S. No

Volume-8 Issue-4C, April 2019, ISSN: 2249-8958 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: Varsha Saxena, Sanjay Srivastava

Paper Title: Image Processing in Brain Tumor Detection

Abstract: Image transforming operations to be strictly isolated fewer than three significant categories, picture Compression, picture upgrade Furthermore Restoration, Also estimation extraction. It includes diminishing the

measure about memory required to store digital image. Picture defects which Might make brought about by

that digitization transform in the imaging set-up (for example, awful lighting) might be remedied utilizing

picture upgrade strategies. Once those pictures may be for great condition, those estimation extraction

operations a chance to be used to acquire suitable data starting with the picture. Percentage illustrations for

picture upgrade and estimation extraction are provided for beneath. Those samples demonstrated all work on

256 grey-scale pictures.

Keywords: Image compression, Image enhancement, Restoration, Digitization, Measurement Extraction

References: 1. Kumar, GJ & Kumar GV (2008), Biological Early Brain Cancer Detection Using Artificial Neural Networks. In Artificial Intelligence

and Pattern Recognition, 89-93. 2. El Emary, IM & Ramakrishnan, S (2008). On the application of various probabilistic neural networks in solving different pattern

classification problems. World Applied Sciences Journal 4(6), 772-780.

3. Kadam, DB (2012). Neural network based brain tumor detection using MR images. International Journal of Computer Science and Communication 2(2), 325-331.

4. Nazem-Zadeh MR, Jafari-Khouzani K, Davoodi-Bojd E, Jiang Q & Soltanian-Zadeh H (2011). Clustering method for estimating principal diffusion directions. NeuroImage, 57(3), 825-838.

5. Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N & Golland P (2010), September. A generative model for brain tumor segmentation in multi-modal images. In International Conference on Medical Image Computing and Computer-Assisted

Intervention (pp. 151-159). Springer, Berlin, Heidelberg. 6. Gooya A, Biros G and Davatzikos C (2011). Deformable registration of glioma images using EM algorithm and diffusion reaction

modeling. IEEE transactions on medical imaging, 30(2), 375-390.

7. Varun Gulshan, Carsten Rather, Antonio Criminisi,Andrew Blake,Anddrew Zisserman “Geodesic star convexity,” http://www. robots.ox.ac.uk/˜vgg/research/iseg/.

8. Fauzi M, Othman B and Abdullah NB (2011). MRI brain classification using support vector machine. In IEEE 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO).

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2.

Authors: Mani Sharma, Sunil.J.Wagh, Miss. Archana Sar

Paper Title: Identifying Cuts by Linear Search Method

Abstract: Tracking cuts and their position in wireless sensor network is the main issue of focus now-a -days. This research paper describes cut detection by linear search method in a dynamic table. We are working on the

values of node’s related factors. In this method, scanning will be performed on each sensor node on the basis of

certain factors and cut will be detected. We hope that our method will be efficiently takes less time in tracking

cut and will help in the process of connectivity restoration.

Keywords: Tracking cuts, wireless sensor network, linear search, scanning

References: 1. N. Shrivastava, S. Suri, and C. Toth, “Detecting cuts in sensor networks,” ACM Transactions on Sensor Networks, vol. 4, no. 2, pp. 1–

25, 2008.

2. P. Barooah, H. Chenji, R. Stoleru, and T. Kalmar-Nagy, “Cut detection in wireless sensor networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 99, no. PrePrints, 2011.

3. M. Won and R. Stoleru, “Destination-based cut detection in wireless sensor networks,” in Proceedings of IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC), 2011

4. Shuguang Xiong and Jianzhong, “An Efficient Algorithm for Cut Vertex Detection in Wireless Sensor Network”, International Conference on Distributed Computing system, 2010

5. Izzet F.Senturk, Kemal Akkaya and Sabri Yilmaz, “Distributed Relay Node Positioning for Connectivity Restoration in Partitioned Wireless Sensor Network” IEEE Explore Digital Library,2012

6. Ms. Rini Mathew and Mrs. Annadevi. E “Artificial Routing Protocol for Cut Detection of Cut Vertices” IOSR Journal of Computer Engineering (IOSR-JCE).vol. 9,

7. [7] D. B. West, “Introduction to Graph Theory (Second Edition)”, Prentice Hall, 2001. 8. [8] X. Liu, L. Xiao, A. Kreling and Y. Liu, “Optimizing Overlay Topology by Reducing Cut Vertices”, in ACM Workshop on

Network and Operating System Support for Digital Audio and Video (NOSSDAV), 2006. 9. [9] P. Barooah, “Distributed cut detection in sensor networks,” in Proceedings of IEEE Conference on Decision and Control and

European Control Conference (CDC), 2008.

10. 2012 11. [10] T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms (Second Edition)”, The MIT Press, 2002 12. [11] B. Milic and M. Malek, “Adaptation of the Breadth First Search Algorithm for Cut-edge Detection in Wireless Multihop

Networks”, in ACM International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM), 2007. 13. [12] M. Won, M. George, and R. Stoleru, “Towards robustness and energy efficiency of cut detection in wireless sensor networks,”

Elsevier Ad Hoc Networks, vol. 9, no. 3, pp. 249–264, 2011.

14. [13]Prof. Prashant P. Rewagad and Harish Prakash Patil, " DCD Algorithm in Wireless Sensor Networks with enhance security mechanism", IJLTET, vol-3, Issue-2, Nov-2013

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Authors: Indu Bhuria, Rajeev Pourush, D. R. Godara

Paper Title: Attenuation Due to Foliage Depth at 35 Ghz Prevailing in Desert Region of India

Abstract: Communication window, ka-band (28 to 42 GHz) is to be investigated for the various developing applications. In 5 G communications all the devices are being developed to use millimeter wave as medium of

propagation as rest of the spectrum is getting exhausted day by day. 35 GHz frequency can be used to

characterize the whole band. For LOS communication foliage is one of the main obstacle which can attenuate

the signal. In this paper an attempt is made to specify the rate of attenuation due to cumulative effect of

atmospheric gases and foliage in depth on the basis of observational studies. In autumn season, rate of

attenuation up-to five trunks is 0.154 dBm per feet and it decreases to .062 dBm per feet if signal prevails

through fourteen trunks. Similarly, if signal propagates through canopy area then rate of attenuation is 0.22

dBm per feet for first five tree canopies and it decreases to 0.095 dBm per feet as foliage depth increases to

fourteen canopies. For spring season, rate of attenuation for first five canopies is 0.179 dBm per feet and for

eleven canopies rate becomes 0.140 dBm per feet. This decrement in rate of attenuation is due to coherent

interplays of field component due to collective scatterers. Rate of decrement suggests that there is possibility of

35 GHz to be used for communication applications. Attenuation in autumn is observed to be lesser then in

other seasons as leaves density which can offer multiple scattering of field components is less.

Keywords: Attenuation; Foliage depth; LOS communication; Scattering; Millimeter wave

References: 1. Indu Bhuria, Rajeev Pourush, D.R Godara “Free space path loss statistics of 35 GHz wave prevailing in desert region of India” at

Mody University International Journal of Computing and Engineering Research ’ vol-2 , Issue-2, ISSN 2456-8333, Pp-17-19, March

2018 2. Schwering, Felix K, E.J.Violette, and R.H. Espeland (1988), ‘Millimeterwave propagation in vegetation: Experiments and

theory’,IEEE Trans. Geoscience Remote Sensing, Vol. 26, No. 3, 355-367

3. Indu Bhuria, Rajeev Pourush, D.R Godara “ Effect of leaf size on millimeter wave Propagation” at ‘National conference on Emerging trends in Engineering ’ organized by CET , Bikaner, 10 February 2017, ISBN No. 978-93-85135-31-6

4. Feinian Wang,Kamal Sarabandi, “An Enhanced Millimeter-Wave Foliage Propagation Model” IEEE Transactions on Antenna and Propagation,Vol. 53, NO. 7, pp.2138-2145, July 2005

5. H. Essen, D Nüßler, N. von Wahl, S. Heinen and S. Sieger “Foliage Penetration upto millimeter wave frequencies” IEEE Fourth European Conference on Antennas and Propagation, , Print ISSN: 2164-3342 ,12-16 April 2010

6. Seville, A. and K. H. Craig,” Semi-empirical model for millimeter-wave vegetation attenuation rates", Electron. Lett., Vol. 31, pp.- 1507 – 1508, Print ISSN: 0013-5194, 1995

7. Gary Comparetto “The Impact of Dust and Foliage on Signal Attenuation in the Millimeter Wave Regime” Journal of Space Comm., Vol. 11, pp.-13-20, July 1993.

8. R. P. Rafuse, "Effects of Sandstorms and Explosion-Generated Mie, G., "A Contribution to the Optics of Turbid Media, Especially Colloidal Metallic Suspensions," Number DCA-16, Massachusetts Institute of Technology Lincoln Laboratory, Ann. Phys., Vol. 25,

pp. 377-445, 10 November 1981. 9. Indu Bhuria, Rajeev Pourush, D.R Godara “Behaviour of millimeter wave with increasing canopy size of scared fig tree ” at

‘International conference on Optical and wireless Technologies’ organized by MNIT , Jaipur , 10-11 February 2018

10. D.R Godara, J.S Purohit , Sandeep Rankawat , S.K Modi(2013) ‘Effects of foliage length on signal Attenuation in Millimeter Band at 35 GHz.’, International Journal of Computer Applications USA( 0975-8887), Vol. 84 Issue No. 2, P.11-13. IJCA solicits original

Papers for June 2015 Edition.

11. P. L. McQuate, J. M. Harman, and A. T. Barsis, "Tabulation of Propagation Data Over Irregular Terrain in the 230 to 9200 MHz Frequency Range. Part 1: Gunbarrel Hill Receiver Site," Technical Report No. ERL 65-ITS 58-1, Institute for Telecommunications

Science, Boulder, Colorado,March 1968 12. Tewari, R.K. S. Swarup, and M. N. Roy, "Radio Wave Propagation through Rain Forests of India," IEEE Transactions on Antenna and

Propagation, Vol. 38, No. 4, pp. 433-449 ,April 1990

13. Tamir, T.,"Radio Wave Propagation Along Mixed Paths in Forest Environments," IEEE Transactions on Applied Physics, Vol. 25, pp. 471-477, July 1977

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Authors: Harsh Purohit, Ravisha Chutani

Paper Title: Financial Literacy and Planning: A Study of Indian Households of Punjab State

Abstract: This study surveys 800 people from Indian households in the state of Punjab to examine the effect

of demographic factors on the factors affecting financial literacy level. Seven factors were identified through

exploratory factor analyses which help in assessing the financial literacy level of the people. The results

indicated that there is a significant difference in the financial literacy level of the people based on educational

qualification, occupation and monthly income. And women possessed lower financial literacy than men on the

parameters of financial planning, financial advisory services, retirement planning, estate planning, insurance

planning, legal services and budgeting and documentation.

Keywords: Demographics, Financial Planning, Financial Literacy, Indian Households, Punjab

References: 1. S. Schagen and A. Lines, "Financial Literacy in Adult Life: A Report to the Natwest Group Charitable Trust", Slough, Berkshire:

National Foundation for Educational Research, 1996.

2. H. CHEN, "An analysis of personal financial literacy among college students", Financial Services Review, vol. 7, no. 2, pp. 107-128, 1998.

3. UK Adult Financial Literacy Advisory Group, Report to the Secretary of State for Education and Employment, December 2000. 4. Volpe, R. P., Kotel, J. E. & Chen, H., “A survey of investment literacy among online investors”, Financial Counseling and Planning,

Vol. 13 (1), pp. 1-13, 2002.

5. Roy Morgan Research, “ANZ Survey of Adult Financial Literacy in Australia: Stage 3: In-Depth Interview Survey Report”, Melbourne: ANZ Bank, 2003c.

6. O. Publishing, Improving Financial Literacy. Paris: Organization for Economic Co-operation and Development, 2005.

16-22

7. A. Lusardi and O. Mitchell, "Planning and Financial Literacy: How Do Women Fare?", American Economic Review, vol. 98, no. 2, pp. 413-417, 2008.

8. H. Hassan Al‐Tamimi and A. Anood Bin Kalli, "Financial literacy and investment decisions of UAE investors", The Journal of Risk Finance, vol. 10, no. 5, pp. 500-516, 2009.

9. A. LUSARDI, O. MITCHELL and V. CURTO, "Financial Literacy among the Young", Journal of Consumer Affairs, vol. 44, no. 2, pp. 358-380, 2010.

10. A. LUSARDI and O. MITCHELL, "Financial literacy around the world: an overview", Journal of Pension Economics and Finance, vol. 10, no. 04, pp. 497-508, 2011.

11. J. Gathergood and R. Disney, "Financial Literacy and Indebtedness: New Evidence for U.K. Consumers", SSRN Electronic Journal, 2011.

12. M. Sherraden, L. Johnson, B. Guo and W. Elliott, "Financial Capability in Children: Effects of Participation in a School-Based Financial Education and Savings Program", Journal of Family and Economic Issues, vol. 32, no. 3, pp. 385-399, 2011.

13. Leora. Klapper, Annamaria. Lusardi and A-Georgios. Panos, “Financial Literacy and the Financial Crisis: Evidence from Russia”, World Bank Policy Research Working Paper No. 5980,

5

Authors: Mayank Singh, Viranjay M. Srivastava

Paper Title: Implementing Architecture of Fog Computing for Healthcare Systems based on IoT

Abstract: Healthcare has been highly benefited with the technology advancement. Technology-based new solutions, methods, applications, and systems completely revolutionize the healthcare industry. Earlier the

decision making of doctor entirely depends on the experience, domain knowledge, laboratory reports,

diagnostics and patient’s symptoms. With the advancement of technology in healthcare, the decision-making

will be highly advanced and add wisdom to it. The patient’s care and regular monitoring have been vastly

improved at low cost with the appearance of the Internet of things based healthcare systems. There are a lot of

Internet of Things (IoT) based healthcare devices that produces enormous of data and transfer to the cloud for

analysing and sharing with other stakeholders. Cloud computing is the backbone for such devices to collect,

analyse and share the data or result with all the concern persons. Data are increasing day by day as the user

base of IoT based healthcare devices has also been increased in folds. Due to such high volume of data, the

latency rate, security issues and quality of analysed data has been decreased on the cloud. To overcome such

limitation of cloud, a new paradigm called Fog computing has appeared. Fog computing is the intermediate

layer between sensors and cloud servers. It facilitates the data gathering from various nearby sensors, analyses

the data and provides the result to the sensor, cloud servers and other concerns locally. Deploying such

computing infrastructure locally reduces the cost and increase the quality of analysis and alerting in real time.

It also adds the security to the data as it is processed locally. This paper proposed an architecture to implement

the fog computing between IoT sensors and cloud to handle the medical data.

Keywords: Fog computing, healthcare systems, IoT based healthcare applications

References: 1. European Commission Information Society, Internet of Things strategic research roadmap, 2009. http://www.internet-of-things-

research.eu/ [accessed 25-05-2018].

2. P. Venkatramanan, I. Rathina “Healthcare leveraging Internet of Things to revolutionize healthcare and wellness,” IT Services Business Solutions Consulting, Tata Consultancy Services Limited, 2014.

3. A. Dohr, R. Modre-Opsrian, M. Drobics, D. Hayn, G. Schreier, “The internet of things for ambient assisted living,” proceedings of the International Conference on Information Technology: New Generations, pp. 804–809, 2010.

4. D. Miorandi, S. Sicari, F. De Pellegrini, I. Chlamtac, “Internet of things: vision, applications and research challenges,” Ad Hoc Networks, vol. 10, no. 7, pp.1497–1516, 2012.

5. M. Carmen Domingo, “An overview of the internet of things for people with disabilities,” Journal of Network and Computer Applications, vol. 35, no. 2, pp. 584–596, 2012.

6. Hairong Yan, Li Da Xu, Zhuming Bi, Zhibo Pang, Jie Zhang, Yong Chen, “An emerging technology: a wearable wireless sensor networks with applications in human health condition monitoring”, Journal of Management Analytics, vol. 2, no. 2, pp. 121–137,

2015. 7. Y. J. Fan, Y. H. Yin, L. D. Xu, Y. Zeng, F. Wu, “Iot-based smart rehabilitation system,” IEEE Transactions on Industrial Informatics,

vol. 10, no. 2, pp. 1568–1577, 2014.

8. C.E. Koop, R. Mosher, L. Kun, J. Geiling, E. Grigg, S. Long, C. Macedonia, R. Merrell, R. Satava, J. Rosen, “Future delivery of health care: cybercare,” IEEE Engineering in Medicine and Biology Magazine, vol. 27, no. 6, pp. 29–38, 2008.

9. A. M. Rahmani, T. N. Gia, B. Negash, A. Anzanpour, I. Azimi, M. Jiang, P. Liljeberg, “Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach,” Future Generation Computer Systems, vol. 78, no. 2, pp. 641-658, 2018.

10. F. Bonomi, R. Milito, J. Zhu, S. Addepalli, “Fog computing and its role in the internet of things,” Proceedings of the First Edition of the MCCWorkshop on Mobile Cloud Computing, pp. 13–16, 2012.

11. M. Aazam, E. N. Huh, “Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT,” 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, pp. 687–694, 2015.

12. M. Aazam, E. N. Huh, “Fog computing and smart gateway based communication for cloud of things,” Future Internet of Things and Cloud (FiCloud), 2014 International Conference on, pp. 464–470, 2014.

13. A. M. Rahmani, N. K. Thanigaivelan, Tuan Nguyen Gia, J. Granados, B. Negash, P. Liljeberg, H. Tenhunen, “Smart e-health gateway: bringing intelligence to IoT-based ubiquitous healthcare systems,” Proceeding of 12th Annual IEEE Consumer Communications and

Networking Conference, pp. 826–834, 2015. 14. C. Doukas, T. Pliakas, I. Maglogiannis, “Mobile healthcare information management utilizing cloud computing and android OS,” 2010

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1037–1040, 2010.

15. G. Fortino, M. Pathan, G.D. Fatta, “Bodycloud: integration of cloud computing and body sensor networks,” 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 851–856, 2012.

16. A.J. Jara, M.A. Zamora, A.F. Skarmeta, “An internet of things—based personal device for diabetes therapy management in ambient assisted living (AAL),” Personal and Ubiquitous Computing, Springer, vol. 15, no. 4, pp. 431–440, 2011.

17. P. K. Gupta, B. T. Maharaj, Reza Malekian, “A novel and secure IoT based cloud centric architecture to perform predictive analysis of users activities in sustainable health centres,” Multimedia Tools and Applications, vol 76, no. 18, pp. 18489–18512, 2017.

18. C. Doukas, I. Maglogiannis, “Bringing IoT and Cloud Computing towards Pervasive Healthcare,” Proceedings of the Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 922–926, 2012.

19. M. Chen, Y. Qian, J. Chen, K. Hwang, S. Mao, L. Hu, “Privacy protection and intrusion avoidance for cloudlet-based medical data sharing,” IEEE Transactions on Cloud Computing, vol. 99, 2017.

20. Pelagia Tsiachri Renta, Stelios Sotiriadis, Euripides G.M. Petrakis, “Healthcare sensor data management on the cloud,” Proceedings

23-27

of the 2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing (ARMS-CC ’17), ACM, pp. 25–30, 2017.

21. T. Nguyen Gia, M. Jiang, V. K. Sarker, A. M. Rahmani, T. Westerlund, P. Liljeberg, H. Tenhunen, “Low-cost fog-assisted health-care IoT system with energy-efficient sensor nodes,” Proceedings of 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1765–1770, 2017.

22. F. Bonomi, R. Milito, J. Zhu, S. Addepalli, “Fog computing and its role in the internet of things,” Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, ACMpp. 13-16, 2012.

23. M. Singh, P. K. Gupta and V. M. Srivastava, "Key challenges in implementing cloud computing in Indian healthcare industry," 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), Bloemfontein, pp. 162-167,

2017.

6

Authors: Shailendra Mishra, Mayank Singh

Paper Title: An improved Energy Efficient Communication Protocol (IEECP) for Wireless Sensor Networks

Abstract: The wireless sensor networks have been experiencing exponential growth in the past decade. A Wireless Sensor Network provides low cost solutions and consists of several sensors distributed across a

geographical area. In many commercial and industrial applications, it often needs to monitor and collect the

information about the environment conditions (temperature, humidity, vibration, acceleration etc.) by using

sensor networks. This paper aims to resolve issues relating to excessive multi-hoping from one node to another.

Proposed addressing can be used for signal spreading and de-spreading and minimize power usage. The

proposed protocol is compared with the two existing protocols namely Tree Routing and Enhanced Tree

Routing. The simulation results show that the proposed protocol has the low hop-count compare to TR and

ETR, also it consumes less power and energy in finding paths and transmitting data to the sink node compare

to TR and ETR protocols.

Keywords: Wireless sensor network, Energy Efficient Hope Count Protocol, Tree Routing,

Extended Tree Routing, Non-Orthogonal Variable Spreading Factor Technique.

References: 1. M. Edal Anand; K. Senthil Kumar; and R. Amutha (2013). Energy efficiency of cooperative communication in wireless sensor

networks. International journal of data mining techniques and applications, (2), 300-314.

2. H. F. Chan; and H. Rudolph (2015). New energy efficient routing algorithm for Wireless Sensor Network. TENCON 2015 IEEE

Region 10 Conference, Macao, 2015, 1-5.

3. Huili Jia; Xunli Fan; Kun Liu; and Yang Qian (2017). An Energy Efficiency Routing Algorithm for Wireless Sensor Network. 2017

IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded

and Ubiquitous Computing (EUC), Guangzhou, 2017, 735-739.

4. H. KIM (2008). Multiple Sink Positioning and Routing to Maximize the Lifetime of Sensor Networks. IEICE Transactions on

Communications, volume 91(B), 3499-3506.

5. Yu, Jun; Zhang; and Xueying (2015). A Cross-Layer Wireless Sensor Network Energy-Efficient Communication Protocol for Real-

Time. Journal of Sensors, 2015(5), 1-13.

6. Vijayan, K; and Arun Raaza (2016). A Novel Cluster Arrangement Energy Efficient Routing Protocol for Wireless Sensor Networks.

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7. Shinji Inoue; Noriyuki Masuda; and Yoshiaki Kakuda (2007). An Autonomous Method for Aggregating and Repairing Paths in

Sensor Networks. Eighth International Symposium on Autonomous Decentralized Systems (ISADS'07), Sedona, Arizona, USA, 526-

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985–997.

11. Mario Macedo;Antonio Grilo; and Mario Nunes(2009). Distributed Latency-Energy Minimization and interference avoidance in

TDMA Wireless Sensor Networks. Elsevier Journal on Computer Networks, 53, 569–582.

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39. K. Akkaya; and M. Younis (2004). Energy-aware to mobile gateway in wireless sensor networks”, Proceedings of IEEE Globecom

2004 Workshops, USA, 16–21.

40. H. Luo;F. Ye; J. Cheng; S. Lu; and L. Zhang(2005).TTDD: two-tier data dissemination in large-scale wireless sensor networks. ACM

Wireless Networks, 11 (1–2), 161–175.

41. K Senthil Kumar; and R Amutha (2016). An Algorithm for Energy Efficient Cooperative Communication in Wireless Sensor

Networks., KSII Transactions on Internet and Information Systems, 10(7), 3080-3099.

42. Kiran Vadde; and Hasan Cam (2004). A Code Assignment Algorithm for Non-blocking OVSF Codes in WCDMA.

Telecommunication Systems, 25(3-4), 417–431.

43. Giuseppe Anastasi; Marco Conti; Mario Di Francesco; and Andrea Passarella (2009). Energy conservation in wireless sensor

networks: A survey. Ad Hoc Networks,7(3) ,537-568

7

Authors: Sandhya Tarar, Vyomika Singh, Vibhash Yadav, Shekhar Singh

Paper Title: Optimized Variational Bayesian Extreme Learning Machine Algorithm for Multimodal

Biometric Recognition

Abstract: In the thriving field of secure biometric systems, numerous advancements have been created and the need of the hour is Variational Bayesian Extreme Learning Machine (VBELM) which has an advantage in

terms of time efficiency, speed, security and accuracy over traditional Extreme Learning Machine method

(ELM). After observing the experimental results of Variational Bayesian Extreme Learning Machine

(VBELM) we observe that testing accuracy, over fitting problem and recognition models are the issues and in

order to address them, we curate an Optimized VBELM (OVBELM) which has opened doors for an

exceptional performance in terms of improved statistical testing accuracy, improved recognition rates,

execution time, reduced error rates and improved average fusion time.In this paper, optimized Variational

Bayesian Extreme Learning Machine (VBELM) is based on local feature fusion of three modalities- Face,

Fingerprint and Iris where appending iris as a third modality makes the system robust and secure. The

optimized biometric recognition system which is trained on an artificial neural network (ANN) exhibits

exceptional results after applying on 240 face images (40 people with 6 images for each individual) from

FERET Face database (Facial Recognition Database), 240 fingerprint images( 40 people with 6 images for

each individual) from FVC2002 fingerprint database and 240 iris images (40 people with 6 images for each

individual) from UBIRIS database and result analysis depicts that the optimized VBELM (OVBELM) is

having an edge over VBELM and traditional ELM duly reflected with improved execution time , testing

accuracy, average fusion time and reduced error rates.

Keywords: Artificial Neural Network (ANN),Extreme Learning Machine (ELM), Feature based fusion, Multimodal biometrics system, Optimizedm Varia-tional Bayesian Extreme Learning Machine

(OVBELM);Variational Bayesian Extreme Learning Machine (VBELM)

References: 1. AkankshaAggarwal, Manoj K. Verma, “Enhancing Performance of multimodal biometric system using gabor feature and similarity

index” June 2016, International Journal of Engineering Trends and Technology (IJETT). 2. Peter Waggett, IBM (2016), “Risk-based Authentication: Biometrics brave new world”. 3. Sandhaya Tarar, Ela Kumar, “Fingerprint Mosaicking Algorithm to Improve the Performance of Fingerprint Matching System”, 2014,

Computer Science and Information Technology . 4. RuchiKumari, Sandhya Tarar,” An Efficient High Dimensional Indexing Method For Content Based Image Retrieval (CBIR)”,

International Journal of Engineering and Techniques - Volume 2 Issue 3, May – June 2016

5. SubiyaZaidi, Sandhya Tarar, Shrish Kumar Singh, “To evaluate the performance of fingerprint enhancement techniques”,2015 Annual IEEE India Conference (INDICON).

6. Sandhya Tarar, ElaKumar,”Fingerprint Image Enhancement: Iterative Fast Fourier Transform Algorithm and Performance

36-43

Evaluation”,2013, International Journal of Hybrid Information Technology, Volume 6, No.4, July 2013. 7. Guang-Bin Huang, Qin-Yu Zhu, Chee-KheongSiew,,”Extreme Learning Machine: Theory and applications”, Volume 70, Issues 1-3,

December 2006, Pages 489-501.

8. Hai-Jun Rong, Yew-Soon Ong, ,”A fast pruned extreme learning machine for classification problem”, December 2008, Volume-72, Issues 1-3, Pages 359-366, Neurocomputing, Elsevier.

9. Jun-Ying Gan, Jun-Feng Liu, “Fusion and recognition of face and iris feature based on wavelet feature and KFDA”, 2009 International Conference on Wavelet Analysis and Pattern Recognition, IEEE.

10. Mohammad Hanif, Usman Ali,” Optimized Visual and Thermal Image Fusion for Efficient Face Recognition”, 2007, 9th International Conference on Information Fusion, IEEE.

11. Yibing Wang, BangjunHu , “A More Efficient Face Recognition Framework Based on Illumination Compensation, Kernel PCA and SVM”, 2014 Seventh International Symposium on Computational Intelligence and Design, IEEE.

12. Mina Farmanbar, OnsenToygar ,” A Hybrid Approach for Person Identification Using Palmprint and Face Biometrics”, June 2015, International Journal of Pattern Recognition and Artificial Intelligence 29(6):1556009.

13. Maryam Eskandari, OnsenToygar ,” A new approach for face-iris multimodal biometric recognition using score fusion.”, May 2013, International Journal of Pattern Recognition and Artificial Intelligence, Volume 27, Issue 03.

14. OmidSharifi, MaryanEksandari ,” Optimal Face-Iris Multimodal Fusion Scheme”, June 2016, Symmetry 8(6):48. 15. Chhaya Verma, Dr. Sandhya Tarar, “Watermark extraction and validation in images using hybrid techniques”(2016), International

journal of computer science trends and technology(IJCST), Volume 4, Issue 1.

16. Maria V. Ruiz, Zhanpeng Jin, Sarah Laszlo,” CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification.”, July 2016,IEEE Transactions on Information Forensics and Security ( Volume: 11, Issue: 7, )

17. C.M Sheela Rani, V. Vijaya Kumar, et al , “An Efficient Block based Feature Level Image Fusion Technique using Wavelet Transform and Neural Network.”, August 2012, International Journal of Computer Applications (0975 – 8887) Volume 52– No.12.

18. Secure ID News, Online source: https://www.secureidnews.com/news-item/iris-vs-retina-biometrics-yes-they-really-are-different/ 19. M2SYS Blog on biometric technology, Online source : http://www.m2sys.com/blog/biometric-hardware/reliable-biometric-modality/ 20. Feretdatabase : http://www.itl.nist.gov/iad/humanid/feret/feret_master.html 21. “Euclidean & Geodesic Distance between a Facial Feature Points in Two-Dimensional Face Recognition System”, Dec 2016,

RachidAhdid, KhaddoujTaifi.

22. Shouyi Yin *, Xu Dai, PengOuyang, Leibo Liu and Shaojun Wei, “A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps”, Sensors , 2014, 14(10), 19561-19581.

23. Online Source: http://bias.csr.unibo.it/fvc2002/ 24. Online Source :https://utiris.wordpress.com/2014/03/04 /university- of- tehran-iris-image-repository/

8

Authors: Dmitry Topchiy, Andrey Tokarskiy

Paper Title: Formation of Hierarchies in the Organization System of the State Construction Supervision

during Reshaping of City Territories

Abstract: The authors in this article review the basic principles of the Formation of Hierarchies in the City of Territories. Also shown are the features of construction control (supervision) as a state regulatory apparatus in

the context of the current legislation of the Russian Federation and the world construction complex as a whole.

The development of an integrated control model for improving the qualimeter parameters of finished

construction products is described.

Keywords: Territories, Principles of the formation of hierarchy

References: 1. Energy audit of buildings commissioned after reprofiling of industrial facilities. Topchy D.V. Scientific Review. 2017. No. 9. P. 114-

117.

2. Adaptation of industrial buildings to social facilities. Topchy D.V. Housing construction. 2007. No. 7. P. 16-19. 3. Local expansion of the span of industrial buildings. Topchy D.V. Vestnik MGSU. 2007. No. 4. S. 95-99. 4. Changing the grid of columns of reconstructed single-storey multi-span buildings when adapting them for civilian objects. Topchy

D.V. Vestnik MGSU. 2010. No. 4-1. Pp. 294-303. 5. Preparation of former industrial sites for the construction of civil facilities. Topchy D.V. Architecture and construction of Russia.

2011. No. 5. P. 14-21.

6. Comprehensive construction supervision: requirements and necessity. Topchy D.V. Technology and organization of construction production. 2014. No. 1. P. 46-47.

7. Assessment of the potential for reprofiling industrial facilities. Topchy D.V. Technology and organization of construction production. 2014. № 3 (8). Pp. 40-42.

8. Evaluation of organizational, technological and economic parameters in the withdrawal of enterprises outside the city limits. Topchy D.V. Technology and organization of construction production. 2014. No. 4. P. 34-41.

9. Evaluation of organizational, technological and economic parameters in the withdrawal of enterprises outside the city limits. Topchy D.V. Technology and organization of construction production. 2015. No. 4-1 (9). Pp. 34-41.

10. Assessment of the correlation dependence of the material intensity of building structures of various types of industrial buildings that are subject to dismantling during the conversion of industrial areas. Topchy D.V. European Research. 2015. № 6 (7). Pp. 6-9.

11. Assessment of the structure of industrial enterprises to be redeveloped and located within the boundaries of large megacities. Topchy D.V. In the collection: innovative technologies in construction and geo-ecology. Materials of the II International Scientific and

Practical Conference. St. Petersburg State University of Communications named after Emperor Alexander I, Department of Engineering Chemistry and Natural Science. 2015. pp. 37-41.

12. Development of an organizational and managerial model for the implementation of projects for reprofiling industrial sites. Topchy D.V. In the collection: innovative technologies in construction and geo-ecology. Materials of the II International Scientific and Practical Conference. St. Petersburg State University of Communications named after Emperor Alexander I, Department of

Engineering Chemistry and Natural Science. 2015. pp. 42-60.

13. Project risk reduction reduction tool. Topchy D.V., Skakalov V.A., Yurgaitis A.Yu. International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 1, January 2018, pp. 985–993

14. A. Lapidus., I Abramov, For example of a construction company / A. Lapidus, I. Abramov // E3S Web of Conferences. - 2018. - No. 33.

15. A. Lapidus, A. Makarov, Formation of structural units within a construction company using A. system, A. Lapidus, A. Makarov // MATEC Web Conf. - 2016. - No. 86.

16. P. Oleinik, Method for a company / Oleinik P. // International Journal of Construction Management. - 2017. - No. 7.

44-46

9 Authors: Ritu Sachdeva (Sharma), Sachin Gupta

Paper Title: A Novel Algorithm for Enhancing Search Engine Optimization

https://www.secureidnews.com/news-item/iris-vs-retina-biometrics-yes-they-really-are-different/http://www.m2sys.com/blog/biometric-hardware/reliable-biometric-modality/http://www.itl.nist.gov/iad/humanid/feret/feret_master.htmlhttp://bias.csr.unibo.it/fvc2002/

Abstract: Google search engine uses Page Rank algorithm to rank websites in their search results. But, Page

Rank calculates only the importance of each page rather than relevancy. Whenever a user triggers a query to

be searched, searching algorithm finds pages associated with the query term i.e. on basis of relevancy. Then,

Page Rank selects the most imperative result. Different Search Engine use different searching algorithm plays

an indispensable role in searching relevant results. But efficiency of searching algorithm is calculated by

drafting algorithms for specific categories of data like strings. In numerous key approaches, the trie is a

recognized fast access method. The proposed algorithm is a search algorithm for path-compressed trie for

keyword searching in a database through a search engine. Faster searching optimizes the search engine and

speeds up the complete process of creating final results. Thus, greater SEO (search engine optimization), faster

will be Page Rank Algorithm

Keywords: binary trie, LC-trie, Page Rank, PAT, PATRICIA, Search Engine Optimization, Trie

References: 1. Stefan Nilsson, Matti Tikkanen, ”Implementing a dynamic compressed Trie”Proceeding WAE’98, Saarbricken, Germany, (1998),

Ed. Kurt , pp. 1-3

2. Heinz, S. Zobel, J. & Williams, H.E., “Burst tries: A fast, efficient data structure for string keys”, ACM Transactions on Information Systems 20(2), 192-223

3. Aanchal Kakkar et al, “Search Engine Optimization: A Game of Page Ranking, (2015) 978-9-3805-4416- 8/15 IEEE 4. John B. Killoran, “how to use search engine optimization techniques to increase website visibility”, (2013), IEEE transaction on

professional communication vol 56 no. 1

5. Santosh Kumar Ganta, “Search Engine Optimization through Web Page Rank Algorithm”,(2011), ISSN: 0976- 8491, IJCST Vol. 2, Issue 3

6. Renalyn C. Antonio et al, “i-search: Document searching using Page Rank Algorithm”(2015), ISSN: 2278- 5299, Int. Journal of Latest Research in science and Technology, Volume 4, Issue 1: Page no. 70-74

7. N. Kaur & J. Kaur, “Development of Ranking algorithm for Search Engine Optimization”,(2014), International Journal of Engineering Research & Technology, India, ISSN: 2278-0181, Vol-3

8. K. Shum, “Notes on Page Rank Algoritnm”, (2013), ENGG2012B Advanced Engineering Mathematics 9. D.R. Morrison,” PATRICIA- practical algorithm to retrieve information coded in alphanumeric (1968), Journal of the ACM, 15,

514-534

10. Masami Shishibori et al, “A key search algorithm using the com pact Patricia Trie”, (1997), IEEE International Conference on Intel ligent Processing systems, Beijing, China

11. Niloufar shafiei, “Non-blocking Patricia Tries with Replace Operations”, (2013), IEEE, 33rd International conference on Distributed Computing Systems

12. M. Shishiburi et al., “An Efficient Method of Compressing Binary Tries”,(1996), IEEE, 7803-3280-6 13. Ayush Jain, “The Role and Importance of Search Engine and Search Engine Optimization”,(2013), IJETICS Vol. 2, Issue 3, ISSN-

2278-6856 14. Shipra Kataria & Pooja Sapra, “A Novel Approach for Rank Optimization using Search Engine Transaction Logs”,(2016), 978-9-

3805-4421-2/16, IEEE

15. Search Engine Optimization – tutorialspoint, simply easy learning, http://www.tutorialspoint.com/seo /seo_tutorial.pdg 16. Neil V. Murray et al, “Reduced implicate tries with updates”,(2008) Oxford University Press, Vol. 20, The author 17. P. Flat, “On the performance evaluation of extendible hashing and trie searching” (1983), Acta Informatica, 20: 345-369 18. E. Fredkin, “This memory” (1960), Communications of the ACM, 3.490-500, 19. Arne Andersson et al, “Efficient implementation of suffix trees”(1995), Software –Practice and Experience, VOL. 25(2), 129- 141,

CCC 0038- 0644/95/020129-13

20. Neha Mangla et al, “Context based Indexing in Information Retrieval System using BST”(2014), International Journal of scientific and research Publications, Volume 4, Issue 6, ISSN 2250-3153.

21. V. R. Kangavalli, G. Maheeja, “A study on the usage of data structures in Information retrieval” (2016), National Conference on Innovations in Communication and Computing Technologies

22. Monther Aldwairi et al. , “IP Lookup using two- level indexing and B-trees”,(2010), International Conference on Internet Computing, ICOMP ,Las Vegas Nevada, USA

23. Roberto Grossi et al, “Fast compressed tries through path decompositions” (2014), ACM journal of experimental algorithms, Vol. 19, No. 1, Article 1.8,

24. Erik Demaine, “Advanced data structures”(2012), Lecture L16 , Spring 2012 25. Isara Nakavisute, “Optimizing information retrieval (IR) time with doubly linked list and binary search tree (BST)” (2015),

international journal of advanced computational engineering and networks, ISSN 2320- 2106, Volume-3, Issue-12

26. Rene De La Briandais, “File searching using variable length keys”, (1959), western joint computer conference, p.295-298, San Francisco, California

27. Andersson, A, Nilsson, S. “ Improved Behaviour of Tries by Adaptive Branching. IPL, 46(6):295-300 28. Hatab, Rayhan,” Improve Website Rank Using Search Engine Optimization (SEO)” (2014), Thesis Submitted to Faculty of

Computer & Information Al-Madinah International University

29. Atish Das Sarma , Anisur Rahaman Molla , Gopal Pandurangan Eli Upfal ,” Fast Distributed PageRank Computation”, ∗Appeared in Theoretical Computer Science (TCS) (2015), volume 561, pages 113- 121

30. H. Ishii, R. Tempo and E. Bai, "A Web Aggregation Approach for Distributed Randomized PageRank Algorithms” (2012), IEEE Transactions on Automatic Control, vol. 57, no. 11, pp. 2703-2717, doi: 10.1109/TAC.2012.2190161.

31. M. Thenmozhi, H. Srimathi, “An Analysis on the Performance of Tree and Trie based Dictionary Implementations with Different Data Usage Models” (2015), Indian Journal of Science and Technology, Vol 8(4), 364–375, ISSN (Online) : 0974-5645 , DOI:

10.17485 /ijst/2015/v8i4/59865 32. Sangita Karmakar and Soumen Swarnakar, “New Concept based Indexing Technique for Search Engine” (2018), Indian Journal of

Science and Technology, Vol 10(18), DOI: 0.17485/ijst/2017/ v10i18/ 114018, 0974-6846 ISSN (Online) : 0974-5645

33. https://www.searchmetrics.com/knowledge-base/ranking-factors/ 34. S. Hussien, “Factors Affect Search Engine Optimization” (2014), IJCSNS International Journal of Computer Science and Network

Security, VOL.14 , No.9

47-53

10

Authors: Y. K. Salal, Prof. S. M. Abdullaev, Mukesh Kumar

Paper Title: Educational Data Mining: Student Performance Prediction in Academic

Abstract: At present data mining techniques become very popular among the data analyst. It became an effective tool for finding the uncovered information from a big database. Due to this feature data mining are

adopted by many areas like education, telecommunication, retail management etc to resolve their business

54-59

http://www.tutorialspoint.com/seo%20/seo_tutorial.pdghttps://www.searchmetrics.com/knowledge-base/ranking-factors/

problems. In this paper, for building classification models for ‘student performance’ dataset consisting of 649

different instances with 33 different attributes implement algorithms like NaiveBayes, Decision Tree (J48),

RandomForest, RandomTree, REPTree, JRip, OneR, SimpleLogistic and ZeroR. After implementing these

algorithms on student performance dataset, we evaluate and compare the implementation result for better

accuracy of prediction. The result of this study is extremely significant and hence provides a greater insight for

evaluating the student performance and underlines the significance of data mining in education. It also shows

that how students attributes affect the student performance.

Keywords: Naive Bayes, k-nearest neighbor, Logistic regression, J4.8, RandomForest.

References: 1. P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds.,

Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS,

ISBN 978-9077381-39-7. [Web Link6 ]

2. P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS,

ISBN 978-9077381-39-7. [Web Link6 ]

3. S. Harvey. Mining Information from US Census Bureau Data. 4. Mukesh Kumar, A.J. Singh, Disha Handa,"Literature Survey on Student’s Performance Prediction in Education using Data Mining

Techniques", International Journal of Education and Management Engineering(IJEME), Vol.7, No.6, pp.40-49, 2017.DOI:

10.5815/ijeme.2017.06.05 5. Turban E.; Sharda R.; Aronson J.; and King D., 2007. Business Intelligence, A Managerial Approach. Prentice-Hall. 6. Witten I. and Frank E., 2005. Data Mining: Practi- cal Machine Learning Tools and Techniques with Java Implementations. Morgan

Kaufmann, San Francisco, CA. 7. Luan J., 2002. Data Mining and Its Applications in Higher Education. New Directions for Institutional Research, 113, 17–36. 8. Mukesh Kumar, A.J. Singh, "Evaluation of Data Mining Techniques for Predicting Student’s Performance", International Journal of

Modern Education and Computer Science(IJMECS), Vol.9, No.8, pp.25-31, 2017.DOI: 10.5815/ijmecs.2017.08.04 9. Ma Y.; Liu B.; Wong C.; Yu P.; and Lee S., 2000. Targeting the right students using data mining. In Proc. of 6th ACM SIGKDD

International Conference on Knowledge Discovery and Data Mining. Boston, USA, 457–464.

10. M. Kumar, S. Shambhu, P. Aggarwal, "Recognition of Slow Learners Using Classification Data Mining Techniques", Imperial Journal of Interdisciplinary Research, vol. 2, no. 12, 2016.

11. Mashael A. Al-Barrak And Mona S. Al-Razgan, predicting students‟ performance through classification: a case study, Journal of Theoretical and Applied Information Technology 20th May 2015. Vol.75. No.2

12. Edin Osmanbegović and Mirza Suljic, DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE, Economic Review – Journal of Economics and Business, Vol. X, Issue 1, May 2012.

13. Raheela Asif, Agathe Merceron, Mahmood K. Pathan, Predicting Student Academic Performance at Degree Level: A Case Study, I.J. Intelligent Systems and Applications, 2015, 01, 49-61 Published Online December 2014 in MECS (http://www.mecs-press.org/) DOI:

10.5815/ijisa.2015.01.05

14. Mohammed M. Abu Tair, Alaa M. El-Halees, Mining Educational Data to Improve Students‟ Performance: A Case Study, International Journal of Information and Communication Technology Research, ISSN 2223-4985, Volume 2 No. 2, February 2012.

15. Dr Pranav Patil, a study of student’s academic performance using data mining techniques, international journal of research in computer applications and robotics, ISSN 2320-7345, vol.3 issue 9, pg.: 59-63 September 2015.

16. Jyoti Bansode, Mining Educational Data to Predict Student‘s Academic Performance, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169, Volume: 4 Issue: 1, 2016.

17. R. Sumitha and E.S. Vinoth kumar, Prediction of Students Outcome Using Data Mining Techniques, International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume-2, Issue-6,June 2016 ISSN: 2395-3470.

18. Karishma B. Bhegade and Swati V. Shinde, Student Performance Prediction System with Educational Data Mining, International Journal of Computer Applications (0975 – 8887) Volume 146 – No.5, July 2016.

19. Mrinal Pandey and S. Taruna, Towards the integration of multiple classifiers pertaining to the Student's performance prediction, http://dx.doi.org/10.1016/j.pisc.2016.04.076 2213-0209/© 2016 Published by Elsevier GmbH. This is an open access article under the

CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

20. Niranjan Lal, Shamimul Qamar , Monika Kalra, “K- Mean Clustering Algorithm Approach for Data Mining of Heterogeneous Data” Information and Communication Technology for Sustainable Development{ICT4SD), LNNS, Springer Proceeding , Volume 10,

pp.61-70 2017.

11

Authors: M Anto Juliet Mary, Vani Ramesh, Vishal C Jaunky

Paper Title: Work Life Integration of Migrant Faculty at Higher Educational Institutions, Bangalore

Abstract: Migration is an intended drive between and within the national fringes by any professional. Recently, initial and growing disparity in development between and among states has triggered such

movement. The dimensions notable are demographic, socio cultural, economic, political, infrastructural,

technological and environmental. Referring to academic migrants, interstate migrants are those who have

moved to other states for employment opportunities. Intra state academicians are moved within the state for

employment opportunities. Research in higher education for the migrant academicians is very stimulating as

well as a demanding. One of the Challenges faced by the migrant academicians is balancing the academic

career with family responsibilities. The use of the term integration refers to the areas of work and life domains

that are interconnected, overlapping, or interspersed throughout the day, which may be a prominent source of

positive spillover or negative conflict for people in the workplace. Hence this research attempts to assess the

work life integration with respect to role of work family conflict (WFC) and family work conflict (FWC)

experienced by the academicians migrated to Bangalore, working in private institutions by using PESTLE

determinants (Political, Economic, Social, Technical, Legal and Environmental). The findings will increase

our understanding PESTLE impact on the migrant faculty’s work life integration and purely for contributing

for the existing literature.

Keywords: Migration, Migrant academicians, work life integration, work life conflict, family work conflict

References:

60-66

1. Adepoju, A. (1998, Sep), Linkages between and internal and international migration : The African Situation. International Social Science Journal, 50(157), 387.

2. Bailyen, L., and Joyce, F. K. (2002), work redesign theory and practise. MIT sloan university. 43-12(1) 3. Byrne, U. (2017), Work-life balance. Business Information Review, 22(1), 53-59. 4. Collina, M., and Rawlings, F. (2014, july), Academic Migration, Discipline Knowledge and Pedagogical Practice. voices from the

Asia pacific. Retrieved from

https://www.nafsa.org/Resource_Library_Assets/Networks/RS/Book_Reviews/Academic_Migration,_Realities,_and_Challenges/Department of Economic Affairs . (2017). Economic Survey of India. Delhi: Goverment of India. Retrieved from

https://www.indiabudget.gov.in/es2016-17/echapter.pdf

5. Greenhaus, J. H., and Beutell, N. J. (1985, january), Sources of conflict between work and family roles. Academy of the management review, 10(1).

6. Halpern, D. F., and Murphy, S. E. (Eds.)(2015), From work–family balance to work– family interaction: Changing the metaphor. Mahwah, NJ: Erlbaum. 1355(24)

7. India, G. o. (2001), census of India 2001. governement of India ,Ministry of home affairs. 8. Kreiner, G. E., Hollensbe, E. C., and Sheep, M. L. (2016), Balancing borders and bridges: Negotiating the work-home interface via

boundary work tactics. Academy of Management Journal, 52, 704-730. 9. Kanter, R. M. (1989), Work and family in the united states :A critical review and agenda for reseach and policy (Vol. 2). New York:

Russel Sage Foundaton(1).

10. Mukerji, S. (1994), Interstate migration and regional disparities in India. Bombay: Himalaya Publishing. 11. Price Water Coopers. (2007), (Citizens perception on Democratic Capital, 2007). Bangalore: PWC. Retrieved from

https://www.pwc.in/assets/pdfs/citizens-perception-on-democratic-capital.pdf

12. Ramesh, R., & Shyam, k. K. (2014), Role of Employee Empowerment in Organizational. International Journal of scientific research and management (IJSRM), 2(8), 1241-1245.

13. Reddy, K. (2010, jul), Work–Life Balance among Married Women Employees. Indian Journal of Pshycological constrol, 32(2). 14. Ragu-Nathan, T. S., and Ragu-Nathan, B. S. (2017), Crossing to the dark side: Examining creators, outcomes, and inhibitors of

technostress. Communications of the ACM, 67(9), 143-170.

15. Srivastava, R., and Sasikumar, S. K. (2003), An overview of migration in India and its key impacts and issues. 16. Regional Conference on Migration, Development and Pro-Poor. Dhaka. 17. Tabachnick, b. s., and fidell, l. s. (1996), Using multivariate statistics, 43-56-89(3). 18. Tembhekar, C. (2013, February 3), The times of India. Retrieved from The times of India.indiatimes.com:

https://timesofindia.indiatimes.com/city/bengaluru/Bangalore-gets-lions-share-of-educated-migrants/articleshow/18344203.cms

19. Niranjan Lal, Shamimul Qamar, Savita Shivani, “Search Ranking for Heterogeneous Data over Dataspace”, Indian Journal of Science and Technology (Scopus Indexed). Volume 9, Issue 36, pp.1-9, 2016

20. Work–Life Balance among Married Women Employees. (2010, Jul-Dec), Indian Journal of Psychological Medicine, 2(32). doi:10.4103/0253-7176.78508

12

Authors: Neha Miglani, Gaurav Sharma

Paper Title: Modified Particle Swarm Optimization based upon Task categorization in Cloud Environment

Abstract: Cloud Computing has become a spearhead in the field of industries and academia. As far as the IT Industry is concerned, it is pioneering the peculiar domains of clustering, virtualization and grid computing.

Traditionally, the complex computation nowadays, demands abundance of resources and computing facilities

to perform operational tasks. Cloud computing provides user a new wave in procuring available resources. To

scale up the capacity, task scheduling has been emerged as one of the key features of Cloud Computing.

Though it is considered as NP-Hard problem, yet numerous researchers and authors have tried to reap out the

effective and implementable results for scheduling of tasks to different virtual machines. Meta-heuristic

techniques have been embedded to obtain nearly optimal results in the previous studies, still loopholes are

lying in the consideration of multiple QoS parameters. In this paper, PSO approach has been modified by

manipulating parameters based on the QoS factors from the very initial stage. Instead of considering the

population randomly, MIPS and Bandwidth factors have been inculcated to refine and adjust the parametric

structure as well as for balancing the load more efficiently. The experimental setup shows that the proposed

algorithm works fairly well in assigning the upcoming tasks, henceforth, resulting in reduction of execution

time as well.

Keywords: Cloud Computing, Load Balancing, Particle Swarm Optimization, Quality of Service, Task Scheduling

References: 1. Karger D, Stein C, Wein J. Scheduling Algorithms. “Algorithms and Theory of Computation Handbook: special topics and

techniques”. Chapman & Hall/CRC; 2010.

2. M.A. Arfeen, K. Pawlikowski, A. Willig. “A Framework for Resource Allocation Strategies in Cloud Computing Environment”. Computer Software and Applications Conference Workshops (COMPSACW), IEEE 35th Annual, 2011, pp. 261 -266.

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2009), Leipzig, Germany. IEEE Press: New York, U.S.A., 21–24 June 2009; 1–11.

13

Authors: Preeti Sirohi, Amit Agarwal, Piyush Maheshwari

Paper Title: A framework for ranking of cloud services using non dominated sorting

Abstract: Cloud computing technology offers variety of cloud services to the users. The cloud user faces the

challenge in choosing the service which can meet his requirements. Therefore, selection of an approach which

can compare and select the best service according to the requirement is an issue. Several approaches,

algorithms and frameworks have been proposed and designed which provide solutions to its user in choosing

the best services. This paper proposed a framework which will use both subjective and objective parameter and

is based on non-dominated sorting approach for ranking of cloud services. Various genetic algorithm are

studied, analyzed and compared with each other find out their limitations.

Keywords: Cloud computing, Genetic Algorithm, Multi-Objective Optimization

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14

Authors: Samir Yerpude, Tarun Kumar Singhal

Paper Title: New Product Development – A Transformational Perspective with Internet of Things

Abstract: There is a fierce competition prevailing in the market due to different aspects that influenced such as globalization. Customers are exposed to the variety of goods encapsulated with complete information.

Organizations are constantly on the lookout for means for survival. New product development is one such area

where the organizations are concentrating rigorously. New product development is amongst the eight

constructs of supply chain and a very important one as the future of the organization depends on the new

product launched by the organizations. For a successful product launch, it is vital to be proactive in capturing

the customer requirements. Researchers vide this study and in consultation with industry experts in supply

chain and Information-technology area recommend the implementation of Internet of Things in this domain.

The primary considerations are due to the speed of the Internet and the ubiquitous presence of Internet and

Internet of Things. The business value derived with this implementation is discussed along with the

recommended architecture for the application.

Keywords: Business Value Realization; EDSOA; Internet of Things; IoT Architecture; New Product

Development.

References: 1. D. Lambert and M. Cooper, "Issues in Supply Chain Management", Industrial Marketing Management, vol. 29, no. 1, pp. 65-83, 2000. 2. S. Wheelwright and K. Clark, Revolutionizing product development. New York, N.Y.: Free Press/Simons & Schuster, 2011. 3. S. Brown and K. Eisenhardt, "PRODUCT DEVELOPMENT: PAST RESEARCH, PRESENT FINDINGS, AND FUTURE

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Journal of Engineering & Technology, vol. 7, no. 27, p. 444, 2018

6. H. Kopetz, Real-time systems. New York, NY: Springer, 2011 7. S. Yerpude and T.K. Singhal, "Internet of Things and its impact on Business Analytics", Indian Journal of Science and Technology,

vol. 10, no. 5, pp. 1-6, 2017

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15. S. Yerpude and T.K. Singhal, Customer Service Excellence through Internet of Things, 1st ed. Germany: Lambert Academic Publishing, 2017

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17. S. Yerpude and T.K. Singhal, "Impact of Internet of Things (IoT) Data on Demand Forecasting", Indian Journal of Science and Technology, vol. 10, no. 15, pp. 1-5, 2017

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15

Authors: Vetriselvi T, Gopalan N P

Paper Title: A Novel Approach to Summarization based on Centroid Fuzzy

Abstract: Text summarization is a way to create a description of a given document. This is a novel approach

to text summarization, which is a combination of fuzzy and centroid methods. In fuzzy method the result is

based on the input given to the membership function. In centroid, missing some relevant words may make the

summary irrelevant, and then the summary content is not meaningful. Our model overcomes the above two

problems by combining the results of those approaches. The rate of summarization determines the size of the

summary. Centroid is a group of words which are highly relevant to the document. Fuzzy membership

functions helps to categorize the most relevant sentences. Both approaches have their own disadvantages, so

we pick the best of the above two and create a novel approach as fuzzy centroids text summarization: this

approach performs well in multi document summary when compare with existing

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