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A Graph has two main components: Vertices and Edges. The vertices are connected using edges. There are two types of graphs, directed and undirected. The major application of graph is representing network on paper. The cost involved in converting paper based network to actual cable based network is majorly controlled by cables required for connection. The cost can be reduced if the length of cable can be reduced. The paper describes the methodology to compute Steiner point. Using Steiner point, it is possible to modify the position of vertices, so as to reduce the cable length, keeping the vertex connectivity intact. The paper describes implementation of Steiner point on graph with number of vertices as 3, 4, 5 and 6. The presented work can be extended for graph with any number of vertices. It is an optimization approach to reduce the cable size and cost of network implementation.

Graph, Network, Cable length, Steiner point, data structures

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M.B.Chandak, S.Bhalotia, S.C.Agrawal, "A novel Approach to Compute Steiner point in Graph: Application for Network Design," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.224-231, 2017.

The piling up of vast quantity of biological data owing to the enormous exploitation of next and third generation sequencing techniques has made their management and handling an uphill task. Cloud computing offers solution to the storage, processing and analysis issues of such a gigantic amount of biological data. The abstraction layer in cloud computing empowers an incorporated access to handling, storage and virtualization. Herein, we review various types of clouds, cloud based service models in bioinformatics and cloud computing platforms with parallel application tools. Lastly, we discuss how the cloud based platforms are being exploited for big data analysis in biology.

Cloud computing, bioinformatics, big data, handling, challenge

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25(11):1363{1369, 2009.
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Shahid Tufail, M. Abdul Qadeer, "Cloud Computing in Bioinformatics: Solution to Big Data Challenge," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.232-236, 2017.

Most of the websites comprises of useful information but along with that they contains non-relevant information mostly related to advertisements, copyright, external links etc. This irrelevant information is considered as noise and if we focus on some of the popular English News web sites i.e., Times of India, Hindustan Times, Indian Express etc. consists of 30-40% of news related information and rest are noise content. In this paper we proposed a novel approach that extracts informative content from news web sites in an unsupervised fashion. Our method utilizes the web page segmentation technique to partition the web page into non overlapping rectangular blocks. In our study we used Artificial Neural Network as a classifier to discriminate the rectangular block using their features as relevant or irrelevant blocks. The main content blocks are filtered from the web page and user is presented with clean news web page. Empirical evaluation of our system shows that ANN classifier gives 96.03% accuracy for web content identification that results in accurately filtering of the web page content.

Artificial Neural Network, Web Page Segmentation, Visual Blocks, Cosine Similarity

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[2] J. Gibson, B. Wellner, and S. Lubar, “Adaptive web-page content identification,” in Proceedings of the 9th annual ACM international workshop on Web information and data management. ACM New York, NY, USA, 2007, pp. 105–112.
[3] J. Prasad and A. Paepcke, “Coreex: content extraction from online news articles,” in CIKM ’08: Proceeding of the 17th ACM conference on Information and knowledge management. New York, NY, USA: ACM, 2008, pp. 1391–1392.
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[7] Neetu Narwal, Mayank Singh, Web Content Extraction A Heuristic Approach, International Journal Of Computer Science and Information Security, Vol 11, No1 , 2013.
[8] N Narwal, S K Sharma, Amit Prakash Singh, Entropy based content filtering for Mobile Web Page Adaptation, Proceeding WCI `15 Proceedings of the Third International Symposium on Women in Computing and Informatics Pages 588-594 , ACM New York, NY, USA ©2015 , table of contents ISBN: 978-1-4503-3361-0.
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N. Narwal, "Noise Removal from News Web Sites," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.237-243, 2017.

Object oriented software engineering concepts are one of the most popular methods in the information technology industry and academia as well as in many other forms of engineering design. Software engineering is a field of engineering that came into existence owing to the various problems that developers of software faced while developing software projects. This paper analyzes some of the most important technological innovations in object oriented software engineering in recent times. The advancements in Object technologies that have been analyzed here include module coupling metrics, software up gradation, layered reusability, monitors, attribute objects, global software development contexts, software testing, recursive types, coupled and co-evolving classes, query-able source codes, meta-model for generating design alternatives, programming micro-worlds, design pattern detection, object schema migration, functional verification, work system theory, and state chart diagrams. From our analysis we predict further advancements in object technologies towards game development, metrics for software design analysis, addition to fundamental Object oriented programming language features and distributed software engineering.

Object orientation, Software engineering, Data warehousing, Teaching methodologies

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[2]. Sharma, Yashvardhan. "Effort Estimation for Program Modification in Object Oriented Development." International Conference on Computational Science and Its Applications. Springer International Publishing, 2016.
[3]. Baas, Andre. An object-oriented component approch to building real-time software systems. Diss. 2016.
[4]. Moreno-Delgado, Antonio, Francisco Durán, and José Meseguer. "Towards Generic Monitors for Object-Oriented Real-Time Maude Specifications." (2016).
[5]. Xia, Haijun, et al. "Object-Oriented Drawing." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016.
[6]. Wong, Y. S., Hayati, M. Y. M., & Tan, W. H. (2016, September). A Propriety Game-Based Learning Game as Learning Tool to Learn Object-Oriented Programming Paradigm. In Joint International Conference on Serious Games (pp. 42-54). Springer International Publishing.
[7]. Chandra, Alaknanda, and Abhishek Singhal. "Study of Unit and Data flow testing in object-oriented and aspect-oriented programming." 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH). IEEE, 2016.
[8]. Jabangwe, Ronald, et al. "A method for investigating the quality of evolving object-oriented software using defects in global software development projects." Journal of Software: Evolution and Process (2016).
[9]. Holopainen, Timo. "Object-oriented programming with Unity: Inheritance versus composition." (2016).
[10]. Cisar, Sanja Maravic, Petar Cisar, and Robert Pinter. "Evaluation of knowledge in Object Oriented Programming course with computer adaptive tests." Computers & Education 92 (2016): 142-160.
[11]. Nierstrasz, Oscar. "The Death of Object-Oriented Programming." International Conference on Fundamental Approaches to Software Engineering. Springer Berlin Heidelberg, 2016.
[12]. Abdoli, Shiva, and Sami Kara. "Designing Warehouse Logical Architecture by Applying Object Oriented Model Based System Engineering." Procedia CIRP 50 (2016): 713-718.
[13]. Amato, Gianluca, Maria Chiara Meo, and Francesca Scozzari. "Exploiting Linearity in Sharing Analysis of Object-oriented Programs." Electronic Notes in Theoretical Computer Science 322 (2016): 3-18.
[14]. Djelil, Fahima, et al. "Microworlds for Learning Object-Oriented Programming: Considerations from Research to Practice." Journal of Interactive Learning Research 27.3 (2016): 247-266.
[15]. Furia, Carlo A., et al. "AutoProof: auto-active functional verification of object-oriented programs." International Journal on Software Tools for Technology Transfer (2016): 1-20.
[16]. Sunitha, E. V., and Philip Samuel. "Object Oriented Method to Implement the Hierarchical and Concurrent States in UML State Chart Diagrams." Software Engineering Research, Management and Applications. Springer International Publishing, 2016. 133-149.
[17]. Börstler, Jürgen, Michael E. Caspersen, and Marie Nordström. "Beauty and the Beast: on the readability of object-oriented example programs." Software Quality Journal 24.2 (2016): 231-246.
[18]. Alter, Steven, and Narasimha Bolloju. "A Work System Front End for Object-Oriented Analysis and Design." International Journal of Information Technologies and Systems Approach (IJITSA) 9.1 (2016): 1-18.
[19]. Miranda, Eliot, and Clément Béra. "A partial read barrier for efficient support of live object-oriented programming." ACM SIGPLAN Notices 50.11 (2016): 93-104.
[20]. Chhabra, Rashmi, Parveen Kumar, and Payal Pahwa. "An approach to Design Object Oriented Data Warehouse." International Journal of Research and Engineering 3.3 (2016): 54-56.
[21]. Oruc, Murat, Fuat Akal, and Hayri Sever. "Detecting Design Patterns in Object-Oriented Design Models by Using a Graph Mining Approach." 2016 4th International Conference in Software Engineering Research and Innovation (CONISOFT). IEEE, 2016.
[22]. Parashar, Anshu, and Jitender Kumar Chhabra. "Mining software change data stream to predict changeability of classes of object-oriented software system." Evolving Systems 7.2 (2016): 117-128.
[23]. Ghoreshi, M., and H. Haghighi. "An incremental method for extracting tests from object-oriented specification." Information and Software Technology 78 (2016): 1-26.
[24]. Noei, Ehsan, and Abbas Heydarnoori. "EXAF: A search engine for sample applications of object-oriented framework-provided concepts." Information and Software Technology 75 (2016): 135-147.
[25]. Ryu, Sukyoung. "ThisType for Object-Oriented Languages: From Theory to Practice." ACM Transactions on Programming Languages and Systems (TOPLAS) 38.3 (2016): 8.
[26]. P.L. Power,M.P. Singh,Bharat Solanki, Jawwad Wasat Shareef, “Computation of External view based Software Metrics: Java Based Tool”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.33-43,2017.

Biswajit Saha, Debaprasad Mukherjee, "Analysis of Applications of Object Orientation to Software Engineering, Data Warehousing and Teaching Methodologies," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.244-248, 2017.

With the advances in technology, data has too accumulated at an alarming pace and with that the need to analyze data has also grown. Facebook, Instagram, Twitter and other social networks have catalyzed the process of data accumulation. Data related to healthcare system is also growing with the growing incidences of cancer, diabetes and other diseases and the parallel advent of high-throughput technologies. In this paper, we have taken data from healthcare sector and analyzed them to accumulate knowledge of how the health condition of female diabetic patients and female non-diabetic patients varies according to various parameters such as age, blood pressure, skin thickness, and body mass index (BMI) and so forth.

Data, Analysis, R programming, healthcare, diabetes, dataset

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[4] Ashfaq Ahmed K, Sultan Aljahdali and Syed Naimatullah Hussain, (2013) “Comparative Prediction Performance with Support Vector Machine and Random Forest Classification Techniques”, International Journal of Computer Applications Vol. 69, No.11, pp 12-16
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[6]https://www.analyticsvidhya.com/blog/2015/09/applications-data-science/
[7] Mailund T. Introduction to R Programming. In: Beginning Data Science in R. Apress, Berkeley, CA, USA, 2017.
[8] HTTP://WWW.KDNUGGETS.COM/2015/06/TOP-20-R-PACKAGES.HTML

Shahid Tufail, M. Abdul Qadeer, "Analysing data using R: An application in healthcare sector," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.249-253, 2017.

It is a research challenge for scientists and researchers to reliably transport data from source to destination. The received copies were found to be loaded with errors and the recovery of the correct original copy was a challenging job. The paper deals with some novel protocols related to physical level representation, circular shift based information transmission, masking scheme and modified packet combining technique through with this problem can be addressed to a considerable extent.

Packet combining scheme; Aggressive packet combining scheme; bit shifting; physical level

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[4]. C T Bhunia, ARQ with two level coding with generalized parity and I (i>1) copies of parts in retransmission, Proceedings of National Conference on Data Communications (NCDC-2000), Computer Society of India, Chandigarh, India, 7-8 April’2000, pp.19
[5]. C T Bhunia, ARQ Techniques: Review and Modifications, Journal IETE Technical Review, Sept-Oct’2001 Vol18, No 5, pp 381-401
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Achyuth Sarkar, S.K. Chakraborty, C.T.Bhunia, Prasun Chakrabarti , "Approach towards validity of Aggressive Packet Combining Scheme with Physical Level Representation," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.254-256, 2017.

One of the important methods of identifying a person is fingerprint because it has several features like invariability, uniqueness and more. Nowadays, many databases hold fingerprint columns and these columns may be search based columns. Database may incur overload search on fingerprint columns which represent a bottleneck to the system. We proposed a method that convert the fingerprint to text which improves the performance of the system and many more other benefits. Fingerprint image is converted to text which is smaller in size that helps in processing as well as storage space. Smaller size of data also is better in transmission over network. In the term of data encryption, text encryption is easier and has less time complexity than of images.

Fingerprint Conversion, Fingerprint Presentation, Minutiae Point, Database Performance

[1] Haonan Su, Dong Zheng, and Hinghui Zhang. “An Efficient and Secure Deduplication Scheme Based on Rabin Fingerprinting in Cloud Storage”. IEEE international Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). 2017.
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Nashwan Yahya Ali, V M Thakare, "Mechanism of Fingerprint Conversion to Text," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.257-260, 2017.

Clustering is the way toward gathering of data, where the gathering is built up by discovering likenesses between data in light of their attributes. Such gatherings are named as Clusters. A relative investigation of clustering algorithms crosswise over two distinct data things is performed here. The execution of the different clustering algorithms is contrasted in view of the time brought with frame the evaluated bunches. The exploratory consequences of different clustering algorithms to shape bunches are portrayed as a chart. Consequently it can be finished up as the time taken to shape the groups increments as the quantity of bunch increments. The most distant first clustering algorithm takes not very many seconds to group the data things though the basic K Means sets aside the longest opportunity to perform clustering. The general objective of data mining procedure is to concentrate data from an expansive data set and move it into an understandable shape for sometime later .Clustering is essential in data examination and data mining applications. Clustering is a division of data into gathering of comparable articles. Each gathering called a bunch comprises of articles that are comparative amongst themselves and unique between contrast with objects of different gatherings. This paper is expected to investigation of all the clustering algorithms. In this paper we analyze a wide range of clustering strategies and gave a concise information about k-implies clustering.

Clustering, K-Means clustering algorithm, data mining, Clustering algorithm, Efficient K-Means, Filtered cluster, Filteredcluster, Farthestfirst

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[2] T. Kanungo, D. Mount, N. Netanyahu, C. Piatko and A.-MeansWu, “Clustering Algorithm: Efficient Analysis and Implementation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, Issue.7, Jul 2002.
[3] V. Jain, "Outlier Detection Based on Clustering Over Sensed Data Using Hadoop", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.2, pp.45-50, 2013.
[4] V.K. Gujare, P. Malviya, "Big Data Clustering Using Data Mining Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.9-13, 2017.
[5] P.K. Dhillon, A.S. Walia, "To evaluate and improve DBSCAN algorithm with normalization in data mining: A Review", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.35-39, 2017.
[6] Pritika Goel, "An Improved Load Balancing Technique in Weighted Clustering Algorithm", International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.80-92, 2016.
[7] S. Joshi, F.U. Khan, N. Thakur, "Contrasting and Evaluating Different Clustering Algorithms: A Literature Review", International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.87-91, 2014.

Savita Sahu, "A New Approach of K-Means Algorithm With M-Tree Algorithm: Survey Paper," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.261-263, 2017.

Mobile Ad-hoc Network for a settled network framework MANET shape a network to trade data. In this paper we are distinguishing affirmation forms in network formalization. There are many Network Routing Protocol which have their own particular benefits and negative marks for Abstraction between different administrations in MANET condition. For arrangement and administration organization in MANET it is to be required with a specific end goal to recognize deliberate affirmation and administration to set up best association amongst source and goal. In MANET affirmation needs AODV, DSR and DSDV protocol so affirmation delay, affirmation drop and affirmation succeed can be effortlessly computed by measurements table and ns2 reproduction is utilized to check this accuracy Ad-hoc networks (MANETs) square measure phenomenally defenseless to a scope of mischievous activities because of their essential focal points, together with absence of correspondence framework, short transmission power, and dynamic network design. To watch and relieve those mischievous activities, a few trust administration plans are given for MANETs. Most assume pre-characterized weights to work out however every evident unfortunate behavior adds to relate general compute of conviction.

AODV, DSR, DSDV, Trust Establishment, Fault tolerance system

[1] Pradeep Kumar Sharma, Shivlal Mewada and Pratiksha Nigam, "Investigation Based Performance of Black and Gray Hole Attack in Mobile Ad-Hoc Network", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.4, pp.8-11, 2013.
[2] Umesh Kumar Singh, Jalaj Patidar and Kailash Chandra Phuleriya, "On Mechanism to Prevent Cooperative Black Hole Attack in Mobile Ad Hoc Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.11-15, 2015.
[3] Italy Melina, “Radon Adaptive User Anonymity for Mobile Opportunistic Networks”, CHANTS’12, August 22, 2012
[4] Meenakshi Jamgade and Vimal Shukla , "Comparative on AODV and DSR under Black Hole Attacks Detection Scheme Using Secure RSA Algorithms in MANET", International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.145-150, 2016.
[5] Zygmunt J. Haas and Milen Nikolov, “Towards Optimal Broadcast in Wireless Networks”, October science direct 31-2010
[6]. Leena Pal, Pradeep Sharma, Netram Kaurav and Shivlal Mewada, "Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc –Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.5, pp.1-4, 2013.

Sagar Sharma, "Review paper Malicious Nodes in Manet: survey report," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.264-267, 2017.

Text mining is the use of data mining techniques to unstructured text in order to extract important and nontrivial knowledge. One of the key methods of text mining, or the unsupervised classification of related content into various categories, is text clustering. The performance of text clustering is being improved in this study. We looked on four areas of the text clustering algorithms: document representation, document similarity analysis, high dimension reduction, and parallelization. We suggest a collection of very effective text clustering techniques that focus on the special features of unstructured text databases. All of the suggested algorithms have undergone thorough performance studies. We contrasted these techniques with current text clustering algorithms in order to assess their performance.

Cluster Algorithm, Data Mining, bisecting k-means, FIHC, CFWS find CFWMS

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Arpit Agrawal, "Evaluation of Clustering Algorithm in Data Mining," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.268-273, 2017.