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The huge amount of NoSQL data has acknowledged a new provision of context for processing. A new trail of data handling technologies with massive resources assists to store and process these gigantic data sets. The current attention is to determine the undisclosed information by assimilating this data bulks & handling it as per use. Further they are pre-processed and converted for needful analysis. The volume and variety of these data sets endure rising relentlessly. Moreover, imbalanced in many real-worlds vast data sets have elevated a point of concern in the research domain. The skewed distribution of classes in the data sets poses a difficulty to learn using traditional classifiers. They tend more towards majority classes. In recent years, numerous solutions have been proposed to address imbalanced classification. However, they fail to address the various data characteristics such as overlapping, redundancy involving classification performance. A rational over_sampling technique i.e. Updated Class Purity Maximization Over_Sampling Technique using Safe-Level based synthetic sample creation is proposed to efficiently handle imbalanced data sets. The newly suggested Lowest versus Highest method addresses the handling of multi-class data sets. The data sets from the UCI repository are processed using the mapreduce based programming on Hadoop framework. The evaluation parameters viz. F-measure and AUC are used to authenticate the performance of proposed technique over benchmarking techniques. The results attained evidently quote the dominance of the proposed technique.

Imbalanced datasets, Big Data, Over_sampling techniques, Multi-class, Safe-Level based Synthetic Samples

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S.S. Patil, S. P. Sonavane, "Handling of Class Imbalanced Problem in Big Data Sets: An Experimental Evaluation (UCPMOT)", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.1-9, 2018.

DTN is subclass of mobile ad hoc network (MANET) where instantaneous end-to-end connectivity is not available in source and destination nodes. Nodes in DTN are sparsely distributed. Frequent disconnections along with limited resources make routing in DTN more challenging. This paper proposes two routing protocols. One is Buddy Router with Time Window, which exploits social relations to maximize delivery probability. Another variant presented is Buddy Router with Replication, where controlled replication approach is used, along with social metric for message forwarding. Detailed formulation of proposed work, along with comparative analysis, based on simulations is presented. The paper also presents impact of buffer size variation and TTL variation on routing performance of different routing protocols.

Delay Tolerant Network (DTN), Routing, Opportunistic Routing and Pocket Switched Networks (PSN)

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[4] Yue Cao and Zhili Sun, Member, IEEE “Routing in Delay/Disruption Tolerant Networks: A Taxonomy, Survey and Challenges” IEEE Communications Surveys & Tutorials, Accepted For Publication.
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[7] Ying Zhu, Bin Xu , Xinghua Shi, and Yu Wang “A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects” IEEE Communications Surveys & Tutorials, Vol. 15, No. 1, First Quarter 2013
[8] Kaimin Wei, Xiao Liang, and Ke Xu, “A Survey of Social-Aware Routing Protocols in Delay Tolerant Networks:Applications, Taxonomy and Design-Related Issues” IEEE Communications Surveys & Tutorials, Accepted For Publication
[9] Paulo Rogerio Pereira, Augusto Casaca, Joel J. P. C. Rodrigues, Vasco N. G. J. Soares, Joan Triay, and Cristina Cervello-Pastor “From Delay-Tolerant Networks to Vehicular Delay-Tolerant Networks” IEEE Communications Surveys & Tutorials, Vol. 14, No. 4, Fourth Quarter 2012
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[13] P. Basu and S. Guha, “Effect of Limited Topology Knowledge on Opportunistic Forwarding in Ad Hoc Wireless Networks,” Eighth International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WIOPT), Avignon, France, June 2010Buffer Management
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[19] Tamer Abdelkader, Kshirasagar Naik, Amiya Nayak, Nishith Goel, and Vineet Srivastava “SGBR: A Routing Protocol for Delay Tolerant Networks Using Social Grouping” IEEE Transactions On Parallel And Distributed Systems (Accepted for Final Publication)
[20] Shengling Wang And Min Liu, Xiuzhen Cheng, “Routing In Pocket Switched Networks” IEEE Wireless Communications, Feb 2012.
[21] The Opportunistic Network Environment simulator. http://www.netlab.tkk.fi/tutkimus/dtn/theone/
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Ajit S. Patil, Prakash J. Kulkarni, "Exploiting Social Relations for Efficient Routing in Delay Tolerant Network Environment", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.10-18, 2018.

Language modeling is a technique for finding the next most probable word in a sentence. It is first and essential task for successful implementation of some natural language processing applications like machine translation and speech recognition. It ensures for correctness and fluency of the target output in these applications. N-gram is a traditional way to implement language model in which only previous words in the sentence are used to predict the probable next word in the sentence. Factored language modeling is a method to utilize linguistic knowledge of the word along with the word itself for constructing the language model. The paper describes the factored language modeling technique and compares the results obtained against the traditional n-gram technique using perplexity as a measure.

Language model, Perplexity, Factored language model, Backoff.

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[3] J.A. Bilmes, K. Kirchhoff, “Factored Language Models and Generalized Parallel Backoff ”, In the Proceedings of the 2003 HLT/NAACL, pp 4-6, 2003.
[4] K. Kirchhoff, J. Bilmes, K. Duh, “Factored Language Models Tutorial”, University of Washington, 2016.
[5] A. E. Axelrod, “Factored Language Models for Statistical Machine Translation ”, University of Edinburgh, 2006.
[6] A. Stolcke, “SRILM- an Extensible Language Modeling Toolkit”, In the Proceedings of the 2002 International Conference on Spoken Language Processing, Denver, Colorado, September 2002.
[7] A. Stolcke, J. Wheng, W. Wang, V. Abrash, “SRILM at Sixteen: Update and Outlook”, In the Proceedings of the 2011 IEEE Automatic Speech Recognition and Understanding Workshop, Waikoloa, 2011.
[8] K. Duh, K. Kirchhoff, “Automatic Learning of Language Model Structure”, In the Proceedings of the 2004 International Conference on Computational Linguistics (COLING), 2004.
[9] E. M. deNovais, “Portuguese Text Generation Using Factored Language Models”, J. Brazilian Computation Society, Vol. 19, Issue. 2, pp 135–146, 2013.
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[14] Z. Alumae, “Sentence Adapted Factored Language Model for Transcribing Stonian Speech”, In the Proceedings of the 2006 ICASSP, Toulouse, pp. 429–432, 2006.
[15] T. Hirsimaki, J. Pylkkonen, M. Kurimo, “Importance of High-Order N-Gram Models in Morph-Based Speech Recognition”, IEEE Trans. Audio, Speech, Lang. Process. , Vol. 17, Issue. 4, pp. 724–732, 2009.
[16] H. Adel, NT. Vu, K. Kirchhoff, D. Telaar, T. Schultz, “Syntactic and Semantic Features for Code-Switching Factored Language Models”, IEEE/ACM Trans. Audio, Speech, Lang. Process, Vol. 23, Issue. 3, pp. 431–440, 2015.

A.R. Babhulgaonkar, S.P. Sonavane, "Factored Language Modeling", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.19-25, 2018.

Region labeling for outdoor scenes to identify sky, green land, water, snow etc. facilitates content-based image retrieval systems. This paper presents use of multiple features to classify various objects of the outdoor natural scene image. Proposed system aims to classify images of the sky, water and green land. As all these nature components are irregular in shape, they can be classified using color and texture features. Color features of the object are extracted by using segmentation in La*b* color space. In the process of texture feature calculation, the image is initially divided into smaller grids. Global GLCM based statistical texture features are calculated using statistical features of these local grids. Results show that color and statistical texture features are not sufficient to differentiate sky and water body. To achieve discrimination between these two objects, a new edge-based horizontal line texture feature is proposed. The proposed feature is used to differentiate between sky and water objects based on the density of horizontal lines. All these features are used together to train probabilistic neural network for classification. The system has achieved improvement of 5% to 8% in F-measure, when all these features are used together for classification of natural scene objects.

Color feature, Statistical texture features,Horizontal line texture feature, Image classification, PNN

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C.A. Laulkar, P.J. Kulkarni, "Outdoor Natural Scene Object Classification Using Probabilistic Neural Network", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.26-31, 2018.

Neural network learning is a technique that is used to solve problems of classification, prediction, clustering, modelling based on variety of data inputs in the form of structured, semi-structured and unstructured data. Learning accuracy is considered as key performance index in these neural network based learning algorithms. Many organization that involves huge amount of data would want to outsource it to cloud for artificial intelligence based services. Various organization who wish to train neural network model on their complex and huge data usually outsource the learning model on cloud. Outsourcing of learning model on cloud creates security concerns for input data and the learned model. In this paper, we propose a practical system that will train a neural network model that is encrypted during training process. The training is performed on the unencrypted data. The output of the system is a neural network model that possesses two properties. First, neural network model is protected from the malicious users, hence allows the users to train the model in insecure environments at no cost of risk. Second, the neural network model can make only encrypted predictions. We make use of homomorphic encryption techniques to fulfill the objectives and test our results on sentiment analysis dataset.

Homomorphic encryption, neural network

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S. S. Sayyad, D. B. Kulkarni, "An Encrypted Neural Network Learning to Build Safe Trained Model", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.32-36, 2018.

World Wide Web is the useful and easy way to get the source of information on the Internet. In order to reduce the content generation and publishing time, templates are used to populate the contents in web documents. Template provides easy access to the web document contents through their layout and structures. However, for search engines, due to its irrelevant terms, the templates degrade search engines accuracy and performance. Also the templates are used by wrapper induction tools used in information extractor to extract and integrate information from various E-commerce sites. Thus it has received a lot of attention to improve the search engines performance and content integration. In this paper we have discussed how heterogeneous web documents i.e. web documents generated from different templates, can be clustered. We have applied document similarity measures to cluster the heterogeneous web documents generated from templates. Our experimental results on real data sets show that cosine distance similarity measure is more suitable for template based clustering of heterogeneous web documents.

Template, Clustering, Cosine, Jaccard, Agglomerative Hierarchical Clustering

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[13] Y. Zhai and B. Liu,“Web Data Extraction Based on Partial Tree Alignment”, Proc. 14th Int’l Conf. World Wide Web (WWW), 2005.
[14] V. Crescenzi, G. Mecca, and P. Merialdo,“Roadrunner: Towards Automatic Data Extraction from Large Web Sites”, Proc. 27th Int’l Conf. Very Large Data Bases (VLDB), 2001.
[15] K. Vieira, A.S. da Silva, N. Pinto, E.S. de Moura, J.M.B. Cavalcanti, and J. Freire,“A Fast and Robust Method for Web Page Template Detection and Removal”, Proc. 15th ACM Int’l Conf. Information andKnowledge Management (CIKM), 2006.
[16] S. Zheng, D. Wu, R. Song, and J.-R. Wen,“Joint Optimization of Wrapper Generation and Template Detection”, Proc. ACMtiSIGKDD, 2007.
[17] Chulyun Kim and Kyuseok Shim,”TEXT: Automatic Template Extraction from Heterogeneous Web Pages”,IEEE Transaction on Knowledge and Data Engineering, 2011

T.I. Bagban, P. J. Kulkarni, "On Applying Document Similarity Measures for Template based Clustering of Web Documents", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.37-42, 2018.

Security of digital information plays important role to keep the integrity of original media. A secret is something which is kept away from the knowledge of any but those who are privileged to access it. Secret sharing scheme provides a mechanism for sharing secrets among different users securely, where each user receives his part of encoded secret information called as a share. Sufficient number of shares need to be combined together to reconstruct secret information. Text, images, audio and video can be used for sharing secret information in secret sharing scheme. Secret sharing scheme in which secret information is encoded in form of concealed images is called as Visual Cryptography. There are various Visual Cryptography Schemes. Visual Cryptography Scheme’s functionality is dependent on their basis matrices. Constructions of basis matrices for various OR-based and XOR-based Visual Cryptography Schemes are elaborated in this paper.

Secret sharing scheme, Visual Cryptography, Data hiding

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[9] X. Wu and W. Sun, “Extended capabilities for XOR-based visual cryptography,” IEEE Trans. Inf. Forensics Security, vol. 9, no. 10, pp. 1592–1605, Oct. 2014.
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[12] Jin, W. Q. Yan, and M. S. Kankanhalli, “Progressive color visual cryptography,” J. Electron. Imag., vol. 14, no. 3, pp. 1–13, 2005.

S.B. Bhagate, P.J. Kulkarni, "Construction of Basis Matrices for (k, n) and Progressive Visual Cryptography Schemes", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.43-47, 2018.

Particle Swarm Optimization (PSO) is a heuristic global optimization method, which is most commonly used for feature subset selection problem. However, PSO requires the fixed number of optimal features as an input. It is a very critical task to analyze initially that how many features are relevant and non-redundant present in the given dataset. To solve the said problem this paper has proposed Improved Genetic – PSO (IG-PSO) algorithm for Extreme Learning Machine (ELM) which returns optimal features as well as an optimal number of features. The IG-PSO algorithm is experimented on six benchmarked dataset for handling medical dataset classification which improves the classification accuracy by using optimal features. Also, the simulation results demonstrate that IG-PSO algorithm has the capability to handle optimization, dimensionality reduction and supervised binary classification problems.

Feature Subset Selection Problem, Pattern Classification Problem, Extreme Learning Machine, Particle Swarm Optimization

[1] L. Yu and H. Liu, “Efficient feature selection via analysis of relevance and redundancy”, Journal of machine learning research, pp. 1205-1224, 2014.
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[5] Iftikhar Ahmad,”Feature Selection Using Particle Swarm Optimization in Intrusion Detection”,International Journal of Distributed Sensor Network, January 2015.
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[13] B. Xue, M. Zhang, and W. N. Browne, “Particle swarm optimization for feature selection in classification: amulti-objective approach,” IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1656–1671, 2013.
[14] B. Xue, M. Zhang, and W. N. Browne, “Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms”, Applied Soft Computing Journal, vol. 18, pp. 261–276, 2014.
[15] M. Lichman, UCI machine learning repository. URL http://archive.ics.uci.edu/ml, 2013. UCI repository
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[17] G. Karakaya, S. Galelli, S. D. Ahipa sao glu and R. Taormina, “Identifying (quasi) equally informative subsets in feature selection problems for classification: a max-relevance min-redundancy approach”, IEEE transactions on cybernetics 46 (6), pp. 1424-1437,2016.
[18] Nahato, K. B., Nehemiah, K. H., Kannan, A, “ Hybrid approach using fuzzy sets and extreme learning machine for classifying clinical datasets”, Elsevier Journal of Informatics in Medicine Unlocked 2, 1–11, 2016.
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[21] Parikh, R., Mathai, A., Parikh, S., Sekhar, G. C., Thomas, R., Understanding and using sensitivity, specificity and predictive values. Indian journal of ophthalmology 56 (1), 45, 2008.
[22] Archana Kale and Shefali Sonavane, “Optimal Feature Subset Selection for Fuzzy Extreme Learning Machine using Genetic Algorithm with Multilevel Parameter Optimization”, IEEE International conference on Signal and Image Processing Applications pp.445-450, Septmber 2017.
[23] A. Kale and S. Sonavane, “Hybrid Feature Subset Selection Approach for Fuzzy-Extreme Learning Machine”, Springer journal of Computational Intelligence and Complexity - Data Enabled and Discovery Applications, September 2017.
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A.P. Kale (IEEE and IEICE Student Member), S.P. Sonavane, "Improved Genetic Particle Swarm Optimization and Feature Subset Selection for Extreme Learning Machine", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.48-54, 2018.

Information available on the web is growing rapidly. A major problem in web search is that the interactions between the users and search engines are limited by the factors like unknown capabilities of search engines adopted, and ill-constructed query by the user. Hence the user has to repeatedly apply the several queries till he reaches the pages of most interest. Any search engine can give its best performance if well-constructed and detailed queries are used. As a result, the users tend to submit shorter/ insufficient/ ambiguous queries yielding unwanted search lists. In order to return highly relevant results to the users, search engines must be able to profile the users’ interests and personalize the search results according to the users’ profiles. This paper discusses the need and specific requirements of personalized search engine, its architecture, the prototype model developed and the results obtained. Also sample sessions performed on the designed model have been given for selected user profile.

Web Search Engines, Personalized Web Searching, Meta Search Engines

[1] K Wai-Ting Leung, D Lee, W Lee, “PMSE: A Personalized Mobile Search Engine”, IEEE Transactions On Knowledge And Data Engineering, Vol. 25, Issue: 4, pp.820-834, April 2013.
[2] S. Prakasha, H.Shashidhar, G.T. Raju, “Structured Intelligent Search Engine for Effective Information Retrieval using Query Clustering Technique and Semantic Web”, International Conference on Contemporary Computing and Informatics (IC3I), 688 695, DOI: 10.1109/IC3I.2014.7019820.
[3] A Annadurai, “Architecture of personalized web search engine using suffix tree clustering”, International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN 2011), pp. 604-608, 2011.
[4] K.W.-T. Leung, W. Ng, and D.L. Lee, “Personalized Concept-Based Clustering of Search Engine Queries,” IEEE Trans. Knowledge and Data Eng., vol. 20, no. 11, pp. 1505-1518, Nov. 2008.
[5] J. Teevan, S.T. Dumais, and E. Horvitz., “Personalizing Search via Automated Analysis of Interests and Activities. Proceedings of the 28th Annual International ACM SIGIR” Conference on Research and development in information retrieval (SIGIR`05), pages 449–456, 2005.
[6] Adah, S.; Bufi, C.; Temtanapat, Y., “Integrated Search Engine”, @IEEE Knowledge and Data Engineering Exchange Workshop, 1997. Pages: 140 – 147.
[7] O. Zamir, O.Etzioni, “A Dynamic Clustering interface to Web search results,” Computer Networks, Netherlands, Amsterdam, 31(11-16):1361-1374, 1999.
[8] M. Ilic, P. Spalevic, M. Veinovic, “Suffix Tree Clustering – Data mining algorithm”, Twenty-Third International Electrotechnical and Computer Science Conference ERK`2014, Portorož, ISSN 1581-4572, pp. 15-18, September 22-24, 2014.
[9] K A Heller, Z Ghahramani. “Bayesian hierarchical clustering”, Proceedings of the 22nd international conference on Machine learning, pp. 297-304, 2005.
[10] R.E. Ruviaro Christ, E. Talavera, C. Maciel, “Gaussian Hierarchical Bayesian Clustering Algorithm”, ISDA 2007, pp. 133-13.

N. A. Borkar, S. V. Kulkarni, "Architecture for Personalized Meta Search Engine", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.55-59, 2018.

MPI (Message Passing Interface) is most widely used parallel programming paradigm. It is used for application development on small as well as large high-performance computing systems. MPI standard provides a specification for different functions but it does not specify any performance guarantee for implementations. Nowadays, its various implementations from both vendors and research groups are available. Users are expecting consistent performance from all implementations and on all platforms. In literature, performance guidelines are defined for MPI communication, IO functions and derived data types. By using these guidelines as a base we have defined guidelines for scalability of MPI communication functions. Also, we have verified these guidelines by using benchmark application and on different MPI implementations such as MPICH, open MPI. The experimental results show that point to point communication functions are scalable. It is quite obvious as in point to point communication the only pair of processes is involved. Hence these guidelines are defined as performance requirement by considering the semantics of these functions. All processes are involved in collective communication functions; therefore defining performance guidelines for collective communication is difficult. In this paper, we have defined the performance guidelines by considering the amount of data transferred in the function. Also, we have verified our defined guidelines and reasons for violations of these guidelines are elaborated.

Performance guidelines for MPI functions, Scalability of MPI functions, High-performance computing

[1] A. Mallón, Guillermo L. Taboada, Carlos Teijeiro, Juan Touriño, Basilio B. Fraguela, Andrés Gómez, Ramón Doallo, J. Carlos Mouriño, “Performance Evaluation of MPI, UPC and OpenMP on Multicore Architectures”, Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2009. Lecture Notes in Computer Science, pp. 174–184, 2009.
[2] William D. Gropp, Rajeev Thakur, “Self-consistent MPI performance guidelines”, IEEE Transaction on parallel and distributed systems, 2005.
[3] William D. Gropp, Dries Kimpe, Robert Ross, Rajeev Thakur and Jesper Larsson Traff, “Self-consistent MPI-IO performance requirements and expectations”, Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2008. Lecture Notes in Computer Science, 2008.
[4] William D. Gropp, Dries Kimpe, Robert Ross, Rajeev Thakur and Jesper Larsson Traff, “Performance Expectations and Guidelines for MPI Derived Datatypes”, Recent Advances in the Message Passing Interface. EuroMPI 2011. Lecture Notes in Computer Science, 2011.
[5] Sascha Hunold, Alexandra Carpen-Amarie, Felix Donatus Lübbe, and Jesper Larsson Träff TU Wien, “Automatic verification of self-consistent MPI performance guidelines”, Parallel Processing, Euro-Par 2016. Lecture Notes in Computer Science, 2016.
[6] Ralf Reussner, Peter Sanders, and Jesper Larsson Träff, “SKaMPI: A Comprehensive Benchmark for Public Benchmarking of MPI,” Journal of Scientific Programming, vol. 10, issue 1, pp. 55-65, 2002.
[7] WCE Rock Cluster, High performance computing cluster, URL: http://wce.ac.in/it/landing-page.php?id=9.
[8] J. Liu, B. Chandrasekaran, W. Yu, J. Wu, D. Buntinas, S. Kini, P. Wyckoff, and D. K. Panda, “Micro-Benchmark Performance Comparison of High-Speed Cluster Interconnects” , Proceedings of 11th Symposium on High Performance Interconnects, 2003.
[9] Hunold, S., Carpen-Amarie, A., “Reproducible MPI benchmarking is still not as easy as you think”, IEEE Transactions on Parallel and Distributed Systems , vol. 27, issue 12, 2016.
[10] Subhash Saini, Robert Ciotti,Brian T. N. Gunney, Thomas E. Spelce, Alice Koniges, Don Dossa, Panagiotis Adamidis, Rolf Rabenseifner, Sunil R. Tiyyagura, Matthias Mueller, “Performance Evaluation of Supercomputers using HPCC and IMB Benchmarks”, Journal of Computer and System Sciences, vol. 74, issue 6, 2008.

K.B. Manwade, D.B. Kulkarni, "MPI performance guidelines for scalability", International Journal of Computer Sciences and Engineering, Vol.06, Issue.01, pp.60-65, 2018.