Volume-07 , Special Issue-05 , Mar - 2019 Go Back

This paper investigates the result of athletic games of Olympic history for the past 100 years. As a case study, we evaluate ZeroR classification machine algorithms on game datasets. Here we compare the dataset in ranking algorithms in order to determine the results like leading city and the winner of the game. In this paper, we used machine learning data mining tool WEKA for different analysis. We have provided an evaluation based on applying these classification methods on our datasets and measuring the accuracy of test results.

Classification, data mining tool, machine learning, Ranking, WEKA, ZeroR

[1] Osama K. Solieman, “Data Mining in Sports:A Research Overview” MIS Masters Project August 2006.
[2]E.W.T.NgaiaLiXiubD.C.K.Chaua, “Application of data mining techniques in customer relationship management: A literature review and classification” IEEE, March 2018.
[3] Lorena Siguenza-Guzman et. al. “Literature Review of Data Mining Applications in Academic Libraries”, The Journal of Academic Librarianship, Volume 41, Issue 4, 2015, pp. 499-510.
[4] T. Femina Bahari, M. Sudheep Elayidom, “An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour”, Procedia Computer Science, Volume 46, 2015, pp. 725-731.
[5] Mark A. Hall, Geoffrey Holmes, “BenchmarkingAttribute Selection Techniques for Discrete Class Data Mining”, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 3, May/June 2003.
[6] Andr´as London, J´ozsef N´emeth and Tam´as N´emeth. “Time-dependent Network Algorithm for Ranking in Sports”, Acta Cybernetica.
[7] Carson K. Leung and Kyle W. Joseph / Procedia Computer Science 35 ( 2014 ) 710 – 719.
[8] Eftim Zdravevski et.al. “System for prediction of the winner in a sports game” https://www.researchgate.net/publication/226597761.
[9] Witten, I.H., Frank, E. ”Data Mining: Practical machine learning tools and techniques (2nd Edition)”. Morgan Kaufmann. (2005).
[10]https://ingsistemastelesup.files.wordpress.com/2017/03/zeror-algorithm.pdf.

C.Lalitha, S. Arulselvarani, "Evaluation of Athletic Events of Olympic History for 100 Years Using Ranking Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.156-160, 2019.

Where there is technology there is always a scope for innovation. Blockchain technology is one such innovation that provides a decentralized solution for storage and immutable transactions that is hardly possible to fake. Blockchain is a subject undergoing intense study that has gained universal attention being the foundation of the most happening cryptocurrency. It is a distributed ledger that uses the appropriate consensus algorithm for secure transactions and records them all in an immutable chain of blocks hence forming a blockchain. Each block in a blockchain can be considered as page of a record that includes all the necessary details of any transaction. The transactions could be of any type based on the application the blockchain technology is being used for. The security and integrity of blockchain is achieved by using hashing and consensus algorithm. Hash is a unique number that determines a specific block. Each block contains the hash of the current block and also the hash of the previous block, any tampering with a block affects the validity of the following blocks making them invalid. Nodes in a blockchain are interconnected using peer-to-peer network and play a decisive role to validate any new transaction. Nodes come to a consensus using complex mathematical calculations after which the validated transaction records are added to the blockchain. Blocks that are once added to a blockchain can never be modified. A single node or a single network cannot control the entire database providing security in a trust less network. These spectacular features of blockchain has gained popularity in various fields other than cryptocurrency, to name a few real-estate, supply chain management and health care etc., This paper gives a compendious summary of blockchain with an overview of its fundamentals, its working and its diverse applications.

Blockchain, Decentralized, Security, Consensus, Transactions, Hashing, Digital Signatures

[1] W. Mougayar,” The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology”, Wiley Publisher,2016.
[2] A. Kaushik. A. Choudhary, C. Ektare, D. Thomas, S. Akram, “Blockchain- Literature Survey” In the Proceedings of the 2nd IEEE Conference on Recent Trends in Electronics Information & Communication Technology, India, pp.2145-2148, 2017.
[3] S. Nakamoto, “Bitcoin: A Peer – to – Peer Electronic Cash System”, 2008
[4] Z. Zheng, S. Xie, H. Dai, X. Chen, H. Wang, “An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends” In the Proceedings of the IEEE 6th International Congress on Big Data, pp.557-563,2017.
[5] D. Mingxiao, M. Xiaofeng, Z. Zhe, W. Xiangwei, C. Qijun, “A review of Consensus Algorithm of Blockchain” In the proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Canada, pp. 2567 – 2572, 2017.
[6] M. Castro, B. Liskov, “Practical Byzantine Fault Tolerance”, In the Proceedings of Third Symposium on Operating Systems Design and Implementation, USA, pp. 1 – 14, 1999.
[7] S. Seebacher, R. Schuritz, “Blockchain Technology as an Enabler of Service Systems: A Structured Literature Review”, In the Proceedings of Exploring Service Science: 8th International Conference, Italy, pp.12-23, 2017.
[8] K. Sultan, U. Ruhi, R. Lakhani, “Conceptualizing Blockchains: Characteristics & Applications”, In the Proceedings of 11th IADIS International Conference on Information Systems, Portugal, pp. 49 – 57, 2018.
[9] D. Puthal, N. Malik, S.P. Mohanty, E. Kougianos, G. Das, “Everything You Wanted to Know About the Blockchain: Its Promise, Components, Processes and Problems”, IEEE Consumer Electronics Magazine, Vol.7, Issue.4, pp. 6 – 14, 2018
[10] S. Thakur, V. Kulkarni, “Blockchain and Its Applications – A Detailed Survey”, International Journal of Computer Applications, Vol.180, No.3, pp.29 -35, 2017.
[11] P. Tasatanattakool, C. Techapanupreeda, “Blockchain Challenges and Applications” In the Proceeding of International Conference on Information Networking”, Thailand, pp. 473 – 475, 2018.
[12] https://innovation.wfp.org/project/building-blocks
[13] https://medrec.media.mit.edu/
[14] https://www.kodak.com/kodakone/default.htm
[15] https://kickcity.io/
[16] S. Olnes, J. Ubacht, M. Janssen, “Blockchain in Government: Benefits and implications of distributed ledger technology for information sharing”, Government Information Quarterly, Vol. 34, Issue. 3, pp. 355 -364, 2017.

Sumathy Kingslin, Rafath Zahra, "Compendious Summary of Blockchain", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.161-166, 2019.

Liver plays a vital role in human body. In the present scenario, Liver related diseases affect large number of peoples in India. Segmentation of liver image from computed tomography helps in disease diagnosis and making pre-planning decisions for hepatic surgery. This paper presents a segmentation method with the combination of K-means clustering, thresholding and morphological operations. The proposed segmentation scheme is applied on a 2-dimentional computed tomography abdominal image and the experimental result is evaluated using Dice similarity co-efficient and the measure is 94.46% against gold standard image.

CT Liver, K-means, Liver Segmentation, Morphology

[1] Allen SE, “The liver: Anatomy, Physiology, Disease and treatment”, North Eastern University Press, USA, 2002
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[3] Dushyant V.Sahani, Sanjeeva P.Kalva, “Imaging the Liver”, The Oncologist Hepatobiliary, pp.385-97, 2004.
[4] Belgherbi A, Hadjidj I, Bessaid A, “A Semi-automated method for Liver lesion extraction from a CT Image based on mathematical morphology”, Journal of Biomedical Sciences, Vol.2,No.2:4, pp.1-9, 2013.
[5] Wu W, Zhou Z, Wu S, Zhang Y, “Automatic Liver Segmentation on volumetric CT Images using Super voxel -Based Graph Cuts”, Computational and Mathematical Methods in Medicine, Hindwani Publishing corporation, 2016.
[6] Xiao Song, Ming Cheng, Boliang Wang, Shaohui Huang, Xiaoyang Huang, “Automatic Liver Segmentation from CT images using Adaptive Fast Marching Method”, Seventh International conference on Image and Graphics, 2013.
[7] Paola Campadelli, Elena Casiraghi, Andrea Esposito, “Liver Segmentation from computed tomography scans: A Survey and a new algorithm”, Artificial intelligence in Medicine, Vol.45, pp.185-196, 2009.
[8] Marcin Ciecholewski, “Automatic Liver Segmentation from 2D CT Images using an approximate contour model”, Journal of Signal Processing Systems, Vol. 74, Issue 2, P.151-174, 2014.
[9] J.B.MacQueen, "Some Methods for classification and Analysis of Multivariate Observations”, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, Vol. 1, pp.281-297, 1967.
[10] T W Ridler, S Calvard, “Picture thresholding using an iterative selection method”, IEEE Transactions on Systems Man and Cybernetics, Vol.8, pp. 630-632, 1978.
[11] J Serra, “Image Analysis and Mathematical Morphology”, Academic Press, London, 1982.
[12] L. Dice, “Measures of the amount of ecologic association between species”, Ecology, Vol. 26, pp.297-302, 1945.

S. Kiruthika , I. Kaspar Raj, "Segmentation of Liver from CT Abdomen using K-Means and Morphological Operations", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.167-171, 2019.

Content Based Image Retrieval (CBIR) is a technique, which is utilized for retrieving identical images from an image database. Dimensionality reduction of the feature space has a significant role in improving the classifiers’ performance. For concerns involving storage and retrieval efficacy, dimensionality reduction in CBIR systems is essential. This research work introduces an efficient and new approach for improving the performance of CBIR based on Scale Invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. After this, bat algorithm is presented for dimensionality reduction, which considerably increases the classification accuracy. This paper provides the comparison of the classification efficacy of classifiers including Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF) for CBIR. The experimental outcomes of the newly introduced classifiers are compared prior and after dimensionality reduction. The evaluation is performed on various image databases for showing the reliability of the newly introduced approach in terms of Precision, Recall, and Accuracy.

CBIR, Dimensionality Reduction, SIFT, Bat Algorithm, SVM, CART, RF, and Image Retrieval

[1]. Guo, J.M., Prasetyo, H. and Chen, J.H., 2015. Content-based image retrieval using error diffusion block truncation coding features. IEEE Transactions on Circuits and Systems for Video Technology, 25(3), pp.466-481.
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[3]. Datta, R., Joshi, D., Li, J. and Wang, J.Z. (2008) Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys, 40, 1-60.
[4]. Mingqiang, Y., Kidiyo, K. and Joseph, R. (2008) A Survey of Shape Feature Extraction Techniques. Pattern Recognition Techniques, Technology and Applications, ISBN: 978-953-7619-24-4, InTech.
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[6]. Liu, Y., Zhang, D., Lu, G. and Ma, W.Y., 2007. A survey of content-based image retrieval with high-level semantics. Pattern recognition, 40(1), pp.262-282.
[7]. Wang, X.H., Park, S.C. and Zheng, B., 2009. Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment. Physics in Medicine &Biology, 54(4), p.949.
[8]. Bakar, S.A., Hitam, M.S. and Yussof, W.N.J.H.W., 2013, October. Content-Based Image Retrieval using SIFT for binary and greyscale images. IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 83-88.
[9]. Suharjito, A. and Santika, D.D., `Content Based Image Retrieval Using Bag Of Visual Words And Multiclass Support Vector Machine. ICIC International, 2017. Vol. 11, no.10, pp. 1479–1488.
[10]. Giveki, D., Soltanshahi, M.A. and Montazer, G.A., 2017. A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern. Optik- International Journal for Light and Electron Optics, 131, pp.242-254.
[11]. Bama, B.S., Valli, S.M., Raju, S. and Kumar, V.A., 2011. Content based leaf image retrieval (CBLIR) using shape, color and texture features. Indian Journal of Computer Science and Engineering, 2(2), pp.202-211.
[12]. A. Vedaldi and B. Fulkerson, “Vlfeat: an open and portablelibrary of computer vision algorithms,” in Proceedings of theInternational Conference on Multimedia (MM ’10), pp. 1469–1472, 2010.
[13]. Preeti Kushwaha, Rashmi R. Welekar, 2016. Feature selection for image retrieval based on evolutionary computation. IJRET: International Journal of Research in Engineering and Technology. Vol.05,no. 07.PP56-62.
[14]. Sharma, S. and Dhole, A., 2013. Content Based Image Retrieval Based on Shape Feature using Accurate Legendre Moments and Support Vector Machines. International Journal of Science, Engineering and Computer Technology, 3(5), p.194.
[15]. Histograms, C.I.I., 2013. Bi-level classification of color indexed image histograms for content based image retrieval. Journal of Computer Science, 9(3), pp.343-349.
[16]. Pal, M., 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), pp.217-222.
[17]. Z. Mehmood, F. Abbas, T. Mahmood, M. A. Javid, A. Rehman,and T. Nawaz, “Content-based image retrieval based on visual words fusion versus features fusion of local and global features,” Arabian Journal for Science and Engineering, pp. 1–20, 2018.

M. Nester Jeyakumar, Jasmine Samraj, "Performance Analysis of Various Classifiers with Effective Dimensionality Reduction in Content-Based Image Retrieval", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.172-180, 2019.

A smart phone is one of the widely used electronic device and it is the pivot of all smart systems. In this paper we focused to find the stolen or lost android device and provide security to the data stored. The proposed system use a siren sound, when victim try to use the android device by putting wrong password at most three attempts and the background application of camera will automatically runs and capture victims face immediately. Here we use one central web data storage andthe mail id to retrieve whole device data where it will already have uploaded. The proposed system use a novel method called Location-Based Delivery (LBD) which combines the Short Message Service(SMS) and Global Positioning System (GPS) and further a realistic system for tracking a target movement is developed. The proposed approach LBD, consists of three primary types: Short message layout, location prediction, and dynamic threshold. Location is predicted using the current location, moving speed, and bearing of the target to calculate its next location. When the distance between the predicted location and the actual location exceeds a definite threshold, the target transmits a short message to the tracker to update its current location. To maintain the location tracking correctness and the number of short messages on the basis of the moving speed of the target, the threshold is vigorously adjusted.

Location-Based Delivery, Short Message Service, Global Position System, location prediction, dynamic threshold

[1]chetankymar,K.Labhade “android based map location tracking systemwithout using internet”International Journal of Advanced Research in Computer sciencevolume 7, No. 1, 2016.
[2]Vinigha .A, Priyanka.K, Sangeetha “Efficient location search using android mobile in offline mode” International Research Journal of engineering and Technology (IRJET) Volume: 05 Issue: 02, Feb-2018.
[3]Prof.SeemaVanjire, UnmeshKanchan,“Location based services on smart phone through the android application", International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Vol. 3, Issue 1, January 2014.
[4] M. Sarwat, J. J. Levandoski, A. Eldawy, and M. F. Mokbel, “Lars*: An efficient and scalable location-aware recommender system,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 6, pp. 1384–1399, Jun. 2014.
[5] A. S. ManavSinghal, “Implementation of location based services in android using gps and web services”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 2, January 2012.
[6] G. S. P. P. Prof. SeemaVanjire, UnmeshKanchan, “Location based services on smart phone through the android application", International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Vol. 3, Issue 1, January 2014.
[7] A. Magdy, A. M. Aly, M. F. Mokbel, S. Elnikety, Y. He, and S. Nath, “Mars: Real-time spatio-temporal queries on microblogs,” in Proc. IEEE 30th Int. Conf. Data Eng. (ICDE), Chicago, IL, USA, Mar./Apr. 2014, pp. 1238–1241.
[8] A. S. and V. B, “Location based intelligent mobile organizer” Mobile and Pervasive Computing Department TIFAC-CORE in Pervasive Computing Technologies IEEE, pp no. 488-491, 2011.
[9] Md.Mofijiul Islam ,Md.abdurRazzaquee,senior member, IEEE, Mohammad mahedihassan, member, IEEE, Walla nagy ,biaosong,member,IEEE. Mobile cloud based big health care data processing in smart cities(2017)
[10]Schneider. (2014). Go green in the city [Online]. Available: http:// 2014.gogreeninthecity.com/smart-cities.html
[11] A. Magdy, A. M. Aly, M. F. Mokbel, S. Elnikety, Y. He, and S. Nath, “Mars: Real-time spatio-temporal queries on microblogs,” in Proc. IEEE 30th Int. Conf. Data Eng. (ICDE), Chicago, IL, USA, Mar./Apr. 2014, pp. 1238–1241.
[12] M. Sarwat, J. J. Levandoski, A. Eldawy, and M. F. Mokbel, “Lars*: An efficient and scalable location-aware recommender system,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 6, pp. 1384–1399, Jun. 2014.
[13] Wei-Meng Lee, “Beginning Android Application Development”, Wiley India Pvt. Limited,pp 385-389, 2011
[14] Reto Meier, “ Professional Android 2 Application Development”, ISBN: 978-0-470-56552-0.March 2010.
[15] Masumi Nakamura, Marko Gargenta, “Learning Android”, 2nd Edition , O`Reilly Media, Inc. 2014.

A.JasmineJinitha, Rexline S.J. , A.JasmineSugil, "Offline Location Tracking Service and Image Capturing System Using Android Mobile", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.181-185, 2019.

Pharmacovigilance(PV) is the science and activities relating to the detection , assessment, understanding and prevention of adverse effects. Adverse effects means the abnormal conditions occurs in the body due to the drugs which are consumed by the peoples. The main process of pharmacovigilance is reporting about the safety and quality of the drugs. It makes the treatment for patients as worthy as possible. This process makes the subject of chemical compositions of the drugs worthy and correctly. This is one of the major process in drug industries. The proposed system use an ideal tool for this process is the reporting by means of forms which are available under medical bodies also by means of web applications for pharmacovigilance process. This paper presents the better results in clinical trials. The success or failure of pharmacovigilance activity depends on the reporting of suspected adverse reactions. Reports made by a health professional are an interpretation of information originally provided by a patient who has experienced the actual benefit or harm of a medicine taken.

adverse reactions, adverse effects, drugs, pharmacovigilance

[1] Overview of Pharmacovigilance Research & Reviews: Journal of Pharmacology and Toxicological Studies/ Amarendra KE Research & Reviews: Journal of Pharmacology and Toxicological Studies e-ISSN: 2319-9873
[2] The Safety of Medicines in Public Health Programmes Pharmacovigilance: an essential tool World Health Organization. ISBN 92 4 159391 1
[3] Dr Arvind Mathur, “WHO guidelines on safety monitoring of herbal medicines in pharmacovigilance”,World Health Organisation
[4] World Health Organization. “Pharmacovigilance: Ensuring the safe use of medicines” Oct 2004
[5] GeetaSharma,RahathKumar.”Pharmacovigilance in India and its impact in patient Management”. The Journal of current trends in Diagnosis and Treatment, 2017, 27-33
[6] “The Uppsala monitoring system”, WHO Collaborating Centre for International Drug Monitoring World Health Organization.
[7] Indian journal of pharmacology volume 50(2); mar-apr 2018
[8] Joerg H. Basic Principles of Pharmacovigilance and Data Sources.
[9] SachdevY.“Pharmacovigilance:SafetyMatters”,IndianPharmacology.2008,vol40, Supplement 1,s13-s16
[10] Kumar A. “ Past, Present and future of Pharmacovigilance in India”. Syst Rev Pharm 2011 2(1),55-58
[11] Naranjo CA, Busto U, Sellers EM, Sandor P, Ruis I, Roberts EA, et al. A method for estimating the probability of adverse drug reactions. Clin PharmacolTher. 1981;30:239–45.
[12] Hartwig SC, Siegel J, Schneider PJ. Preventability and severity assessment in reporting adverse drug reactions. Am J Hosp Pharm. 1992;49:2229–32.

A.JasmineJinitha, Rexline S.J., "Pharmacovigilance: An Essential Tool for Drug Safety Monitoring", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.186-190, 2019.

A new variant geodetic problem, circular geodetic is defined as follows: Let S={x_1,x_2,...,x_k,x_(k+1)=x_1} be a geodetic set of G. Then S is said to be a circular geodetic set of G, there exists an index i, 1≤i≤k, such that I[x_i,x_(i+1)] contains atleast a vertex v other than x_i and x_(i+1), also I[S]=V(G). The minimum number of vertices needed to form a circular geodetic set is called circular geodetic number of G and it is denoted by g_cir (G).

Circular geodetic, Complete bipartite, Hexagonal mesh network, Apollonian

[1] Jurczyk, M., Siegel, H. J., Stunkel, C. B., Interconnection Networks for Parallel Computers, Wiley Encyclopedia of Computer Science and Engineering, 2008.
[2] G.Chartrand, F. Harary, H. C. Swart and P. Zhang, Geodomination in Graphs, Bulletin of the ICA, 31(2001), 51-59.
[3] Pelayo, Ignacio M. Geodesic convexity in graphs. New York: Springer, 2013.
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[5] Pelayo, Ignacio M. Geodesic convexity in graphs. New York: Springer, 2013.
[6] Hansberg, Adriana, and Lutz Volkmann. "On the geodetic and geodetic domination numbers of a graph." Discrete Mathematics 310.15 (2010): 2140-2146.

S. Arul Amirtha Raja, D. Antony Xavier, "Circular Geodetic Number of Certain Graphs", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.191-193, 2019.

Nithyanantha Jothi and Thangavelu studied the properties of the product of two power sets and introduced the concept of binary topology. In this paper, properties of the product of arbitrarily n-power sets are discussed where n >2. Further an n-ary topology on the product of power sets similar to binary topology is introduced and studied.

Binary topology, n-ary sets, n-ary topology, product topology

[1] S.S.Benchalli , P.G.Patil, S.Abeda Dodamani and J.Pradeepkumar, “On Binary Soft Separation Axioms in Binary Soft Topological Spaces”, Global Journal of Pure and Applied Mathematics, vol.13, no.9, pp. 5393-5412, 2017.
[2] M.Jamal Mustafa, “On Binary Generalized Topological Spaces”, General Letters in Mathematics, vol.2, no.3, pp.111-116, 2017.
[3] M.Lellish Thivagar, J. Kavitha, “On Binary Structure of Supra topological spaces”, Bol.Soc. Paran. Mat., vol.35, no.3, pp.25-37, 2017.
[4] S.Nithyanantha Jothi, “Binary semicontinuous functions”, International Journal of Mathematics Trends and Technology, vol.49, no.2, pp.152-155, 2017.
[5] S.Nithyanantha Jothi , P.Thangavelu, “Topology Between Two Sets”, Journal of Mathematical Sciences and Computer Applications, vol.1, no.3, pp.95-107, 2011.
[6] S.Nithyanantha Jothi , P.Thangavelu, “On Binary Topological Spaces”, Pacific-Asian Journal Mathematics, vol.5, no.2, pp.133-138, 2011.
[7] S.Nithyanantha Jothi, P.Thangavelu, “Generalized Binary Regular Closed Sets”, IRA International Journal of Applied Sciences, vol.4, no.2, pp.259-263, 2011.
[8] S.Nithyanantha Jothi, P.Thangavelu , “On Binary Continuity and Binary Separation Axioms”, Ultra Scientists vol.24, no.1A, pp.121-126, 2012.
[9] S.Nithyanantha Jothi, P.Thangavelu, “Binary semiopen sets in Binary Topological Spaces”, International Journal of Mathematical Archiv, vol.7, no.9, pp.73-76, 2016.
[10] R.Seethalakshmi, K.Kamaraj, “Nearly Binary Open Sets in Binary Topologica; Spaces”, International Conference On Applied and Computational Mathematics(ICACM- 2018),
Erode Arts and Science College(Autonomous) Rangampalaym, Erode-638009, Tamilnadu, India, 2018.

R.Seethalakshmi, M.Kamaraj, "Extension of Binary Topology", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.194-197, 2019.

For a given χ-coloring of a graph (V,E,ψ_G) , a dominating set S⊆V(G) is said to be dom-colouring set if it contains at least one vertex from each colour class of G. The dom-chromatic number is the minimal cardinality taken over all dom-colouring sets and is denoted by γ_dc (G). In this paper we have obtained the dom-chromatic number of various types of graphs like star graphs, windmill graphs, ladder graphs, comb graphs and for cycles.

Dominating set, Dom-colouring set, Star graphs, Windmill graphs, Ladder graphs, Comb graphs

[1] C. Berge, “Theory of Graphs and its Applications”, Methuen, London, 1962.
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Usha. P, Joice Punitha. M., Beulah Angeline E. F., "Dom-chromatic Number of certain graphs", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.198-202, 2019.

An efficient infinite Kolakoski sequence that’s not even in any particular order can be generated in two dimensional [2D] array of size (3x3) over a binary alphabet Ʃ={1,2} is introduced and it is denoted by K_((i,j))^3c- (i-blocks, j – positions, 3c- 3rd column). In this paper first 66 blocks with 100 positions from Kolakoski sequence is considered and 2D arrays are analyzed. Also combinatorial properties of the basis arrays are studied.

2D word, Block, Fibonacci, Kolakoski, Palindrome

[1] Valerie Berthe, Srecko Brlek, Philippe Choquette, “Smooth words over arbitrary alphabets:Theoretical Computer Science, volume 341, issues 1-3, (2005) 293-310
[2] Bernd Sing, “Kolakoski-(2m,2n) are Limit-Periodic Model Sets: J. Math.Phys. 44, No 2, 899-912 (2003).
[3] Genevieve Paquin, Srecko Brlek, “Extremal Generalized Smooth Words: Computer Science [cs] / Discrete Mathematics [cs.DM], 2009.
[4] Bernd Sing, “More Kolakoski Sequences: [Math.CO], 2010.
[5] S. Brlek, A. Ladouceur, A note on differentiable palindromes, Theoret. Comput. Sci., 302 (2003) 167–178
[6] S. Brlek, S. Dulucq, A. Ladouceur, L. Vuillon, Combinatorial properties of smooth infinite words, MFCS’04, Prague, Czech Republic (2004) submitted.
[7] F. M. Dekking, What is the long range order in the Kolakoski sequence, in R. V. Moody (ed.), The mathematics of Long-Range Aperiodic Order, Kluwer Academic Publishers (1997) 115–125.
[8] S. Brlek, S. Dulucq, A. Ladouceur, L. Vuillon, Combinatorial properties of smooth infinite words, Theoret. Comput. Sci. 352 (2006) 306–317.
[9] V. Chv´atal, Notes on the Kolakoski Sequence, DIMACS Techn. Rep. 93-84, February 1994. [8] F. M. Dekking, On the structure of self generating sequences, S´eminaire de th´eorie des nombres de Bordeaux, expos´e 31 (1980–1981).
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[11] B. Steinsky, A recursive formula for the Kolakoski sequence A000002, J. Integer Sequences 9(3): 06.3.7 (2006), 5pp. The corresponding numerical study of the letter frequencies is available at http://www.lirmm.fr/ monteil/blog/BruteForceKolakoski.
[12] E.J. Kupin and E.S. Rowland, Bounds on the frequency of 1 in the Kolakoski word, preprint. http://arxiv.org/abs/0809.2776/.

N Jansi Rani, L Vigneswaran, V R Dare, "A New Recursive Two Dimensional Pattern On Kolakoski Sequence", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.203-207, 2019.