious growth of digital data. This chapter addresses the problem of employing deep learning algorithms in Big Data analytics. Unlike the traditional algorithms, this chapter comes up with various solutions to employ advanced deep learning mechanisms with less complexity and finally present a generic solution. The deep learning algorithms require less time to process the big amount of data based on different contexts. However, collecting the accurate feature and classifying the context into patterns using neural networks algorithms require high time and complexity. Therefore, using deep learning algorithms in integration with neural networks can bring optimize solutions. Consequently, the aim of this chapter is to provide an overview of how the advance deep learning algorithms can be used to solve various existing challenges in 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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