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Geodetic number and its variants is one of the widely studied topic in the field of graph theory. Over the recent years many variants of geodetic number have been extensively studied in the literature. In this paper, we introduce a new variation called doubly edge geodetic number and proved that it is Np-complete. The doubly edge geodetic number for some standard graphs is determined. Furthermore, certain characterization and realization results of doubly edge geodetic number are discussed.

doubly edge geodetic set, doubly geodetic set, geodesic, geodetic set

[1] Buckley F., Harary F., Distance in Graphs(Addison- Wesley, Redwood City, CA, 1990).R. Solanki, “Principle of Data Mining”, McGraw-Hill Publication, India, pp. 386-398, 1998.
[2] Harary F., Graph Theory (Reading, MA: Addison-Wesley,1969).
[3] Atici M., On the edge geodetic number of a graph, International Journal of Computer Mathematics, 80, 2003,853-861
[4] Santhakumaran A. P., and John J., Edge geodetic number of a graph, J. Discrete Math. Sci.Cryptography, 10(3), 2007,415-432.
[5] Santhakumaran A. P., and Jebaraj T., Double geodetic number of a graph, DiscussionesMathematicae Graph Theory 32(1), 2012, 109-119.
[6] Chartrand G., Harary F., and Zhang P., On the geodetic number of a graph,Networks, 39(1), 2002, 1-6.

D. Antony Xavier, Elizabeth Thomas, "On the Doubly Edge Geodetic Number of a Graph", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.208-212, 2019.

An eigenvalue of a graph is an eigenvalue of its adjacency matrix. The energy of a graph is the sum of absolute values of its eigenvalues. Two graphs having same energy and same number of vertices are called equienergetic graphs. One might be interested to know, as to how the energy of a given graph can be related with the graph obtained from original graph by means of some graph operations. As an answer to this question we have considered the Cartesian product of two graphs. In this paper we obtain the eigenvalues and energy of Cartesian product of two graphs from the eigenvalue of the given graph.

Cartesian Product, Adjacency Matrix, Eigenvalues, Energy of graph

[1] Andries E. Brouwer, Willem H. Haemers, “Spectra of Graph”, Monograph, Springer, February 1, 2011
[2] S. Avgustinovich and D. Fon-der-flaass, “Cartesian Products of Graphs and Metric Spaces”, Europ. J. Combinatorics, pp.847-851 (2000).
[3] R. Balakrishnan, “The Energy of a Graph”, Lin. Algebra Appl. 387 ,pp.287-295 (2004).
[4] R. Balakrishnan, K. Ranganathan, “A Textbook of Graph Theory”, Springer, New York, 2000.
[5] R. B. Bapat, S. Pati, “Energy of a graph is never an odd integer”, Bull. Kerala Math. Assoc.1 129-132 (2004).
[6] D. Cvetkovi_c, P. Rowlison, S. Simi_c, “An Introduction to the Theory of Graph Spectra”, Cambridge Univ. Press, Cambridge, 2010.
[7] Douglas B. West, “Introduction to Graph Theory”, University of Illinois, 2nd edition, 2001.
[8] I. Gutman, “The Energy of a Graph”, Ber. Math Statist. Sekt. Forschungsz. Graz 103,pp.1-22 (1978).
[9] I. Gutman, Y. Hou, H.B.Walikar, H.S. Ramane, P.R. Hampiholi, “No Hückel graph is Hyperenergetic”, J. Serb. Chem. Soc. 65 (11) 799–801 (2000).
[10] R. A. Horn, C. R. Johnson, “Topics in Matrix Analysis”, Cambridge Univ. Press, Cambridge, 1991
[11] S. Lang, “Algebra”, Springer, New York, 2002.
[12] X. Li, Y. Shi, I. Gutman, “Graph Energy”, Springer, New York, 2012.
[13] S. Pirzada, I. Gutman, “Energy of a graph is never the square root of an odd integer”, Appl. Anal. Discr. Math. 21, pp.18-121 (2008).
[14] Samir K. Vaidya and Kalpesh M. Popat, “Some new results on Energy of Graphs”, Match Commun. Math. Comput. Chem. 77, pp.589-594 (2017).
[15] M. A. Sriraj, “Some studies on energy of graphs, Ph. D. Thesis, Univ. Mysore, India, 2014.
[16] H.B. Walikar, I. Gutman, P.R. Hampiholi, H.S. Ramane, “Graph Theory Notes” New York Acad. Sci.41, pp.14–16 (2001).
[17] H.B.Walikar, H.S. Ramane, P.R. Hampiholi, “Energy of trees with edge independence number three”, preprint.

R.Prabha, Vadivukkarasi P.R, "Energy of Cartesian product of Graphs", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.213-215, 2019.

A language generating model called Pattern languages was introduced by Dassow , motivated by Angulin’s Pattern languages that use strings as language descriptors. Investigation of patterns has been of relevance in many areas such as combinatorics on words, learning theory and so on. Pattern grammars provide an alternative method in defining languages in automaton theory. Several methods to generate two-dimensional languages known as array languages or picture languages have been defined and investigated in literature and they have been extending the techniques and results of formal string language theory. A picture is defined as a rectangular array of terminal symbols in a rectangular plane. In this paper we extend the Pattern languages defined for strings by Dassow, to a two-dimensional case, while the simplicity and compactness of their descriptors as defined in one dimensional case are preserved. Hence, Two-dimensional Pattern languages are defined and investigated for their closure properties based on array operations.

Two-dimensional patterns, Component, Two-dimensional axioms, Catenation, Factorization of arrays

[1] D. Angulin, “Finding Pattern common to set of strings,” Journal of Computer and System Sciences 21, 46-62, (1980)
[2] D. Giammarresi, A. Restivo, “Recognizable picture languages," International Journal of Pattern Recognition and Artificial Intelligence. 6:31-46, 1992.
[3] H. Fernau, Markus L. Schmid, K.G. Subramanian, “Two-Dimensional Pattern Languages”, In S.Bensch, F. Drewes, R. Freund, and F.Otto, editors, “Fifth Workshop on Non-Classical Models for Automata and Applications, NCMA, Volume 294 of books@ocg.at, pp.117-132. Osterreichische Computer Gesellschaft, 2013.
[4] J. Dassow, G.Paun, A.Salomaa, “Grammars based on Patterns,” International Journal of Foundations of Computer Science, Vol 4: 1-14, 1993.

Christopher Kezia Parimalam, J.D. Emerald, "Two – Dimensional Grammars Based on Patterns", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.216-220, 2019.

Deep learning is a part of the broader family of Machine Learning. It refers to learning multiple levels of representation and abstraction that helps to understand data such as images, sound and text. This paper aims at giving an overview of deep learning, its applications and an approach for Network Intrusion Detection. KDDCup dataset is used for Intrusion detection and a comparison of different deep learning techniques applied for Intrusion Detection is made. A special focus is given on Self taught learning using Sparse Coding and its usage in classification. Self taught learning (STL) is a machine learning framework for using unlabeled data in supervised classification tasks. It is a deep learning approach that comprises of two stages for the classification task. Initially, a good feature representation is learnt from a large collection of unlabeled data, called as Unsupervised Feature Learning (UFL). Finally, the learnt representation is applied to labeled data and then classification task is performed.

Network Intrusion Detection, Classification, Deep Learning, Self-Taught Learning

[1]. Adebayo O. Adetunmbi, Samuel O. Falaki (2008). “Network Intrusion Detection based on Rough Set and k-Nearest Neighbour”, International Journal of Computing and ICT Research, Vol.2, No.1, pp.60-66., http://www.ijcir.org/volume1number2/article7.pdf.
[2]. Bhatnagar and Vishal. (2014). “Data Mining and Analysis in the Engineering Field”, IGI Global.
[3]. Brian Lee, Sandhya Amaresh, Clifford Green, Daniel. (2018). “Comparative Study of Deep Learning Models for Network Intrusion Detection”, SMU Data Science Review”, Vol.1, No.1.
[4]. Breiman, L. (2001). “Random Forests”, Machine Learning, Vol.45, Issue 1, pp.5-32.
[5]. Dietterich, T.G. (2000). “An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization”, Machine Learning, Vol.40, Issue 2, pp.139–157.
[6]. Ho, T.K. (1998). “The Random Subspace method for constructing decision forests”, IEEE transaction on Pattern Analysis and Machine Intelligence, Vol.20, Issue 8, pp.832-844.
[7]. KDD CUP DATASET (1999). http://kdd.ics.uci.edu/databases/kddcup99/
[8]. Kok-Chin Khor, et al (2009). “From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective”, American Journal of Applied Sciences, Vol.6, No.11, pp.1948-1959.
[9]. Lee,W., Stolfo,S.J., and Mok, K.W. (1999). “Algorithms for Mining System Audit Data”, Proceedings of KDD.
[10]. Neveen I.Ghali. (2009). “Feature Selection for Effective Anomaly Based Intrusion Detection”, International Journal of Computer Science and Network Security, Vol.9, No.3, pp.285-289.
[11]. “Understanding Intrusion Detection Systems”, SANS Institute InfoSec Reading Room.
[12]. Quamar Niyaz, Weiqing Sun, Ahmad Y Javaid, Mansoor Alam (2015). “A Deep Learning Approach for Network Intrusion Detection System”, ACM Digital Library, BICT’15, pp.21-26.
[13]. Venkata Lakshmi, S. and Edwin Prabakaran, T. (2014).
“Application of k-Nearest Neighbour Classification Method for Intrusion Detection in Network Data”, International Journal of Computer Applications, (0975-8887) Vol.97, No.7, pp.34-37.
[14]. Venkata Lakshmi, S. and Edwin Prabakaran, T. (2014). “A comparative study of classification of application of classification algorithms on KDDCup dataset to detect intrusions using WEKA tool”, International Journal of Engineering Research and Technology, Conference Proceedings of RACMS, pp.69-71.
[15]. Venkata Lakshmi, S. and Edwin Prabakaran, T. (2015). “Performance Analysis of Multiple Classifiers on KDD Cup dataset using WEKA tool”, Indian Journal of Science and Technology, Vol.8, No.17, pp.1-10.
[16]. Venkata Lakshmi, S. and Edwin Prabakaran, T. (2015). “Deployment of Models developed in Knowledge Flow Process using WEKA tool on KDDCup dataset” presented in the International Conference on Information Technology organized by Thiruthangal Nadar College, Chennai.
[17]. Venkata Lakshmi, S. and Edwin Prabakaran, T. (2015). “Feature selection and classification of network connection data into normal or attack records using WEKA tool”, Proceedings of the International Conference on Recent Trends in Computer Science and Digital Technology, ICCSDT-2015, pp.72-77, at Guru Shree Shanthi Vijay Jain college, Chennai and received the BEST PAPER AWARD.
[18]. Venkata Lakshmi, S. and Edwin Prabakaran, T. (2016). “Feature selection and Identification of Attacks in Network connection records using classifiers in WEKA tool”, Proceedings of the WCC Centenary International Conference on Viable synergies in Mathematical and Natural Science, pp.215-227 at WCC, Chennai.
[19]. Venkata Lakshmi, S. and Edwin Prabakaran, T. (2018). “Application of Ensemble, Voting and Stacking methods for Better Classification of Network Intrusion Detection and Improving the Performance of Random Forest Method by a new method of Feature Selection”, Mathematical Sciences International Research Journal, Vol.7, Spl.Issue 4, pp.6-16.
[20]. Xindong Wu (2008). “Survey Paper, Top 10 algorithms in data mining”, Knowledge Information Systems, Vol.14, pp.1–37.

S. Venkata lakshmi, T.Edwin Prabakaran, "A Study on Deep Learning approach for Network Intrusion Detection", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.221-224, 2019.

Cloud Computing is a potential paradigm employed for the deployment of applications on the Internet. Cloud is an on-demand computing service that offers a dynamic environment for the users to guarantee Quality of Service (QoS) onjjjjj’ data in cloud data centers. Security is an important role in cloud data storage while the services provided storing the data in the cloud. Most of the research works have been designed for secure cloud data storage. However, cloud users still have security issues with their outsourced data. In order to overcome such issues, we surveyed towards the fragmented hashing methods and data storage techniques in cloud. The main goal of this survey is to analysis the different method towards hashing and security methods to store data in cloud environment based on the metrics for different methods on the performance in terms of execution time and data retrieval efficiency.

Cloud data storage, Cloud users, Security, Confidentiality, Fragmented data, Query Processing

[1] Lifeng Guo, Wei-Chuen Yau, “Efficient Secure-Channel Free Public Key Encryption with Keyword Search for EMRs in Cloud Storage”, Journal of Medical Systems, Springer, Volume 39, Issue 11, Pages 1-11, 2015.
[2] Zuojie Deng, Kenli Li, Keqin Li and Jingli Zhou, “A multi-user searchable encryption scheme with keyword authorization in cloud storage”, Future Generation Computer Systems, Elsevier, Pages 1-25, 2016.
[3] Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang, “Dual-Server Public-Key Encryption with Keyword Search for Secure Cloud Storage”, IEEE Transactions on Information Forensics and Security, Volume 11, Issue 4, Pages 789 – 798, April 2016.
[4] Youwen Zhu, Zhiqiu Huang, Tsuyoshi Takagi, “Secure and controllable k-NN query over encrypted cloud data with key confidentiality”, Journal of Parallel and Distributed Computing, Elsevier, Volume 89, Pages 1–12, March 2016.
[5] Xu An Wang, Fatos Xhafa, Weiyi Cai, Jianfeng Ma, Fushan Wei, “Efficient privacy preserving predicate encryption with fine-grained searchable capability for Cloud storage”, Computers and Electrical Engineering, Elsevier, Volume 56, Pages 871–883, November 2016.
[6] Ramalingam Sugumar and Sharmila Banu Sheik Imam, “Symmetric Encryption Algorithm to Secure Outsourced Data in Public Cloud Storage”, Indian Journal of Science and Technology, Volume 8, Issue 23, Pages 1-5, 2015.
[7] Valentina Ciriani, Sabrina De Capitani Di, Vimercati, Sara Foresti, Sushil Jajodia, Stefano Paraboschi, Pierangela Samarati, “Combining fragmentation and encryption to protect privacy in data storage”, ACM Transactions on Information and System Security (TISSEC), Volume 13 Issue 3, Pages 1-33, July 2010.
[8] Swapnil V.Khedkar , A.D.Gawande, “Data Partitioning Technique to Improve Cloud Data Storage Security”, International Journal of Computer Science and Information Technologies, Volume 5, Issue 3, Pages 3347-3350, 2014.
[9] R.Kirubakaramoorthi, D. Arivazhagan and D. Helen, “Survey on Encryption Techniques used to Secure Cloud Storage System”, Indian Journal of Science and Technology, Volume 8, Issue 36, Pages 1-7, 2015.
[10] Pothula Sujatha, “An Authentication Based Secure Data Storage in Cloud Computing”, International Journal of Advanced Computing and Electronics Technology (IJACET), Volume 1, Issue 1, Pages 8-13, 2014.
[11] Xinyue Cao, Zhangjie Fu and Xingming Sun, “A Privacy-Preserving Outsourcing Data Storage Scheme with Fragile Digital Watermarking-Based Data Auditing”, Journal of Electrical and Computer Engineering, Hindawi Publishing Corporation, Volume 2016, Article ID 3219042, Pages 1-7, 2016.
[12] Tonghao Yang, Junquan Li, and Bin Yu, “A Secure Ciphertext Self-Destruction Scheme with Attribute-Based Encryption”, Mathematical Problems in Engineering, Hindawi Publishing Corporation, Volume 2015, Article ID 329626, Pages 1-8, 2015.
[13] Lan Zhou,Vijay Varadharajan,and Michael Hitchens, “Trust Enhanced Cryptographic Role-based Access Control for Secure Cloud Data Storage”, IEEE Transactions on Information Forensics and Security, Volume 10, Issue 11, Pages 2381 – 2395, 2015.
[14] Joseph K. Liu, Kaitai Liang, Willy Susilo, Jianghua Liu, Yang Xiang, “Two-Factor Data Security Protection Mechanism for Cloud Storage System”, IEEE Transactions on Computers, Volume 65, Issue 6, Pages 1992 – 2004, 2016.
[15] Wenjing Lou, Kui Ren, Qian Wang, Sherman S.M. Chow, Cong Wang, “Privacy-Preserving Public Auditing for Secure Cloud Storage”, IEEE Transactions on Computers, Volume 62, Issue 02, Pages 362-375, 2013.
[16] Yogesh V. Bhapkar, Rakesh S. Gaikwad, Milind R. Hegade, “Providing Security and Privacy to Cloud Data Storage”, International Journal of Computer Science and Information Technologies, Volume 6, Issue 2, Pages 969-971, 2015.
[17] Muhammad Usman, Mian Ahmad Jan, Xiangjian He, “Cryptography-based secure data storage and sharing using HEVC and public clouds”, Information Sciences, Elsevier, Volume 387, Pages 90–102, 2017.
[18] Dharavath Ramesh, Rahul Mishra, Damodar Reddy Edla, “Secure Data Storage in Cloud: An e-Stream Cipher-Based Secure and Dynamic Updation Policy”, Arabian Journal for Science and Engineering, Springer, Pages 1–11, 2016.
[19] Tao Jiang, Xiaofeng Chena, Jin Li, Duncan S. Wong, Jianfeng Ma, Joseph K. Liu, “Towards secure and reliable cloud storage against data re-outsourcing”, Future Generation Computer Systems, Elsevier, Volume 52, Pages 86–94, November 2015.
[20] Peng Yong, Zhao Wei, Xie Feng, Dai Zhong-Hua, Gao Yang, Chen Dong-Qing, “Secure cloud storage based on cryptographic techniques”, The Journal of China Universities of Posts and Telecommunications, Elsevier, Volume 19, Supplement 2, Pages 182–189, October 2012.
[21] Dandan Yuan, Xiangfu Song , Qiuliang Xu , Minghao Zhao, Xiaochao Wei , Hao Wang and Han Jiang, “An ORAM-based privacy preserving data sharing scheme for cloud storage”, Journal of Information Security and Applications, Elsevier, Volume 39, Pages 1-9, April 2018.
[22] Shangping Wang, Jian Ye and Yaling Zhang, “A keyword searchable attribute-based encryption scheme with attribute update for cloud storage”, PLOS One, Volume 13, Issue No.5, Pages 1-19, May 2018.
[23] Yunxue Yan, Lei Wu, Ge Gao, Hao Wang and Wenyu Xu, “ A dynamic integrity verification scheme of cloud storage data based on lattice and Bloom filter”, Journal of Information Security and Applications, Elsevier, Volume 39, Pages 10-18, April 2018.
[24] Nesrine Kaaniche and Maryline Laurent, “Data Security and Privacy preservation in Cloud Storage Environments based on Cryptographic Mechanisms”, Computer Communications, Elsevier, Volume 111, Pages 120-141, October 2017.

Jegadeeswari, Dinadayalan, Gnanambigai, "A Review on Fragmented hash based Storage techniques for Query Processing in Cloud Data Storage", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.225-229, 2019.

Several business problems have many possible solutions across many variables. The multivariate techniques, such as Discriminant Analysis, are often used for solving such business problems. In this Paper, the Discriminant Analysis Technique is used to generate a set of well-defined rules, which would be very helpful to the HR department of an IT Company at the time of new recruits.

Multivariate Techniques; Discriminant Analysis

[1] J. Bughin, “Big Data, Big Bang? “ , Journal of Big Data, Vol. 3, Issue 2, pp. 1-14, 2016.
[2] C. Muthu and M.C. Prakash, “Impact of Hadoop EcoSystem on Big Data Analytics”, International Journal of Exclusive Management Research – Special Issue, Vol. 1, Issue 1, pp. 88-90, 2015.
[3] C.Muthu and M.C. Prakash, “Building a Price predictor for an Auctioning Website”, RETELL, Vol. 15, Issue 1, pp. 135-137, 2015.

S. Jeeva Kumar, C. Muthu, "Discriminant Analysis of IT Employees’ Performance", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.230-231, 2019.

Learning is a combined factor in education. Individual learning styles differs from each student to student. The very term “learning styles” means, the method of understanding the students learning behavioural pattern. As not all student learns the same way the methods of observing, absorbing the concept, the methods of implementing the read facts and the memory of retaining the learned information differ from individual to individual. Every single student learns in their own perceptive way and not all students learn and understand the same way. Individual learning of concept depends on various factors such as cognitive factor, environmental factor, experimental factor as well as emotional factor and so do the styles also depend upon instructional design, individual behaviour, need to master the concept, e-materials, other digital resources and teaching guidance. Each individual follow a different processes and categorizes information differently based on personal requirements. The ways of social interaction also differs from person to person. In this paper the varied learning styles along with blended methods which, is the combination of both classical and non-classical education programs are well discussed towards competence enhancement benefitting the educational domain

General styles(GS), Case of repetitive style(RS), Content based style(BS), Real world implementation style(IS)

[1] David Smith, “ Advantages & Disadvantages of Different Learning Styles; 2018
[2]Pattama Chandavimol, Onjaree Natakuatoong, Pornsook Tantrarungroj, ”Blended Training Model with Knowledge Management and Action Learning Principles to Develop Training Program Design Competencies”, International Journal of Information and Education Technology, Vol. 3, No. 6, December 2013
[3] David Smith “Advantages & Disadvantages of Different Learning Styles”, June 25, 2018
[4] Buket Akkoyunlu , Meryem Yilmaz Soylu ,“A Study of Student’s Perceptions in a Blended Learning Environment Based on Different Learning Styles”, Educational Technology & Society 11 (1), 183-193. (2008)
[5] Kristen Shand , Susan Glassett Farrelly ,“Using Blended Teaching to Teach Blended Learning: Lessons Learned from Pre-Service Teachers in an Instructional Methods Course “,Journal of Online Learning Research (2017)
[6]Alina Zapalska, Dallas Brozik, “Learning styles and online education”, Volume 23, Issue 5, 2006
[7] Diana J. Muir,” Adapting Online Education to Different Learning Styles”, National Educational Computing Conference, “Building on the Future” 1 July 25-27, 2001
[8] Joanna Poon, ”Use of blended learning to enhance the student learning experience and engagement in propertyeducation”, Volume 30 Issue 2,Published: 2012
[9]OnlineInteractions(http://tutorials.istudy.psu.edu/learningonline/learningonline7.html)
[10]Pattama Chandavimol, Onjaree Natakuatoong, Pornsook Tantrarungroj ,“Blended Training Model with Knowledge Management and Action Learning Principles to Develop Training Program Design Competencies”, International Journal of Information and Education Technology, Vol. 3, No. 6, December 2013
[11] Dr. Raghad Dwaik, Mr. Abdulmuti Jweiless,Dr. Salah Shrouf , “Using Blended Learning to Enhance Student Learning in American Literature Courses”, volume 15 issue 2, April 2016
[12] Katherine D . Arbuthnott, Gregory P . Krätzig, “Effective teaching: Sensory learning styles versus general memory processes”, Volume 4, Article 2, 2015
[13] Michael Pearn ,“10 Steps to Improve Your Learning”, (2006)
[14]“Improving Own Learning and Performance”, (http://dera.ioe.ac.uk/7642/1/Improving%20Own%20Learning%20and%20Performance.pdf)
[15] Khanyie Dlamin, “How the Blended Learning concept refines Education”
[16] Costa and Kallick, “The Art Costa Centre For Thinking”
[17] Dr. Raghad Dwaik, Mr. Abdulmuti Jweiless, Dr. Salah Shrouf, “Using Blended Learning to Enhance Student Learning in American Literature Courses”, TOJET:The Turkish Online Journal of Educational Technology –volume 15 issue 2, April 2016
[18] J. Gervais, “The operational definition of competency- based education”, March­2016
[19] Deb Peterson, “The Learning Styles Controversy - Arguments For and Against, A collection of arguments regarding the validity of learning styles”, March 08, 2017
[20] Lydia Leimbach “Using Blended Learning in the Classroom”, March 9, 2015

Ananthi Sheshasaayee, S. Malathi, "Adaptation of a Blended Method for Various Learning Styles to Enhance Competence in the Educational Domain", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.232-235, 2019.

In today’s scenario blended learning is becoming more common in a very wide range of learning environment. Blended learning is a combination of traditional (face –to-face) learning and online digital media learning. Blended learning is also termed as hybrid learning or mixing mode instruction. Blended learning is helpful in the presence for both teachers as well to students. Blended learning uses and online technology is not just a supplement, but a transform to improve the learning process. In a blended-learning course, students might attend a class taught by a teacher in a traditional classroom and also as an independent online classroom. This type of learning approach ensures the learner in engaging and driving his or her individual learning experience. The benefit of blended learning is time saving, cost reduction and flexible. This paper presents the review of various types of blended learning model and types of evaluation such as formative and summative evaluation.

blended learning(BL), formative evaluation, summative evaluation, traditional learning, online learning.

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Ananthi Sheshasaayee, M.Gayathri, "A Review Based Study for Evaluation of Various Blended Learning Techniques in the Educational Domain", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.236-239, 2019.

The main task of this work is to survey the various medical image segmentation methods analysed by researchers. Segmentation plays a crucial role in medical image processing. It is often used to pinpoint the objects and retrieve pertinent information in an image. Image is acquired with collection of objects having different intensities. The process of image segmentation is assessed through the different intensity level of the objects. Segmentation basically starts from threshold, histogram, clustering, edge based and many other methods. This paper analyses various medical image segmentation methods with their applications. Also it discusses with recent developments in segmentation techniques that are proposed for multiple diagnostic issues.

Clustering, Histogram, Medical Image Segmentation, Thresholding, Region Based

[1] Li Hao, Leow Wee Kheng “Registration-Based Segmentation of Medical Images”, 2006.
[2] Waseem Khan “Image Segmentation Techniques: A Survey”, Journal of Image and Graphics, Vol. 1, No. 4, 2013. doi : 10.12720/joig.1.4.166-170
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[4] Annemie Ribbens, Jeroen Hermans, Frederik Mae, “Unsupervised Segmentation, Clustering and Group wise Registration of Heterogeneous Populations of Brain MR Images”, IEEE Transactions on medical imaging Vol. 33, No. 2, 2014.
[5] Hau-Lee Tong, Mohammad Faizal Ahmad Fauzi, and Su-Cheng Hawl, “Ventricles segmentation and matching for content based medical image retrieval”, 10th International Conference on Information Science, Signal Processing and their Applications, IEEE, 2010. doi: 10.1109/ISSPA.2010.5605414.
[6] Wuxia Zhang, Pingkun Yan, Xuelong Li, “Estimating patient-specific shape prior for medical image segmentation”, pp.1451-1454, IEEE 2011. doi: 10.1109/ISBI.2011.5872673
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Jeevitha Sivasamy, T. S. Subashini, "Segmentation in Medical Image Processing - A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.240-245, 2019.

Wireless Sensor Networks (WSNs) has gained special deliberation among research groups with its assuming technology in wireless communication field. Wireless sensors are contiguously scattered and enthusiastic for recording and monitoring the environmental physical activity such as target tracking, defences, disaster relief and many more. The important function of sensor network is to collect and sends the data to its destination where the awareness of location (Viz. location of data) is a crucial challenge in (WSNs). This kind of information can be availed using localization techniques. For many wireless sensor networks applications, Localization has been regarded as one of the significant and aiding technology. It is also a way to govern the location of sensor nodes and also it is highly favourable to design low cost efficient localization mechanism for (WSNs). This study presented an overview of localization techniques, different classification methods, reviewed important localization algorithm, and summarized their advantages and disadvantages for Wireless Sensor Networks and few possible future research directions.

Localization techniques; sensor nodes; range-based localization; range-free localization; wireless sensor networks

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Syed Jamalullah.R, V. RajaLakshmi, "Assessment of Localization Performances in Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.246-250, 2019.