Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms
Citation
MLA Style: Swarajya Lakshmi V Papineni, Snigdha Yarlagadda, Harita Akkineni, A. Mallikarjuna Reddy "Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms" International Journal of Engineering Trends and Technology 69.1(2021):24-28.
APA Style:Swarajya Lakshmi V Papineni, Snigdha Yarlagadda, Harita Akkineni, A. Mallikarjuna Reddy. Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms International Journal of Engineering Trends and Technology, 69(1), 24-28.
Abstract
Data is evolving with the rapid progress of population and communication for various types of devices such as networks, cloud computing, Internet of Things (IoT), actuators, and sensors. The increment of data and communication content goes with the equivalence of velocity, speed, size, and value to provide the useful and meaningful knowledge that helps to solve the future challenging tasks and latest issues. Besides, multicriteria based decision making is one of the key issues to solve for various issues related to the alternative effects in big data analysis. It tends to find a solution based on the latest machine learning techniques that include algorithms like decision making and deep learning mechanism based on multicriteria in providing insights to big data. On the other hand, the derivations are made for it to go with the approximations to increase the duality of runtime and improve the entire system`s potentiality and efficacy. In essence, several fields, including business, agriculture, information technology, and computer science, use deep learning and multicriteria-based decision-making problems. This paper aims to provide various applications that involve the concepts of deep learning techniques and exploiting the multicriteria approaches for issues that are facing in big data analytics by proposing new studies with the fusion approaches of data-driven techniques.
Reference
[1] Z. Miao and L. Fan,A novel multi-agent decision making architecture based on dual’s dual problem formulation, IEEE Transactions on Smart Grid, 99(2016) 1–1.
[2] M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, Network anomaly detection: Methods, systems and tools, IEEE Commun. Surv. Tutor., 16(1)(2014) 303–336.
[3] M. Weiten, Ontostudio® as an ontology engineering environment,in Semantic knowledge management. Springer, (2009) 51–60.
[4] N. M. Meenachi and M. S. Baba,Web ontology language editors for the semantic web-a survey, International Journal of Computer Applications, 53(2012) 12.
[5] R. Klapsing, G. Neumann, and W. Conen,Semantics in web engineering: applying the resource description framework,IEEE MultiMedia, 8(2)(2001) 62–68.
[6] K. R. Kurte, S. S. Durbha, R. L. King, N. H. Younan, and R. Vatsavai, Semantics-enabled framework for spatial image information mining of linked earth observation data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(1)(2017) 29–44.
[7] S. BOU, H. KITAGAWA, and T. AMAGASA, Cbix: Incremental sliding-window aggregation for real-time analytics over out-of-order data streams,DEIM Forum, F7-5,(2018)
[8] U. Fiore, F. Palmieri, A. Castiglione, and A. De Santis, Network anomaly detection with the restricted boltzmann machine, Neurocomputing, 122(2013) 13–23.
[9] J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, 61(2015) 85–117.
[10] R. Vijayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, Deep learning approach for an intelligent intrusion detection system, IEEE Access,7,(2019) 41525–41550.
[11] S. M. Kasongo and Y. X. Sun, A deep learning method with filter-based feature engineering for a wireless intrusion detection system, IEEE Access, 7(2019) 38597–38607.
[12] W. Shu and H. Shen,Multi-criteria feature selection on cost-sensitive data with missing values, Pattern Recognition, 51(2016) 268–280.
[13] K. Vougas, T. Sakellaropoulos, A. Kotsinas, G.-R. P. Foukas, and V. G. Gorgoulis,Machine learning and data mining frameworks for predicting drug response in cancer: an overview and a novel in silico screening process based on association rule mining, Pharmacology &Rerapeutics, 203,Article ID 107395, (2019).
[14] Mallikarjuna Reddy, A., Rupa Kinnera, G., Chandrasekhara Reddy, T., Vishnu Murthy, G., et al., (2019), Generating cancelable fingerprint template using triangular structures, Journal of Computational and Theoretical Nanoscience, 16(5) 5-6 1951-1955, doi: https://doi.org/10.1166/jctn.2019.7830.
[15] A.Mallikarjuna, B. Karuna Sree, et al, Security towards Flooding Attacks in Inter-Domain Routing Object using Ad hoc Network International Journal of Engineering and Advanced Technology (IJEAT), 8(3)(2019) 545-547.
[16] A Mallikarjuna Reddy, Vakulabharanam Venkata Krishna, Lingamgunta Sumalatha and Avuku Obulesh, Age Classification Using Motif and Statistical Feature Derived On Gradient Facial Images, Recent Advances in Computer Science and Communications (2020) 13:965. https://doi.org/10.2174/2213275912666190417151247
[17] Srinivasa Reddy, K., Suneela, B., Inthiyaz, S., Kumar, G.N.S., Mallikarjuna Reddy, A.Texture filtration module under stabilization via random forest optimization methodology ,International Journal of Advanced Trends in Computer Science and Engineering, 8(3)(2019).
[18] A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0,” Information Fusion, 50(2019) 92–111.
[19] Mallikarjuna Reddy, A., Venkata Krishna, V. and Sumalatha, L. Face recognition approaches: A survey. International Journal of Engineering and Technology (UAE), 4.6,6(7)(2018) 117-121. doi: 10.14419/ijet.v7i4.6.20446.
[20] C. R. T, G. Sirisha and A. M. Reddy, Smart Healthcare Analysis and Therapy for Voice Disorder using Cloud and Edge Computing, 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Mangalore, India, (2018) 103-106.doi: 10.1109/iCATccT44854.2018.9001280.
[21] I. M. Cavalcante, E. M. Frazzon, F. A. Forcellini, and D. Ivanov, A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing, International Journal of Information Management, 49(2019) 86–97.
[22] Y. Liu, Y. Qian, Y. Jiang, and J. Shang,Using favorite data to analyze asymmetric competition: machine learning models, European Journal of Operational Research, 287(2)(2020) 600–615.
Keywords
Big data, deep learning algorithm, data-driven approach, machine learning, multiple criteria decision-making.