658 506
Full Length Article
Journal of Intelligent Systems and Internet of Things
Volume 9 , Issue 1, PP: 49-68 , 2023 | Cite this article as | XML | Html |PDF

Title

Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing

  A. Madhuri 1 * ,   Veerapaneni Esther Jyothi 2 ,   S. Phani Praveen 3 ,   Mustafa Altaee 4 ,   Ibrahim N. Abdullah 5

1  Department of CSE, PVPSIT, Andhra Pradesh, India
    (madhuria@pvpsiddhartha.ac.in)

2  Department of Computer Applications, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India
    (vejyothi@vrsiddhartha.ac.in)

3  Department of CSE, PVPSIT, Andhra Pradesh, India
    (phani.0713@gmail.com)

4  Department of Medical instruments engineering techniques, Alfarahidi University, Baghdad, Iraq
    (m.altaee@alfarahidiuc.edu.iq)

5  Department of computer science, Al-turath University College, Baghdad, Iraq
    (Ibrahim.n@turath.edu.iq)


Doi   :   https://doi.org/10.54216/JISIoT.090104

Received: January 19, 2023 Revised: April 07, 2023 Accepted: June 08, 2023

Abstract :

Natural computation, motivated by the organic game arrangement, is a knowledge base field that formalizes the measurements seen in living organic entities to plan machine techniques to tackle complicated issues or to plan artificial structures with additional traditional behaviors. Seeable corporations disconnected from natural wonders, reminiscent of mind demonstration, self-association, self-redundancy, Darwinian resistance, self-evaluation, discernment, and granulation, nature is crammed as a supply of motivation to advance competition. Computational devices or frameworks accustomed solve complex problems. The ideal, nature-motivated primary computation models used for such sweetening incorporate artificial neural organizations, spongy reasoning, arduous set, biological process calculations, shape mathematics, DNA registration, artificial life, And granular or insight-based processing. The granulation of information within the granular register is an innate attribute of human thought and therefore the life of thought acted call at regular daily existence. This paper illustrates the importance of normal recording in terms of granulation-based data preparation models, for example, neural organizations, soft and ugly sets, and their hybridization. we have a tendency to emphasize the bio-sensitive inspiration, designing standards, application zones, open scan problems, and testing issues for these models.

Keywords :

Natural Computing; Granular Processing; Redundancy; Evaluation; Granulation-Based Data Fusion Approach.

References :

[1]          S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice Hall, NJ, USA, 1998.

[2]          L.N. de Castro, J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer, 2002.

[3]          J. Kennedy, R. Eberhart, Y. Shi, Swarm Intelligence, Morgan Kaufmann, 2001.

[4]          D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.

[5]          T. Sienko, A. Adamatzky, N.G. Rambidi, M. Conrad (Eds.), Molecular Computing, MIT Press, USA, 2003.

[6]          G. Paun, G. Rozenberg, A. Salomaa, DN.A. Computing, New Computing Paradigms, Springer-Verlag, Germany, 1998.

[7]          Praveen, S. P., Murali Krishna, T. B., Anuradha, C. H., Mandalapu, S. R., Sarala, P., & Sindhura, S. (2022). A robust framework for handling health care information based on machine learning and big data engineering techniques. International Journal of Healthcare Management, 1-18.

[8]          B. Maclennan, Natural computation and non-turing models of computation,Theoretical Computer Science 317 (2004) 115–145.

[9]          L.N. de Castro, Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications, Chapman and Hall/CRC Computer and Information Science Series, 2006.

[10]        L.N. de Castro, Fundamentals of natural computing: an overview, Physics of Life Reviews 4 (2007) 1–36.

[11]        P.J. Denning, Computing is a natural science, Communications of the ACM 50 (2007) 13–18.

[12]        L. Kari, G. Rozenberg, The many facets of natural computing, Communications of the ACM 51 (2008) 72–83.

[13]        D.E. Shasha, C. Lazere, Natural Computing: DNA, Quantum Bits, and the Future of Smart Machines, W.W. Norton and Company, 2010.

[14]        L.A. Zadeh, Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems 90 (1997) 111–127.

[15]        W. Pedrycz, A. Skowron, V. Kreinovich, Handbook of Granular Computing, Wiley-Interscience, 2008.

[16]        Y.Y. Yao, A partition model of granular computing, LNCS Transactions on Rough Sets 1 (2004) 232–253.

[17]        T.Y. Lin, Divide and conquer in granular computing topological partitions, in: 2005 Annual Meeting of the North American Fuzzy Info. Processing Society, 2005, pp. 282–285.

[18]        Y. Yao, Perspectives of granular computing, in: Proc. of IEEE Int. Conf. on Granular Computing, 2005, pp. 85–90. 

[19]        L.A. Zadeh, Fuzzy sets, Infection Control 8 (1965) 338–353.

[20]        S. Donepudi, S. C. Palagani, P. S. N. Pramod, Y. R. Kumar, S. Karthikeya and S. P. Praveen, "Brain Metastasis Tumor Detection using Image Segmentation and VGG16 Architecture," 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2023, pp. 150-155, doi: 10.1109/ICSSIT55814.2023.10061029.

[21]        N. R. Sai, B. S. Chandana, S. P. Praveen, S. S. Kumar and M. J. kumar, "Improving Performance of IDS by using Feature Selection with IG-R," 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2021, pp. 1-8, doi: 10.1109/I-SMAC52330.2021.9640749..

[22]        S.K. Pal,A. Skowron(Eds.),Rough-FuzzyHybridization:ANew Trendin Decision Making, Springer-Verlag, Singapore, 1999.

[23]        S.K. Pal, J.F. Peters, Rough Fuzzy Image Analysis: Foundations and Methodologies, Chapman and Hall/CRC, Boca Raton, FL, 2010.

[24]        P. Maji, S.K. Pal, Rough-Fuzzy Pattern Recognition: Application in Bioinformatics and Medical Imaging, Wiley-IEEE Computer Society Press, 2012.

[25]        Sindhura, S., Phani Praveen, S., Madhuri, A., & Swapna, D. (2022, May). Different feature selection methods performance analysis for intrusion detection. In Smart Intelligent Computing and Applications, Volume 2: Proceedings of Fifth International Conference on Smart Computing and Informatics (SCI 2021) (pp. 523-531). Singapore: Springer Nature Singapore.

[26]        S.K. Pal, S.K. Meher, S. Dutta, Class-dependent rough-fuzzy granular space, dispersion index and classification, Pattern Recognition 45 (2012) 2690– 2707.

[27]        R.E. Bellman, R. Kalaba, L.A. Zadeh, Abstraction and pattern classification, Journal of Mathematical Analysis and Applications 13 (1966) 1–7.

[28]        S. Sindhura, S. P. Praveen, M. A. Safali and N. Rao, "Sentiment Analysis for Product Reviews Based on Weakly-Supervised Deep Embedding," 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 999-1004, doi: 10.1109/ICIRCA51532.2021.9544985.

[29]        Swamy, S. R., Praveen, S. P., Ahmed, S., Srinivasu, P. N., & Alhumam, A. (2023). Multi-features disease analysis based smart diagnosis for covid-19. Computer Systems Science and Engineering, 869-886.

[30]        Satapathy, S. C., Bhateja, V., & Das, S. (Eds.). (2018). Smart Intelligent Computing and Applications: Proceedings of the Second International Conference on SCI 2018, Volume 2 (Vol. 105). Springer.

[31]        L. Polkowski, S. Tsumoto, T.Y. Lin (Eds.), Rough Set Methods and Applications, Physica Verlag, Heidelberg, 2001.

[32]        ZM. Muthumari, V. Akash, K. P. Charan, P. Akhil, V. Deepak and S. P. Praveen, "Smart and Multi-Way Attendance Tracking System Using an Image-Processing Technique," 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2022, pp. 1805-1812, doi: 10.1109/ICSSIT53264.2022.9716349.

[33]        Y. Qian, J. Liang, Y. Yao, C. Dang, MGRS: A multi-granulation rough set, Information Sciences 180 (2010) 949–970.

[34]        X. Yang, X. Song, H. Dou, J. yang, Multigranulation rough set: from crisp to fuzzy case, Annals of Fuzzy Mathematics and Informatics 1 (2011) 55–70.

[35]        W. Pedrycz, G.Vukovich, Granular neural networks, Neurocomputing 36 (2001) 205–224.

[36]        A. Vasilakos, D. Stathakis, Granular neural networks for land use classification, Soft Computing 9 (2005) 323–340.

[37]        Y.Q. Zhang, B. Jin, Y. Tang, Granular neural networks with evolutionary interval learning, IEEE Transactions on Fuzzy Systems 16 (2008) 309–319.

[38]        S.K. Pal, L. Polkowski, A. Skowron (Eds.), Rough-neural computing: techniques for computing with words, Springer-Verlag, Berlin, 2004.

[39]        S.K. Pal, S. Mitra, Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, Wiley-Interscience, NJ, USA, 1999.

[40]        S.K. Pal, A. Ghosh, Neuro-fuzzy computing for image processing and pattern recognition, International Journal of Systems Science 27 (1996) 1179–1193.

[41]        A. Ghosh, B. Uma Shankar, S.K. Meher, A novel approach to neuro-fuzzy classification, Neural Networks 22 (2009) 100–109.

[42]        P. Lingras, R. Jensen, Survey of rough and fuzzy hybridization, in: Proceedings of the IEEE International Conference on Fuzzy Systems, 2007, pp. 1–6.

[43]        D. Sen, S.K. Pal, Generalized rough sets, entropy and image ambiguity measures, IEEE Transactions on Systems, Man, and Cybernetics, Part B 39 (2009) 117–128.

[44]        S.K. Pal, P. Mitra, Case generation using rough sets with fuzzy representation, IEEE Transactions on Knowledge and Data Engineering 16 (2004) 293–300.  

[45]        M. Jogendra Kumar, S. Phani Praveen, K. Raju Tella, R. Vijaya Kumar Reddy and N. Raghavendra Sai, "Examination Of Diabetes Mellitus For Ahead Of Schedule Expectation Utilizing Ideal Highlights Determination," 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2021, pp. 1-6, doi: 10.1109/I-SMAC52330.2021.9641001.

[46]        P. Maji, S.K. Pal, Rough set based generalized fuzzy c-means algorithm and quantitative indices, IEEE Trans. Syst, Man, and Cybernetics, Part B 37 (2007) 1529–1540.

[47]        M. Banerjee, S. Mitra, S. Pal, Rough fuzzy MLP: Knowledge encoding and classification, IEEE Transactions on Neural Networks 9 (1998) 1203–1216.

[48]        S.K. Pal, S. Mitra, P. Mitra, M.L.P. Rough-fuzzy, Modular evolution, rule generation, and evaluation,IEEE Transactions onKnowledge and Data and Engineering 15 (2003) 14–25.

[49]        Algamal, Z. Y., Abonazel, M. R., & Lukman, A. F. (2023). Modified Jackknife Ridge Estimator for Beta Regression Model With Application to Chemical Data. International Journal of Mathematics, Statistics, and Computer Science, 1, 15–24. https://doi.org/10.59543/ijmscs.v1i.7713

 


Cite this Article as :
Style #
MLA A. Madhuri, Veerapaneni Esther Jyothi , S. Phani Praveen, Mustafa Altaee, Ibrahim N. Abdullah. "Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing." Journal of Intelligent Systems and Internet of Things, Vol. 9, No. 1, 2023 ,PP. 49-68 (Doi   :  https://doi.org/10.54216/JISIoT.090104)
APA A. Madhuri, Veerapaneni Esther Jyothi , S. Phani Praveen, Mustafa Altaee, Ibrahim N. Abdullah. (2023). Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing. Journal of Journal of Intelligent Systems and Internet of Things, 9 ( 1 ), 49-68 (Doi   :  https://doi.org/10.54216/JISIoT.090104)
Chicago A. Madhuri, Veerapaneni Esther Jyothi , S. Phani Praveen, Mustafa Altaee, Ibrahim N. Abdullah. "Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing." Journal of Journal of Intelligent Systems and Internet of Things, 9 no. 1 (2023): 49-68 (Doi   :  https://doi.org/10.54216/JISIoT.090104)
Harvard A. Madhuri, Veerapaneni Esther Jyothi , S. Phani Praveen, Mustafa Altaee, Ibrahim N. Abdullah. (2023). Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing. Journal of Journal of Intelligent Systems and Internet of Things, 9 ( 1 ), 49-68 (Doi   :  https://doi.org/10.54216/JISIoT.090104)
Vancouver A. Madhuri, Veerapaneni Esther Jyothi , S. Phani Praveen, Mustafa Altaee, Ibrahim N. Abdullah. Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing. Journal of Journal of Intelligent Systems and Internet of Things, (2023); 9 ( 1 ): 49-68 (Doi   :  https://doi.org/10.54216/JISIoT.090104)
IEEE A. Madhuri, Veerapaneni Esther Jyothi, S. Phani Praveen, Mustafa Altaee, Ibrahim N. Abdullah, Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 9 , No. 1 , (2023) : 49-68 (Doi   :  https://doi.org/10.54216/JISIoT.090104)