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Journal articles on the topic 'Computing approach'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Anjaneyulu, P., and Mr S. Srinivasa Reddy. "Cloud Computing Adoption Approach towards Securing Data in Cloud Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 390–93. http://dx.doi.org/10.31142/ijtsrd12995.

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2

Panhwar, Muhammad Aamir. "Workflow-based Approach in Cloud Computing Environment." Journal of Advanced Research in Dynamical and Control Systems 12, no. 7 (July 20, 2020): 815–22. http://dx.doi.org/10.5373/jardcs/v12i7/20202066.

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3

Emmert-Streib, Frank. "Biostatistics: A Computing Approach." CHANCE 26, no. 4 (November 2013): 59–60. http://dx.doi.org/10.1080/09332480.2013.868760.

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4

William Hughes, P. "Biostatistics: a computing approach." Journal of Applied Statistics 39, no. 10 (October 2012): 2306. http://dx.doi.org/10.1080/02664763.2012.692545.

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5

Baba, Toshio, Yoshi-aki Shimada, Seiichiro Kawamura, Masanori Matoba, Toshikazu Fukushima, Shinichiro Fujii, Toshiki Nagano, Yasuhiro Katsumata, Nobuo Kochi, and Yasunori Kimura. "Proposal of disruptive computing (A computing-domain-oriented approach)." Japanese Journal of Applied Physics 59, no. 5 (April 27, 2020): 050503. http://dx.doi.org/10.35848/1347-4065/ab8577.

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6

Negi, Adhyayan, Gautam Sharma, and Ankit Mittal. "Green Computing: An Efficient Approach." International Journal of Computer Applications 179, no. 32 (April 17, 2018): 1–3. http://dx.doi.org/10.5120/ijca2018916715.

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7

Zauner, K. P., and M. Conrad. "Molecular approach to informal computing." Soft Computing 5, no. 1 (February 2001): 39–44. http://dx.doi.org/10.1007/s005000000064.

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8

GIORGADZE, G. "MONODROMY APPROACH TO QUANTUM COMPUTING." International Journal of Modern Physics B 16, no. 30 (November 30, 2002): 4593–605. http://dx.doi.org/10.1142/s0217979202014607.

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In this work, a gauge approach to quantum computing is considered. It is assumed that there exists a classical procedure for placing certain classical system in a state described by a holomorphic vector bundle with connection with logarithmic singularities. This bundle and its connection are constructed with the aid of unitary operators realizing the given algorithm using methods of the monodromic Riemann–Hilbert problem. Universality is understood in the sense that for any collection of unitary matrices there exists a connection with logarithmic singularities whose monodromy representation involves these matrices.

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9

Anderson, M., S. L. Anderson, and C. Armen. "An Approach to Computing Ethics." IEEE Intelligent Systems 21, no. 4 (July 2006): 56–63. http://dx.doi.org/10.1109/mis.2006.64.

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10

Huang, Ching-Huei, Horn-Yong Jan, Chun-Liang Lin, and Chia-Soon Lee. "System identification: DNA computing approach." ISA Transactions 48, no. 3 (July 2009): 254–63. http://dx.doi.org/10.1016/j.isatra.2009.01.006.

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11

Érdi, Péter. "Neurobiological approach to computing devices." Biosystems 21, no. 2 (January 1988): 125–33. http://dx.doi.org/10.1016/0303-2647(88)90005-6.

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12

Mathieu, R. G. "Top-down approach to computing." Computer 35, no. 1 (2002): 138–39. http://dx.doi.org/10.1109/2.976930.

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13

Förtsch, Michael, and Tobias M. Wintermantel. "Quantum computing – the photonic approach." PhotonicsViews 19, no. 6 (November 27, 2022): 35–37. http://dx.doi.org/10.1002/phvs.202200051.

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14

Joshi, Garima, and S. K. Verma. "Load Balancing Approach in Cloud Computing using Improvised Genetic Algorithm: A Soft Computing Approach." International Journal of Computer Applications 122, no. 9 (July 18, 2015): 24–28. http://dx.doi.org/10.5120/21729-4894.

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15

Manoj Kumar, P. K. "Analysis and Motivation for Green Computing, an Approach in India." Bonfring International Journal of Networking Technologies and Applications 3, no. 1 (April 30, 2016): 01–03. http://dx.doi.org/10.9756/bijnta.7138.

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16

AlNuaim, Abdullah, and Shakeel Ahmed. "Fog Computing: A Novel Approach to provide Security in Cloud Computing." Indian Journal of Science and Technology 11, no. 15 (April 1, 2018): 1–6. http://dx.doi.org/10.17485/ijst/2018/v11i15/119540.

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17

Biryaltsev, Eugeniy Vasiljevich, Marat Razifovich Galimov, Denis Evgenievich Demidov, and Aleksandr Mikhailovich Elizarov. "The platform approach to research and development using high-performance computing." Program Systems: Theory and Applications 10, no. 2 (2019): 93–119. http://dx.doi.org/10.25209/2079-3316-2019-10-2-93-119.

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In this paper, we analyze the prerequisites and substantiate the relevance for creating an open Internet platform that employs big data technologies, highperformance computing, and multilateral markets in a unified way. Conceived as an ecosystem for the development and use of applied software (including in the field of design and scientific research), the platform should reduce time/costs and improve the quality of software development for solving analytical problems arising in industrial enterprises, scientific research organizations, state bodies and private individuals. The article presents a working prototype of the platform using supercomputer technologies and desktop virtualization systems.

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18

White, Steve R., James E. Hanson, Ian Whalley, David M. Chess, Alla Segal, and Jeffrey O. Kephart. "Autonomic computing: Architectural approach and prototype." Integrated Computer-Aided Engineering 13, no. 2 (February 5, 2006): 173–88. http://dx.doi.org/10.3233/ica-2006-13206.

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19

Marosi, Attila Csaba, and Robert Lovas. "Defining volunteer computing: a formal approach." Computer Research and Modeling 7, no. 3 (June 2015): 565–71. http://dx.doi.org/10.20537/2076-7633-2015-7-3-565-571.

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20

Zhou, Bing. "A Logic Approach to Granular Computing." International Journal of Cognitive Informatics and Natural Intelligence 2, no. 2 (April 2008): 63–79. http://dx.doi.org/10.4018/jcini.2008040104.

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21

Patel, R. B., and Manpreet Singh. "Cluster Computing: A Mobile Code Approach." Journal of Computer Science 2, no. 10 (October 1, 2006): 798–806. http://dx.doi.org/10.3844/jcssp.2006.798.806.

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22

Ishikawa, Yutaka. "MPC++ approach to parallel computing environment." ACM SIGAPP Applied Computing Review 4, no. 1 (April 1996): 15–18. http://dx.doi.org/10.1145/240732.240738.

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23

Chafouk, Houcine, Ghaleb Hoblos, Nicolas Langlois, Serge Le Gonidec, and José Ragot. "Soft Computing Approach for Data Validation." Journal of Aerospace Computing, Information, and Communication 4, no. 1 (January 2007): 628–35. http://dx.doi.org/10.2514/1.20742.

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24

Petre, Luigia, Kaisa Sere, and Marina Waldén. "A Topological Approach to Distributed Computing." Electronic Notes in Theoretical Computer Science 28 (2000): 59–80. http://dx.doi.org/10.1016/s1571-0661(05)80630-9.

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25

Sands, David. "Computing With Contexts A simple approach." Electronic Notes in Theoretical Computer Science 10 (1998): 134–49. http://dx.doi.org/10.1016/s1571-0661(05)80694-2.

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26

Appavoo, Paramasiven. "The constructivist' approach to teaching computing." International Journal of Continuing Engineering Education and Life-Long Learning 20, no. 3/4/5 (2010): 407. http://dx.doi.org/10.1504/ijceell.2010.037055.

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27

Kalantari, Mohsen. "Spatial Cloud Computing: A Practical Approach." Journal of Spatial Science 60, no. 1 (January 2, 2015): 197–98. http://dx.doi.org/10.1080/14498596.2015.1011588.

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28

Li, Chunshien, Jyh-Yann Huang, and Chih-Ming Chen. "Soft computing approach to feature extraction." Fuzzy Sets and Systems 147, no. 1 (October 2004): 119–40. http://dx.doi.org/10.1016/s0165-0114(03)00229-x.

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29

Kobayashi, Satoshi. "Enumeration approach to computing chemical equilibria." Theoretical Computer Science 499 (August 2013): 51–87. http://dx.doi.org/10.1016/j.tcs.2013.05.017.

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30

Geru, I. I. "Non-traditional Approach to Quantum Computing." Applied Magnetic Resonance 45, no. 12 (October 17, 2014): 1427–39. http://dx.doi.org/10.1007/s00723-014-0594-7.

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31

Spyra, Grzegorz, William J. Buchanan, and Elias Ekonomou. "Sticky policies approach within cloud computing." Computers & Security 70 (September 2017): 366–75. http://dx.doi.org/10.1016/j.cose.2017.07.005.

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32

Karam, Nihad A. "Specific Approach for Computing Hubble's Constant." Journal of Al-Nahrain University Science 9, no. 2 (March 1, 2006): 90–98. http://dx.doi.org/10.22401/jnus.9.2.14.

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33

Nguyen, Son H., Andrzej Skowron, and Jaroslaw Stepaniuk. "Granular Computing: a Rough Set Approach." Computational Intelligence 17, no. 3 (August 2001): 514–44. http://dx.doi.org/10.1111/0824-7935.00161.

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34

Alexander, J. "A new approach to business computing." ITNOW 47, no. 2 (March 1, 2005): 20–21. http://dx.doi.org/10.1093/itnow/bwi035.

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35

Fatoorehchi, Hooman, Ivan Gutman, and Hossein Abolghasemi. "Computing graph energy: An alternative approach." Kragujevac Journal of Science, no. 36 (2014): 69–78. http://dx.doi.org/10.5937/kgjsci1436069f.

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36

Sukhwani, Bharat, Hong Min, Mathew Thoennes, Parijat Dube, Bernard Brezzo, Sameh Asaad, and Donna Eng Dillenberger. "Database Analytics: A Reconfigurable-Computing Approach." IEEE Micro 34, no. 1 (January 2014): 19–29. http://dx.doi.org/10.1109/mm.2013.107.

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37

Bastos, Ricardo M., Flávio M. de Oliveira, and José Palazzo M. de Oliveira. "Autonomic computing approach for resource allocation." Expert Systems with Applications 28, no. 1 (January 2005): 9–19. http://dx.doi.org/10.1016/j.eswa.2004.08.014.

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38

Narayana Samy, Ganthan, Nurazean Maarop, Doris Hooi-Ten Wong, Fiza Abdul Rahim, Noor Hafizah Hassan, Sundresan Perumal, Pritheega Magalingam, and Sameer Hasan Albakri. "Proposed Proactive Digital Forensic Approach for Cloud Computing Environment." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 12. http://dx.doi.org/10.14419/ijet.v7i4.15.21362.

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There are many challenges for the digital forensic process in the cloud computing due to the distinguished features of the cloud computing environment. Many of well-known digital forensic methods and tools are not suitable for cloud computing environment. The multi-tenancy, multi-stakeholder, Internet-based, dynamics expendability, and massive data, logs and datasets are examples of the cloud computing environment features that make conducting digital forensics in the cloud computing environment a very difficult task. Therefore, there is a need to develop an appropriate digital forensic approach for cloud computing environment. Thus, this paper proposed a proactive digital forensic approach for cloud computing environment.

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39

Yadav, Mahendra Pratap, Gaurav Raj, Harishchandra A. Akarte, and Dharmendra Kumar Yadav. "Horizontal Scaling for Containerized Application Using Hybrid Approach." Ingénierie des systèmes d information 25, no. 6 (December 31, 2020): 709–18. http://dx.doi.org/10.18280/isi.250601.

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Cloud computing is a paradigm to provide services to end-users through the Internet. The availability of services to end-users is dependent on various factors such as the availability of computing resources as well as the number of users to access those services. To manage the real-time fluctuating workload cloud providers use elasticity mechanisms. Elasticity is one of the important characteristics of cloud computing that dynamically allocates computing resources to manage the fluctuating workload. The failure of allocation/de-allocation of computing resources at the right moment leads to SLA violation, degradation of services performance, maximum power consumption, minimum throughput, and maximum response time. To address these challenges, we have proposed a hybrid approach to perform horizontal elasticity. The proposed approach uses both reactive and proactive approaches for provisioning/de-provisioning of computing resources. The simulation results of the proposed model show that performance of system has improved in terms of CPU utilization, response time, and throughput.

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40

Ghanbari, Shamsollah. "PRIORITY-AWARE JOB SCHEDULING ALGORITHM IN CLOUD COMPUTING: A MULTI-CRITERIA APPROACH." Azerbaijan Journal of High Performance Computing 2, no. 1 (June 30, 2019): 29–38. http://dx.doi.org/10.32010/26166127.2019.2.1.29.38.

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41

N. M. Mallika, and B. Srinivasan. "An Efficient Approach for Data Security and Integrity in Multi Cloud Computing." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, no. 01 (February 9, 2017): 06–12. http://dx.doi.org/10.9756/sijcsea/v5i1/05010020101.

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42

Gusev, Marjan. "Scalable Dew Computing." Applied Sciences 12, no. 19 (September 22, 2022): 9510. http://dx.doi.org/10.3390/app12199510.

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Dew computing differs from the classical cloud and edge computing by bringing devices closer to the end-users and adding autonomous processing independent from the Internet, but it is still able to collaborate with other devices to exchange information on the Internet. The difference is expressed also on scalability, since edge and cloud providers can provide (almost endless) resources, and in the case of dew computing the scalability needs to be realized on the level of devices, instead of servers. In this paper, we introduce an approach to provide deviceless and thingless computing and ensure scalable dew computing. The deviceless approach allows functions to be executed on nearby devices found closer to the user, and the thingless approach goes even further, providing scalability on a low-level infrastructure that consists of multiple things, such as IoT devices. These approaches introduce the distribution of computing to other smart devices or things on a lower architectural level. Such an approach enhances the existing dew computing architectural model as a sophisticated platform for future generation IoT systems.

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43

Raju, Y. Home Prasanna, and Nagaraju Devarakonda. "A cluster medoid approach for cloud task scheduling." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 1 (April 9, 2021): 65–73. http://dx.doi.org/10.3233/kes-210053.

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One of the familiar distributed technologies for sharing computing resources through internet is a cloud computing technology. One need not setup all computing resources on their own to design their applications. They can own as much they want by requesting computing resources through net. These resources are shared between users upon request by properly scheduling tasks in cloud. The process of scheduling tasks is to be optimized to share the resources very fast. The paper proposes a cluster medoid based task scheduling technique KMPS (K-medoid particle swarm approach) for minimizing the makespan. KMPS uses the merits of both Particle Swarm Optimization (PSO) and k-medoid approaches with added weights concept. Experimental results have shown that KMPS has optimized the results of make span and it is most suitable one for cloud computing.

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44

Elements and Will Riherd. "Neuromorphic Computing." Elements 17, no. 1 (March 27, 2022): 33–40. http://dx.doi.org/10.6017/eurj.v17i1.14909.

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In the face of increasingly large computational demands and the impending halt to Moore's law, the semiconductor industry has been forced to re-evaluate the traditional computing paradism. Central to this re-evaluation has been the novel development of neuromorphic computing - an approach that, at its core, seeks to replicate the brain in silicon. Despite challenges on the algorithmic front, neuromorphic computing promises a massively parallel, efficicient, and scalable computational solution with large implications on the daily lives of consumers. The future of the technology, however, is uncertain. With the rise of high performance and Quantum computing as promising alternatives, te semiconductor industry at large must consider the extent to which neuromorphic computing can emerge as a viable and feasible solution in the coming years. It is vital, therefore, to understand the theoretical underpinnings of the neuromorphic approach and predict the likelihood of its implementation within the next decade

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45

Pavan Kumar, T., B. Eswar, P. Ayyappa Reddy, and D. Sindhu Bhargavi. "An efficient fog computing for comprising approach to avoid data theft attack." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 680. http://dx.doi.org/10.14419/ijet.v7i2.8.10556.

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Cloud computing has become a new paradigm shift in the IT world because of its revolutionary model of computing. It provides flexibility, scalability, and reliability and decreased operational and support expenses for an organization. The Enterprise edition software’s are very costly and maintaining a separate IT team and maintaining their own servers is very expensive and that’s the reason why most of the companies are opting for Cloud computing over enterprise edition of the software. However, few organization cloud customers are not willing to step to cloud computing up on a big scale because of the safety problems present in cloud computing. One more disadvantage of Cloud is it’s not suitable for another revolutionary technology i.e.IoT(Internet of things)In this paper we are going to present the Advantages of Fog Computing and Decoy technology to address the security in cloud computing by extending it into fog computing.Fog Computing is a new paradigm in which the computing power moves to the edge of the network. So, it’s also called as Edge Computing.

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46

Farooq, Muhammad, Sehrish Sarfraz, Christophe Chesneau, Mahmood Ul Hassan, Muhammad Ali Raza, Rehan Ahmad Khan Sherwani, and Farrukh Jamal. "Computing Expectiles Using k-Nearest Neighbours Approach." Symmetry 13, no. 4 (April 11, 2021): 645. http://dx.doi.org/10.3390/sym13040645.

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Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

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47

Dhote, Bharti, and A. M. Kanthe. "Secure Approach for Data in Cloud Computing." International Journal of Computer Applications 64, no. 22 (February 15, 2013): 19–24. http://dx.doi.org/10.5120/10776-5188.

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48

Azeem, Waqar, Muhammad Sadiq Ali Khan, and Muhammad Naeem Khan. "Reducing Network Computing by Adopting Intelligent Approach." Indian Journal of Science and Technology 10, no. 29 (February 1, 2017): 1–7. http://dx.doi.org/10.17485/ijst/2017/v10i29/117329.

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49

Baidoo, Evans, and Stephen O. "Solving the TSP using Traditional Computing Approach." International Journal of Computer Applications 152, no. 8 (October 17, 2016): 13–19. http://dx.doi.org/10.5120/ijca2016911906.

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50

Kumar, Munindra, and Prashant Kumar. "Security of Grid Computing: A Cryptographic Approach." International Journal of Computer Applications 164, no. 6 (April 17, 2017): 1–3. http://dx.doi.org/10.5120/ijca2017913646.

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