S.L. Volume-8 Issue-1C, May 2019, ISSN: 2277-3878 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page

No.

1.

Authors: Kumar.R.Rao, Sreekeshava K.S, Manish S Dharek, Prashant Sunagar

Paper Title: Raster Least Cost Approach for Automated Corridor Alignment in Undulated Terrain

Abstract: A raster based route alignment using multi criteria factors created as raster layers is useful for

evolving total cost raster layer utilized at different stages for multi-criteria decision analysis (MCDA).

The integration of least cost path algorithm into the 3-D view of the terrain or false color composite

Remote sensing data can give value addition in planning process and formulation of action plans. The

designing of optimum route alignment depends upon the number of factor considered in the process of

corridor analysis model for finding the optimum route for alignment to ensure designing of the road

from source to a destination within two specified end point locations. The raster data layer on land use

and land cover type can be prepared for generating relative cost layer using the image classification

algorithms and the slope raster layer can be obtained from the digital Elevation Model. The optimum

route has the least cost and it can incorporate parameters on environmental, technical, social, and

economic issues. The raster corridor analysis has been implemented to design optimum route alignment

between two locations in the Himalayan region of India to consider the suitability of the technique for hill

road construction. The details of the same are given in the paper.

Index Terms: Cost raster layer, Route alignment planning, Geographic information system (GIS),

weighted overlay, Multi-criteria analysis, slope layer, least cost path.

References 1. Anderson, J. R., Harday, E. E., Roach, J. T., & Witmer,R. E. (1976).A Land Use and Land Cover Classification System.

Washington D.C: United States Government Printing Office.

2. Bagli, S., Geneletti, D., & F, O. (2011).Routing ofpower lines through least-cost path analysis and multi-criteria evaluation to minimize environmental impacts.Env.Impact Assessment review.

3. Blaschke T (2013) Land suitability analysis for Tabriz County, Iran: a multi-criteria evaluation approach using GIS. J Environ Planning Mgmt 56(1):1–23.

4. Carver SJ (1991) Integrating multi-criteria evaluation with geographical information systems. Int J Geogr Inf Syst 5(3):321–339.

5. Collischonn W, Pilar JV (2000) A directional dependent least-cost path algorithm for roads and canals. Int J Geogr Inform Sci 14(4):397–406.

6. Eastman IR et al (1995) Raster procedures for multi-criteria/multi-objectives decisions. Photogram Eng Remote Sens 61(5):539–547.

7. Garber, N.J. and Hoel, L.A. (1999).Traffic and Highway Engineering, 2nd edition. Pacific Grove, CA: Brooks/Cole Publishing Company.

8. Ibrahim, A. A. (2011). Assessment of the urban canopy heat island (UCHI) of Kano state during dry season. International conference on climate change impacts risk and opportunities, 1359-1367.

9. Jankowski P (1995) Integrating geographical information systems and multiple criteria decision-making methods. Int J Geogr Inf Syst 9(3):251–273.

10. Khanna, S. K., & Justo, C. E. (2011).Highway Engineering (Ninth edition ed.). (M. I. Justo, Ed.) Delhi:Nem Chand and Bros. 11. Manoj, K. J., & Paul, S. (2004). A highway alignment optimization model using geographic information systems.

Transportation Research Part A: Policy and Practice , 455-481.

12. Malczewski J (2006) GIS-based multi-criteria decision analysis: a survey of the literature. Int J Geogr Inf Sci 20(7):703–726. 13. Shaw, S.L. and Xin, X. (2003). Integrated land use and transportation interaction: temporal GIS exploratory data approach.

Journal of Transport Geography 11 (2),103-115.

1-4

2.

Authors: ManjunathItagi, B.P. Annapurna

Paper Title: Experimental Studies on Flexural Behavior of wastePlastic Fiber Reinforced Concrete Slab

Abstract: Plastic disposal is challenging issue across the globe. The use of plastic in concrete will

overcome the disposal problem. In this paper, we study the possibility of disposing waste in concrete. The

effect of fiber is been studied for varying percentages of fiber 0.5% to 3.0% by weight of cement, with a

variation of 0.5 % interval. The effect of fiber is been studied for two types of dispersion of fiber

1.Dispersed both in tension and compression zone 2.Dispersed only in tension zone. The flexural strength

of slab with fiber reinforced concrete for nine point loading is been studied, the grade of concrete

considered is M20. The results are compared with conventional concrete. It is been found that with the

addition of fiber the strength of concrete considerably increases. Addition of plastic fiber of 1.5%shows

the maximum increase in strength of concrete. The addition of plastic fiber increases the strength

maximum at first crack level compared to ultimate load level. The number of cracks developed in fiber

reinforced concrete is very less compared to conventional concrete.

Keywords: Plastic fiber, Fiber reinforced concrete,Flexural strength,Compression and Tension zone,

Tension zone.

References 1. Emilia Vasanellia, Francesco Micellia, Maria AntoniettaAielloa, and Giovanni Plizzarib .Long term behavior of FRC flexural

beams under sustained load . Accepted 23 July 2013 Available online 12 September 2013 Elsevier Ltd .

2. VasanelliE,MicelliF,AielloMA,PlizzariG.Mechanicalcharacterizationoffiber reinforced concrete with steel and polyester fiber.

In: Proceedings of the BEFIB 2008, 7th RILEM international symposium on fibre reinforced concrete, Chennai, India. p. 537–

46. [19] ACI Committee 544.

5-9

3. Measurement of properties of fiber reinforced concrete. ACI report 544.2R-89. Farmington Hillis, Mich: American Concrete

Institute. p. 1–11.

4. . Job Thomas &SyamPrakash V, ‘Strength and Behavior of Plastic Fibre Reinforced Concrete’, Journal of Structural Engg

Vol.26, No.3 October 1999, and pp187-192.

5. Karthikeyan O. H. V. Kumar, D. Singhal and B.D. Nautiyal., ‘Fibres for Fibre Reinforced Concrete-Their Properties

Applications and Mixing’: A Review Report Indian Concrete Institute Journal, March 1991, pp 37-49.

6. ]Foti, N. (2012): Preliminary analysis of concrete reinforced with waste bottles PET fibers. Construction and Building

Materials, ELSEVIER, 25, pp. 1906-1915.

7. PatilShweta&KavilkarRupali (2014) “Study of Flexural Strength in Steel Fibre Reinforced Concrete”.IJRDET, 02(05).

8. Ho N.Y. and YogeshChhabra “Use of glass-fibre reinforced plastics for structural strengthening of reinforced concrete” The

Indian Concrete Journal, Vol.3, 1999, pp.219-224.

9. Kandasamy.R and Murugesan.R “Fibre Reinforced self compacting concrete using domestic waste plastics as fibres” Journal of Engineering and applied science Vol.7, 2012, pp 405-410.

3.

Authors: GNarayana, Naveena M.P, RavichandraR, RamchandraP

Paper Title: Influence of Molarity on Fracture Behaviour in Geopolymer Concrete Beams

Abstract:In this present study an effort was made to know the fracture behavior of Geopolymer concrete

beams with different molarities. The beams which were made of geopolymer concrete with notch were

subjected to three point bending test and load vs. deflection curves for all the members were obtained.

From the obtained data fracture properties such as fracture energy, fracture toughness and nominal stress

were determined. The test showed increasing trend in fracture property such as fracture toughness,

fracture energy and nominal tress in molarity range from 12M to 14M after which it showed decreasing

trending in the molatity range of 14M to 16M..

Index Terms: Geopolymer- geopolymer; fracture behaviour ;three point bending test.

References. 1. Deepa Raj S, “Fracture properties of fibre reinforced geopolymer concrete,” International journal of scientific and engineering

research, vol. 4, no. 5, pp. 75-80, 2013. 2. N Ganesan, “Fracture properties of geopolymer concrete,” Asian journal of civil engieering , vol. 16, no. 1, pp. 127-134, 2015. 3. T.S.Ng, “Mode 1 and 2 fracture behaviour of steel fibre reinforced high strength geopolymer concrete: an experimental

investigation,” Fracture mechanics of concrete and concrete structures, vol. 7, no. 1, pp. 1504-1511, 2010.

10-14

4.

Authors: Lavanya C S, Rajeeva S J, Dr G Narayana

Paper Title: Tracking of Construction Project By EVM Using Microsoft Project Software

Abstract:Earned value management is a method that predicts the project giving early warning of project

cost and schedule. This study is to check effectiveness of EVM in construction industries for tracking the

project progress using MSP software. Tracking will enhance the opportunities for project success.

Tracking helps to monitor the true progress of the activities from starting of project and manage them

using earned value concepts will result in cost saving and time completion

Index Terms: Earned value management, tracking.

References

1. Padma Athani, Sachin Kulkarni, “planning, scheduling and tracking of a residential bungalow using Microsoft project”

IRJET, international journal of engineering research and technology, Vol. 5, pp1362 - 1366, July 2018.

2. Anurag Mahure, Amithkumar Ranit, “planning, scheduling and tracking of building using primavera P6” IJESI, international journal of engineering science invention, Vol. 7, 60 – 64, august 2018.

3. Suchithra L, Anne Ligoria S, “Tracking and management of construction projects using primavera” IRJET, international journal of engineering research and technology, Vol. 4, pp 677-682, July 2017.

4. Amruta B Vyas, B V Birajdar, “Tracking Construction projects by earned value management” IRJET, international journal of engineering research and technology, Vol. 5, pp 829 - 831, March 2016.

5. 5. K. K Chitkar, construction project management, McGraw Hill Education, 1998.

15-19

5.

Authors: Lavanya S, G Narayana

Paper Title: Cost analysis of construction building by earned value method using MS Project software

Abstract:Earned value management is the utmost common method used in study of performance of the

project. EVM incorporates the project possibility, budget and agenda processes to promote the project

management crew to measure project performance from beginning to the end of the project. It is capable

of providing exact predictions of project performance complications, which is an essential role for project

management. EVA is reliant to two important elements such as detailed cost info and practical

development of project. The profit of the project will get esteemed absolutely if these two elements are

well-organized. This paper summarizes the evolution, basic terminologies of earned value analysis and

effective use of it in the construction activities by using MS Project Software. There are many ways to

implement EVM in the construction industry. MS Project is a software to define the earned value and its

factors in an effective method with exactness and within time limits.

Index Terms: Earned value, Cost control, schedule variance, cost variance, tracking.

20-24

References

1. Khalid Mohiuddin Khan, Mr. Masoom Reza, “Earned value management for design and

construction projet”, IJTSRD, 2018, pg.no. 1483-1502.

2. Shabniya V, “Factors Affecting Construction Cost Estimation of Building Projects”, IJRTER,

2017, pg.no. 379-387.

3. Prof. Yogini K. Patil, Prof. Pankaj, “Investigation of Factors Influencing Cost Overrun in High-

Rise Building Constructions”, IJLTET, 2016, pg.no.338-342.

4. Anuj Dubey “Earned Value Analysis for a Construction Project”, IJCIET, 2015, pg.no.53-66.

5. Radhika R. Gupta and Parag S. Mahatme “The Cost Controlling and Monitoring of Construction

Project through Earned Value Management System”, IJATES, 2015, pg.no.651-656.

6. T. Subramani, D S Stephan Jabasingh and J Jayalakshmi, “Analysis of cost controlling in

construction industries by earned value method using Primavera”, IJERA, 2013, pg.no.145-153.

7. Muhammad Waris, Mohd Faris Khamidi and Arazi Idrus, “The cost monitoring of construction

projets through earned value analysis”, JCEPM, 2012, pg.no. 42-45.

8. Sagar K. Bhosekar, Gayatri Vyas, “Cost Controlling Using Earned Value Management in

Construction Industries”, IJEIT, 2012, pg.no. 324-332

6.

Authors: Pallavi G B, P Jayarekha

Paper Title: Secure Multi-tenant Design for Cloud Computing Environment

Abstract:Multi-tenancy is one of the most important elements of cloud computing environment due to

salability and economic benefit it offers for both cloud service provider (CSP) and end user. As resources

are shared, it induces security and privacy issues and tenant dependent on cloud service provider to assign

trustworthy cotenants. However, CSP maximize its resource utilization by allowing maximum co-tenancy

irrespective of the behaviors of tenants. Subsequently techniques like Reputation management based

design aid in identifying good and malicious tenants. However, the state-of-art model is not efficient

when behavior of malicious tenant changes rapidly. Addressing the said research issue, a secure multi-

tenant (SMT) design using a modified reputation management model is proposed which handles dynamic

behavior of tenants efficiently for cloud computing environment. Experiments are conducted using TPCC

benchmark shows significant performance in terms of latency and throughput by SMT over state-of-art

model

Index Terms: Cloud computing, Multi-tenant, Resource scheduling, Reputation management Security.

References 1. NIST Special Publication 800-145, September 2011. 2. Andreas Gobel,”MuTeBench: Turning OLTP-Bench into a Multi-Tenancy Database Benchmark Framework”, 2014, The fifth

International Conference on Cloud Computing, GRIDs and Virtualization, ISBN: 978-1-61208-338-4, 2014.

3. G. B. Pallavi and P. Jayarekha, "An efficient resource sharing technique for multi-tenant databases," 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, pp. 90-95, 2017.

4. D. Breslow, A. Tiwari, M. Schulz, L. Carrington, L. Tang, and J. Mars.Enabling fair pricing on hpc systems with node sharing.In SC, 2013.

5. D. Gmach, J. Rolia, and L. Cherkasova. Selling t-shirts and time shares in the cloud. In CCGRID, 2012. 6. Bates, B. Mood, J. Pletcher, H. Pruse, M. Valafar, and K. Butler, “On detecting co-resident cloud instances using network flow

watermarking techniques,” Int. J. Inf. Secur., vol. 13, no. 2, pp. 171– 189, Apr. 2014. 7. Y. Azar, S. Kamara, I. Menache, M. Raykova, and B. Shepard, “Colocation- resistant clouds,” in Proceedings of the 6th

Edition of the ACM Workshop on Cloud Computing Security, ser. CCSW ’14. New York, NY, USA: ACM, pp. 9–20, 2014.

8. S. Habib, S. Hauke, S. Ries, and M. Mhlhuser, “Trust as a facilitator in cloud computing: a survey,” Journal of Cloud Computing, vol. 1, no. 1, 2012.

9. J. Huang and D. Nicol, “Trust mechanisms for cloud computing,” Journal of Cloud Computing, vol. 2, no. 1, 2013. 10. R. Ko, P. Jagadpramana, M. Mowbray, S. Pearson, M. Kirchberg, Q. Liang, and B. S. Lee, “Trustcloud: A framework for

accountability and trust in cloud computing,” in Services (SERVICES), 2011 IEEE World Congress on, pp. 584–588, 2011.

11. R. Cogranne, G. Doyen, N. Ghadban and B. Hammi, "Detecting Botclouds at Large Scale: A Decentralized and Robust Detection Method for Multi-Tenant Virtualized Environments," in IEEE Transactions on Network and Service Management,

vol. 15, no. 1, pp. 68-82, March 2018.

12. D. Gonzales, J. M. Kaplan, E. Saltzman, Z. Winkelman and D. Woods, "Cloud-Trust—a Security Assessment Model for Infrastructure as a Service (IaaS) Clouds," in IEEE Transactions on Cloud Computing, vol. 5, no. 3, pp. 523-536, 1 July-Sept. 2017.

13. G. Li, J. Wu, J. Li, Z. Zhou and L. Guo, "SLA-Aware Fine-Grained QoS Provisioning for Multi-Tenant Software-Defined Networks," in IEEE Access, vol. 6, pp. 159-170, 2018.

14. W. Ma, Z. Han, X. Li and J. Liu, "A multi-level authorization based tenant separation mechanism in cloud computing environment," in China Communications, vol. 13, no. 5, pp. 162-171, May 2016.

15. O. Abdel Wahab, J. Bentahar, H. Otrok and A. Mourad, "Optimal Load Distribution for the Detection of VM-based DDoS Attacks in the Cloud," in IEEE Transactions on Services Computing. doi: 10.1109/TSC.2017.2694426

16. F. Banaie and S. A. H. Seno, "A cloud-based architecture for secure and reliable service provisioning in wireless sensor network," 2014 4th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, 2014, pp. 96-101.

17. S. Thakur and J. G. Breslin, "A Robust Reputation Management Mechanism in Federated Cloud," in IEEE Transactions on Cloud Computing. doi: 10.1109/TCC.2017.2689020.

25-29

7.

Authors: Radha B.Kalaskar, Bharati Harsoor

Paper Title: An End-to-End Point of Cardiovascular Body Sensor Network with Cloud Service

Abstract:India is the world capital of cardiovascular diseases and there is an immense shortage of doctors

to serve the patients. This work focuses on cardiovascular sensor data collection and processing. Unlike 30-33

other wireless body sensor network where the parameters are discrete, often cardiovascular data analysis

needs continuous data at a high sampling rate. Such wireless signal gathering over a continuous wireless

channel hasn’t been proposed so far due to critical consequences of noise in that signal during

transmission. Furthermore, existing many techniques proposes a small data collection in a local node and

then dissipating them to cloud. But, continuous wireless sensor signal data transmission and simultaneous

processing hasn’t been successfully performed. This work addresses the aforementioned issue and

delivers an end-to-end sensor network solution to acquire continuous cardiac signal, transmission to a

local processing node and mitigating the data to cloud in real time and also implemented simple heart rate

monitoring algorithm of the cloud to visualize continuous heart rate of a patient with this sensor node.

Index Terms: Cardiovascular Disease (CVD), Electrocardiogram (ECG), Biomedical Single Processing

(BSP), Ballistocardiograpy (BCG), Artificial Neural Network (ANN), Artificial Intelligence (AI),

Wireless Sensor Network (WSN).

References

1. Shu-Yu Hsu, Yao-Lin Chen, Po-Yao Chang, Jui-Yuan Yu, Ten-Fang Yang, Ray-Jade Chen, Chen-Yi Lee “A micropower biomedical signal processor for mobile healthcare applications”. November 14-16, 2011.

2. Qiuzhen Xue, Yu Hen Hu ,Willis J. Tompkins “Neural network based adaptive matched filtering for QRS detection” 3. Hiroshi Nakajima, Yutaka Hata, Toshikazu Shiga “Systems Health Care”, IEEE 2011. 4. Masatoshi Sekine and Kurato Maeno “Non-contact heart rate detection using periodic variation in Doppler frequency”, IEEE

2011.

5. Reza S. Dilmaghani, Hossein Bobarshad, M. Ghavami, Sabrieh Choobkar, and Charles Wolfe “Wireless sensor networks for monitoring physiological signals of multiple patients”, IEEE 2011.

6. Christoph Bruser, Stefan Winter and Steffen Leonhardt “How speech processing can help with beat-to-beat heart rate estimation in ballistocardiograms”,IEEE 2013.

7. Diptee C. Pandhe , H. T. Patil , "ECG Data Compression for a Portable ECG Recorder and Transmitter" IEEE 2014. 8. Yangdong (Jack) Liao, Ru-Xin (Tony) Na, and Derek Rayside, "Accurate ECG R-peak detection for telemedicine" , IEEE 2014. 9. Krishna Bharadwaj Chivukula, Naresh Vemishetty, Agathya Jagirdar, and Amit Acharyya, "A low-complexity onchip real-time

automated ECG frame identification methodology targeting remote heath care", IEEE 2014. 10. G. Warmerdam, R. Vullings, C. Van Pul, P. Andriessen, S.G. Oei, P. Wijn "QRS classification and spatial combination for robust

heart rate detection in low-quality fetal ECG recordings" , IEEE July 2013

8.

Authors: Manjunath S, Sanjay Pande M B, Madhusudhan G K

Paper Title: Brain Tumor Detection and Classification Using Convolution Neural Network

Abstract:Understanding Human activity has lead researchers to work on one of the major organ of

human body namely Brain. The smooth function of Human Brain enhances the activities of human body.

The systematic working of Human brain is affected by various causes. In the present work, we have taken

one such cause that is Brain tumor, which is mainly due to abnormal growth of Cells in Brain.The

recognition of Brain is generally done by Magnetic resonance imaging (MRI). The major drawback of

this is to find the exact location/position. Hence it becomes important to find the means and methods to

detect, identify and classify the disease based upon the image. The proposed work involves Extraction to

grading of Tumor to be relevant class. The complexity of the present work is due to conversion of the

extracted image as symbolic data and use of Convolution Neural networks. The experimentation were

corroborated with BPNN and CNN classifier.

Keywords: Convolution, Neural Network, Brain Tumor, LBP,Fuzzy-C

References

1. NileshBhaskarraoBahadure, Arun Kumar Ray, and Har Pal Thethi, Image Analysis for MRI Based Brain Tumor

Detection and Feature Extraction Using Biologically Inspired BWT and SVM, International Journal of Biomedical

Imaging Volume 2017, Article ID 9749108, 12 pages https://doi.org/10.1155/2017/9749108.

2. Luxitkappor,SanjeevThankur, A survey on Brain tumor detection using image processing techniques, 7 th International conference on cloud computing, Data Science & Engineering – Confluence ,page no. 582 – 585.

3. Sindhu, S.Meera, A survey on Detecting Brain Tumor in MRI, IJIRCCE, Vol. 3, Issue 1, January 2015. 4. Malathi R, Dr Nadirabanu Kamal A R ,Brain tumor detection and identification using K – Means clustering technique, ,

IJANA, 27th March 2015.

5. Sindhu, S.Meera,A survey on Detecting Brain Tumor in MRI, IJIRCCE, Vol. 3, Issue 1, January 2015. 6. Ed – EdilyMohd. Ansari, Muhd. MuddZakkirMohd.Hatta, ZawZawHtike and Shoon Lee Win, Tumor detection in

medical imaging: A survey, IJAIT, Vol 4, No. 1, February 2014.

7. Ana sanjuan et al., Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors, frontiers in neuroscience, methods article, 17th December 2013, doi: 3389/fnins.2013.00241

8. R.Suganya, R.Shanthi, Fuzzy C-Means Algorithm – A Review, IJSRP, Volume 2, Issue 11, November 2012, ISSN 2250-3153.

9. T. Rajesh, R. Suja Mani Malar,” Rough Set Theory and Feed Forward Neural Network Based Brain Tumor Detection in Magnetic Resonance Images",IEEE International Conference on Advanced Nanomaterials& Emerging Engineering

Technologies, 20 13. 10. R. J. Ramteke1, KhachaneMonali Y., “Automatic Medical Image Classification and Abnormality Detection Using K-

Nearest Neighbour" , International Journal of Advanced Computer Research,Volume-2 Number-4 Issue-6 December-

2012. 11. MohdFauzi Othman, MohdAriffanan, MohdBasri, "Probabilistic Neural Network for Brain Tumor Classification" ,IEEE

International Conference on Intelligent Systems, Modelling and Simulation,2011.

12. Xiao Xuan, Qingmin Liao, Statistical Structure Analysis in MRI Brain Tumor Segmentation" ,IEEE International Conference on Image and Graphics, 2007.

34-40

13. Hiremath P, Shivashankar S. Wavelet basedfeatures for texture classification. Graphics,Vision and Image Processing Journal;6:55-8.2006.

14. Huang K, Aviyente S. Wavelet featureselection for image classification. IEEE Transaction on Image Processing; 17:1709-20, 2008.

15. Shijinkumar P.S etal.Extraction of texture features using GLCM and shape features using connected regions, IJET, ISSN: 0975 - 4024.

16. T .Ojala, M. Pietikainen and D. Harwood, “A comparative study of texture measures with classification based on feature distributions” Pattern Recognition vol. 29, 1996.

17. Md. Adbul Rahim, Md. NajmulHossain, Tanzillah Wahid & Md. ShafilAzam, Face recognition using Local Binary Patterns (LBP), Global Journal of computer science and technology graphics & vision, volume 12 Issue 4 version 1.0 2013.

18. Ira Cohen, Qi Tian, Xian Sean Zhou, Thomas S.Huang, Feature Selection Using Principle Feature Analysis, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana.

19. Y. LeCun, K. Kavukvuoglu, and C. Farabet.Convolutional networks and applications in vision. In Circuits and Systems, International Symposium on, pages 253–256, 2010.

20. Krizhevsky, I. Sutskever, and G. E. Hinton.Image Net classification with deep convolutional neural networks.In Advances in Neural Information Processing Systems, pages 1097–1105, 2012.

21. K. Jarrett, K. Kavukcuogl, M. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In Computer Vision, International Conference on, pages 2146–2153, 2009.

22. Scherer, A. Müller, and S. Behnke.Evaluation of pooling operations in convolutional architectures for object recognition. In Artificial Neural Networks, International Conference on, pages 92 –101, 2010.

9.

Authors: Aditya Pai H, Sameena H S, Sandhya Soman, Piyush Kumar Pareek

Paper Title: ROC Structure analysis of Lean Software Development in SME’s Using Mathematical

CHAID Model

Abstract:These days, numerous software associations are utilizing Agile philosophies to improve the

execution of their procedures. In any case, some of them are discovering benefits in the better approaches

for improving these officially settled procedures. Lean software development has been utilized to upgrade

these procedures significantly more, for the most part because of the decrease of waste. So as to have the

capacity to push forward the impact of this marvel, giving progressively empiric proof on this theme is

required. This Paper attempts to present a questionnaire survey summarized results of SME’s in

Bengaluru regarding Lean software development , Results are analysed using IBM SPSS package , The

questionnaire used was verified using Cronbach alpha test reading a high reliable and valid status of the

conduction of collection process .

Index Terms: Agile, IBM SPSS, Cronbach Alpha Test,SMEs

R References 1. Kupiainen, E., Mäntylä M. V., Itkonen J., "Utilizing Metrics in Agile and Lean Software Development - A Systematic

Literature Review of Industrial Studies", Information and Software Technology, 2015.

2. Brian Fitzgerald et.al., "Scaling agile strategies to managed situations: an industry contextual investigation", ICSE '13 Proceedings of the 2013 International Conference on Software Engineering, Pages 863-872.

3. Robert Imreh , Mahesh S. Raisinghani ,"Impact of Agile Software Development on Quality inside Information Technology Organizations", VOL. 2, NO. 10, October 2011, ISSN 2079-8407, Journal of Emerging Trends in Computing and Information Scienc

4. Sandhya Tarwani, Anuradha Chug, "AGILE METHODOLOGIES IN SOFTWARE MAINTENANCE:A SYSTEMATIC REVIEW", Informatica, Vol 40, No 4 (2016).

5. Taghi Javdani Gandomani, HazuraZulzalil, Abdul Azim Abdul Ghani, Abu BakarMd Sultan, "A Systematic Literature Review on connection between agile strategies and Open Source Software Development technique", International survey

on PCs and software (IRECOS), 2012, Vol. 7, Issue 4, pp. 1602-1607. 6. Jarosław Berłowskiaet.al., "Profoundly Automated Agile Testing Process: An Industrial Case Study", e-Informatica

Software Engineering Journal, Volume 10, Issue 1, 2016.

7. S. Harichandan , N. Panda , A.A. Acharya, "Scrum Testing With Backlog Management in Agile Development Environment", International Journal of Computer Sciences and Engineering, Volume-2 , Issue-3 , Page no. 187-192,

Mar-2014.

8. AthulJayaram, "Lean Six Sigma Approach for Global Supply Chain Management utilizing Industry 4.0 and IIoT", second International Conference on Contemporary Computing and Informatics (IC3I), pp. 89– 94, 2016.

9. Man Mohan Siddh, GunjanSoni, Gaurav Gadekar and Rakesh Jain, "Incorporating lean six sigma and inventory network approach for quality and business execution", second International Conference on Business and Information

Management (ICBIM), pp. 53– 57, 2014.

10. Zhang Yuan, Xu Yan and Zhao Xuan, "Exploration on the utilization of Six-Sigma's strategy to Supply Chain Management", IEEE International Conference on Automation and Logistics, pp. 76– 81, 2010.

11. Yousef Amer, Lee Luong, Sang-Heon Lee, William Y C Wang, Muhammad Azeem Ashraf and Zahid Qureshi, "Executing Design for Six Sigma to Supply Chain Design", IEEE International Conference on Industrial Engineering and

Engineering Management, pp. 1517– 1521, 2007. 12. JingyueXu, "A Six Sigma-Based Methodology for Performance Measurement of a Supply Chain", fourth International

Conference on Wireless Communications, Networking and Mobile Computing, pp. 1– 4, 2008.

13. LI Wei，MA Yi-zhong, "A Framework for Using Six-sigma Metrics to Evaluate the Performance of Supply Chain",

twentieth International Conference on Management Science and Engineering, pp. 664– 669, July 2013.

14. PratimaMishrai and Rajiv Kumar Sharmaii, "Joining of Six Sigma and ISM to improve Supply Chain Coordination – An applied system", International Journal of Production Management and Engineering, pp.75– 80, 2014.

15. Erin M.Mitchell and JamisonV.Kovach, "Improving store network data sharing utilizing Design for Six Sigma", European Research on Management and Business Economics 22, pp.147– 154, 2016.

16. Firat Kart, Louise Moser, Peter Melliar-Smith, "An Automated Supply Chain Management System and Its Performance Evaluation", International Journal of Information Systems and Supply Chain Management, Vol.3, Iss.2, pp. 84-107, April

2010.

41-45

10.

Authors: Swathi K, Arun Biradar

Paper Title: Analysing the Adaptability of Software Defect Prediction in Small Software Firms

Abstract:Software Defect Prediction [SDP] assumes an imperative job in the dynamic research territories 46-52

of software engineering. A software defect is an error, bug, glitch or error in software that makes it make

a wrong or startling result. The significant hazard factors related with a software defect which isn't

recognized amid the early period of software development are time, quality, cost, exertion and wastage of

assets. Defects may happen in any period of software development. Blasting software organizations

center fixation on the software quality, especially amid the early period of the software development.

Thus the key goal of any association is to decide and address the defects in an early period of

development process of the software. The research carried out in this paper shows how we have attempted

to perceive the troubles in Software Defect Prediction with the help of a survey outline, a sorted out

survey toward this way was set up with 71 questions and 370 respondents down the results using IBM

SPSS Tool.

Index Terms: Software Defect Prediction; Software Engineering; component; SPSS Tool

References 1. Pooja Paramshetti , D. A. Phalke Survey on Software Defect Prediction Using Machine Learning Techniques International

Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Impact Factor (2012): 3.358 Volume 3 Issue 12, December 2014 .

2. Xiaoxing Yang , Ke Tang , Senior Member, IEEE, and Xin Yao , Fellow, IEEE, A Learning-to-Rank Approach to Software Defect Prediction, This article has been acknowledged for consideration in a future issue of this diary. Content is last as exhibited, except for pagination (2015)

3. Baljinder Ghotra, Shane McIntosh, Ahmed E. Hassan Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models , Software Analysis and Intelligence Lab (SAIL) School of Computing, Queen's University,

Canada (2014)

4. Er.Rohit Mahajan,Dr. Sunil Kumar Gupta, Rajeev Kumar Bedi, COMPARISON OF VARIOUS APPROACHES OF SOFTWARE FAULT PREDICTION: A REVIEW International Journal of Advanced Technology and Engineering Research (IJATER) www.ijater.com ISSN No: 2250-3536 Volume 4, Issue 4, July 2014

5. Pradeep Kumar Singh, Ranjan Kumar Panda and Om Prakash Sangwan , A Critical Analysis on Software Fault Prediction Techniques World Applied Sciences Journal 33 (3): 371-379, 2015 ISSN 1818-4952

6. Wanjiang.Han , Lixin. Jiang Tianbo. Lu,Xiaoyan and. Zhang,Sun Yi , Study on Residual Defect Prediction utilizing Multiple Technologies , JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY, VOL. 5, NO. 3, AUGUST 2014

7. Emad Shihab , Practical Software Quality Prediction Department of Computer Science and Software Engineering Concordia University , 2014

8. Carol Woody, Robert Ellison and William Nichols , Predicting Software Assurance Using Quality and Reliability Measures, Software Engineering Institute , Dec 2014.

9. A.C. Catal, Software blame prediction: "A literature survey and current patterns," Expert frameworks with applications, vol. 38, no. 4, pp. 4626-4636, 2011.

10. Y. Kamei, A. Monden, S. Morisaki, and K.- I. Matsumoto, A crossover flawed module prediction utilizing affiliation rule mining and strategic relapse examination," in Proceedings of the Second ACM-IEE global symposium on Empirical software

engineering and estimation, pp. 279-281, ACM, 2008.

11. G. Czibula, Z. Marian, and I. G. Czibula, "Recognizing software configuration defects utilizing social affiliation rule mining," Knowledge and Information Systems, pp. 1-33, 2012.

12. Campan, G. Serban, T. M. Truta, and A. Marcus, "A calculation for the disclosure of subjective length ordinal affiliation rules," DMIN, vol. 6, pp. 107-113, 2006.

13. Okutan, Ahmet, and Olcay Taner Yıldız. "Software defect prediction utilizing Bayesian systems." Empirical Software Engineering 19.1 (2014) 154-181

14. J. Nam, Survey on software defect prediction," 2010. 15. D. Dim, D. Bowes, N. Davey, Y. Sun, and B. Christianson, "Software defect prediction utilizing static code measurements

belittles defect-inclination," in Neural Networks (IJCNN), The 2010 International Joint Conference on, pp. 1-7, IEEE, 2010.

16. Naidu, M. Surendra, and N. GEETHANJALI. "Order of Defects in Software utilizing Decision Tree Algorithm." International Journal of Engineering Science and Technology (IJEST) 5.06 (2013).

17. M. M. T. Thwin and T.- S. Quah, Application of neural systems for software quality prediction utilizing object-arranged measurements," Journal frameworks and software, vol. 76, no. 2, pp. 147-156, 2005

18. Text Book on Software Engineering by Pressman

11.

Authors: Amareshwari Patil, Bharati Harsoor

Paper Title: Competent Asset Planning for Cloud Computing

Abstract:Cloud Computing is being widely utilized in today’s world. The proposed system comes under

the Iaas (Infrastructure as a service) which provides CPU and memory as a resource. As cloud computing

is very popular, the users of cloud are also increasing accordingly and this has become the important issue

for the cloud service providers in terms of load balancing. Each of the client requests has to be executed

with proper allocation of assets. The major challenge is to understand how these requests are allocated to

the user. Management of assets and allocation of assets has to be done proficiently so a s to increase the

system utilization and also overall performance of the system else this becomes the serious issue for

governing the cloud environment with multiple customers. In the proposed system dynamic load

balancing concepts have been used which helps in fair allocation of resources to achieve high user

satisfaction as well as proper asset utilization. This proposed model has a Controller and Balancers that

gather and analyze the information. Status of the server is monitored and then appropriate load balancing

techniques selected on the basis of system status as a achieve resource utilization.

Index Terms: Cloud Partition, Assets, Balancer, Skewness, Load balancing.

References

1. S. Shin, Y. Kim, and S. Lee, "Due date ensured booking calculation with enhanced asset usage for distributed computing," in 2015 twelfth Annual IEEE Consumer Communications and Networking Conference (CCNC), pp. 814–819, IEEE 2015.

2. Y. Han and X. Luo, "A powerful calculation and displaying for data assets planning for distributed computing," in Advanced Cloud and Big Data (CBD), 2013 International Conference on, pp. 14– 19, IEEE 2013.

3. V. K. Prasad, "Load adjusting and booking of assignments in parallel handling condition".

53-56

4. L. Luo, W. Wu, D. Di, F. Zhang, Y. Yan, and Y. Mao, “A resource scheduling algorithm of cloud computing based on energy efficient optimization methods,” in Green Computing Conference (IGCC), 2012 International, pp. 1– 6, IEEE, 2012

5. Kanani Bhavisha Bhumi Maniyar "Review on Max-min Task Scheduling Algorithm for Cloud Computing" Journal of Emerging Technologies and Innovative Research vol. 2 pp. 781-784 2015.

6. Choudhary Monika Sateesh Kumar Peddoju "A dynamic optimization algorithm for task scheduling in cloud environment" International Journal of Engineering Research and Applications (IJERA) vol. 2 no. 3 pp. 2564-2568 2012

7. Singh Raja Manish Sanchita Paul Abhishek Kumar "Task Scheduling in Cloud Computing: Review" International Journal of Computer Science and Information Technologies vol. 5 no. 6 pp. 7940-7944 2014.

8. R. S. Jha and P. Gupta. “Power & load aware resource allocation policy for hybrid cloud” . Procedia Computer Science, 78:350–357, 2016.

9. R. Jemina Priyadarsini L. Arockiam "PBCOPSO: A parallel optimization algorithm for task scheduling in cloud environment" Indian Journal of Science and Technology vol. 8 no. 16, 2015.

10. P.-J. Maenhaut H. Moens B. Volckaert V. Ongenae F. D. Turck "A simulation tool for evaluating the constraint-based allocation of storage resources for multi- tenant cloud applications" 2016 IEEE/IFIP Network Operations and Management

Symposium (NOMS 2016) pp. 1017-1018 April 2016

11. Amareshwari Patil, Bharati Harsoor ”Survey on techniques for utilization of resources in cloud computing system” (ICCRAES-16,) Vol 23,issue 06 2016,ISSN:0976-1353

12. Amareshwari Patil,Bharati Harsoor”Study on dexternity for utilization of resourses in cloud computing system 2019 JETIR Vol 6 Issue 3

13. Jain Kansal, Inderveer Chana, Cloud Load balancing techniques:A step towards green computing, IJCSI International Journal of Computer Science Issues, Vol.9,Issue 1,2012

14. Zenon Chaczko, Venkatesh Mahadevan, Shahrzad Aslanzadeh and Christopher Mcdermid, Availabilty and load balancing in cloud computing, 2011 International Conference on Computer and Software Modeling, IPCSIT vol.14 (2011) ACSIT Press,

Singapore.

15. Tanveer Ahmed, Yogendra Singh, Analytic study of load balancing techniques using tool cloud analyst. 16. George Reese, “Cloud Application Architectures,” Pub. O’Reilly Media, it-ebooks.info/book/286/, pp.1-10, 2009.

12.

Authors: Divya R, Nandini Prasad K. S

Paper Title: Automation of Desktop Applications using Keyword Driven Approach

Abstract:In the recent era, the most challenging task to any IT field or a company is to consistently

progress and maintain the standards as for quality and also to develop the software systems efficiently. In

most of the projects related to software, due to the constraints of time and value, testing is mostly to be

neglected. However, manual testing increases time, cost, and is therefore not economical. Software testing

primarily involves execution of a program to seek out errors. Effective testing creates top quality code.

Automation testing, otherwise called Test Automation, is a point at which product is tested using software

by writing scripts. This is a process of automation wherein the manual steps are automated through the

software. Manual Testing requires an individual’s effort to be spent in front of a computer for careful

execution of test scenario steps. Testing scenario that undergo Automation requires Automation tool for

the execution of the test cases. The software of automation gives us an added advantage of testing the

system by entering the test data, wherein the results of actual are compared with the expected results to

generate detailed test reports. Automation Testing focuses on the key area where the test case suites are

executed in a quicker and repeated manner when compared with running the test cases manually.

Index Terms: Automation Testing, Manual Testing, Test scenario, test data

References 1. Ms. Rigzin Angmo, and Mrs. Monika Sharma, “Performance Evaluation of Web Based Automation Testing Tools” 731978-1-

4799-4236-7/14/ @2014 IEEE 2. Hari Sankar Chaini, Dr. Sateesh Kumar Pradhan, “Test Script Execution and Effective Result Analysis in Hybrid Test

Automation Framework” 978-1-4673-6911-4/15/$31.00©2015 IEEE

3. Jingfan Tang, Xiaohua Cao and Albert Ma, “Towards Adaptive Framework of Keyword Driven Automation Testing” 978-1-4244-2503-7/08/$20.00 © 2008 IEEE

4. Dietmar Winkler, Reinhard Hametner, Thomas Östreicher and Stefan Biffl, “ A Framework for Automated Testing of Automation Systems” 978-1-4244-6850-8/10/$26.00 ©2010 IEEE.

5. Zeng Wandan, Jiang Ningkang and Zhou Xubo, “ Design and Implementation of a Web Application Automation Testing Framework” 978-0-7695-3745-0/09 $25.00 © 2009 IEEE

6. Hung Q. Nguyen, “The Design and implementation of a flexible, reusable and maintainable Automation Framework”, 2010. 7. Li Feng, Sheng Zhuang, “Action-Driven Automation Test Framework for Graphical User Interface (GUI) Software Testing”,

Autotestcon, 2007 IEEE, pp.22-27.

8. Linda G. Hayes, “The Automated Testing Handbook”, page 81-88. 9. H. Kaur, Dr. G. Gupta, “Comparative Study of automation testing tools:selenium, quick test professional and testcomplete,”

International Journal of Engineering Research and Application, vol. 3, no. 5, pp. 1739-1743, 2013.

10. https://support.smartbear.com/testcomplete/docs/

57-61

13.

Authors: Harshitha M R, Harshitha J S, Brunda K S, Shrihari M R

Paper Title: An Approach for Supervising the Security Threats using Software Defined Networks

Abstract: Providing protection for the network is major significant subject by continued existence of

systems which are allied by means of network in this world, which broadcast information regarding every

part of circumstances in our life and occupation. The systems which have fine security to a network

would support business in addition to, it decreases the hazard of diminishing fatality in favor of data theft

and sabotage. The framework of software defined networking (SDN) disjoins the data and control planes.

The fundamental set of connections (network) structural design is inattentive from applications, the state

of a network along with brilliance are logically integrated. It increases security for a network with the

help of overall visualness of the network condition wherever a collision could be straightforwardly

decided commencing the understandably united control plane. The SDN has some types of mechanics

together with network virtualization, functional separation along with computerization by practicability

by programs. Basically, SDN equipment mainly paid attention on partition of the control plane out of the

network data plane. Based on the packets flow through the network the control plane generates outcomes,

while the plane of data shifts packets from one position to another position. Even so, open protection

difficulties, like man-in-the middle attacks, denial of service (DoS) attacks, along with saturation attacks.

The design of SDN authorizes networks toward actively observes the transfer passage and analysis the

risk to simplifies network disputation, safety procedure modification, and safety examine inclusion. In

this paper, we examine safety threats to appliance, the planes of SDN that is data and control plane. The

safekeeping designs that protect all the planes are defined and succeeded by different safety ways for

network-wide security in SDN. The safety of SDN is examined conceding to protection proportions of the

ITU-T recommendation, in addition with the costs of security keys. Highlighting the present and

upcoming safety difficulties in SDN and expecting guidelines for safe SDN in this paper.

Index Terms: SDN, OpenFlow, network security, SDN security, application plane, control plane, data

plane.

References

1. B.Raghavan et al., “Software-defined internet architecture: decoupling architecture from infrastructure,” in Proc. 11th ACM Workshop Hot Topics Netw., 2012, pp. 43–48.

2. N. McKeown et al., “OpenFlow: Enabling innovation in campus networks,” ACM SIGCOMM Comput. Commun. Rev., vol. 38, no. 2, Apr. 2008.

3. H. Hamed and E. Al-Shaer, “Taxonomy of conflicts in network security policies,” IEEE Commun. Mag., vol. 44, no. 3, pp. 134–141, Mar. 2006.

4. .A. Wool, “A quantitative study of firewall configuration errors,” Computer, vol. 37, no. 6, pp. 62–67, Jun. 2004. 5. S. Scott-Hayward, G. O’Callaghan, and S. Sezer, “SDN security: A survey,” in Proc. IEEE SDN4FNS, 2013, pp. 1–7. 6. D. Clark, R. Braden, K. Sollins, J. Wroclawski, and D. Katabi, “New Arch: Future generation Internet architecture,” Def.

Tech. Inf. Center (DTIC),Fort Velvoir, VA, USA, Tech. Rep. AFRL-IF-RS-TR-2004-235, 2004.

7. D. L. Tennenhouse, J. M. Smith, W. D. Sincoskie, D. J. Wetherall, and G. J. Minden, “A survey of active network research,” IEEE Commun.Mag., vol. 35, no. 1, pp. 80–86, Jan. 1997.

8. D. L. Tennenhouse and D. J. Wetherall, “Towards an active network architecture,” in Proc. DARPA Active NEtw. Conf. Expo., 2002, pp. 2–15.

9. Z. Liu, R. Campbell, and M. Mickunas, “Active security support for active networks,” IEEE Trans. Syst., Man, Cybern., Part C, Appl. Rev.,vol. 33, no. 4, pp. 432–445, Nov. 2003.

10. .S. Murphy, E. Lewis, R. Puga, R. Watson, and R. Yee, “Strong security for active networks,” in Proc. IEEE Open Archit. Netw. Programm.,2001, pp. 63–70.

11. A. Greenberg et al., “A clean slate 4D approach to network control and management,” ACM SIGCOMM Comput. Commun. Rev., vol. 35, no. 5, pp. 41–54, Oct. 2005.

12. Z. Cai, A. L. Cox, and T. E. N.Maestro, “Maestro: A system for scalable OpenFlow control,” RiceUniv.,Houston, TX,USA, Tech.Rep. TR10-08, 2010.

13. M. Casado et al., “SANE: A protection architecture for enterprise networks,” in Proc. Usenix Security, 2006, pp. 137–151.

14. M. Casado et al., “Ethane: Taking control of the enterprise,” ACM SIGCOMM Comput. Commun. Rev., vol. 37, no. 4, pp. 1–12, Oct. 2007.

15. Y. Jarraya, T. Madi, and M. Debbabi, “A survey and a layered taxonomy of software-defined networking,” IEEE Commun. Surveys Tuts., vol. 16, no. 4, pp. 1955–1980, 4th Quart. 2014.

16. S. Namal, I. Ahmad, S. Saud, M. Jokinen, and A. Gurtov, “Implementation of OpenFlow based cognitive radio network architecture: SDN&R,” in Wireless Networks. New York, NY, USA: Springer, 2015, pp. 1–15.

17. M. Liyanage, A. Gurtov, and M. Ylianttila, Software Defined Mobile Networks (SDMN): Beyond LTE Network Architecture. Hoboken, NJ,USA: Wiley, 2015.

18. S. J. Vaughan-Nichols, “OpenFlow: The next generation of the network?” Computer, vol. 44, no. 8, pp. 13–15, Aug. 2011.

19. T. Nadeau, “Software driven networks problem statement,” Network Working Group Internet-Draft, Sep. 30, 2011. [Online]. Available:https://tools.ietf.org/html/draft-nadeau-sdn-problem-statement-00

20. .H. Xie, T. Tsou, D. Lopez, H. Yin, and V. Gurbani, “Use cases for ALTO with software defined networks,” Working Draft, IETF Secretariat, Internet-Draft, 2012.[Online]. Available: https://tools.ietf.org/ html/draft-xie-alto-sdn-use-cases-

01

62-68

14.

Authors: Jagadish N, Pranay Saha, Sunny Singh

Paper Title: An Efficient Machine Learning Framework for Speaker Authentication using Voice Input

Abstract:With the advancements in the hardware industry, an increase in the computation power and

development in Artificial Methods, we can think of working on cognitive tasks. We have worked on

various speech recognition methods using Natural language processing and Hidden Markov Models. We

have done the classification of the users on the basis of their utterances. In this paper, we propose a

discrete probability approach. The result which we have got gives us high accuracy results in recognizing

the speakers. This helps in concluding that Learning and Uncertain reasoning are important components

of Artificial Intelligence that could help in the development of solutions to problems which are interesting

and complex.

Index Terms: Machine learning, VUI, Cognitive technology, NLP.

References

69-72

1. Speaker identification experiments using HMMs Webb,J.J.; Rissanen, E.L.; Acoustics, Speech, and Signal Processing, 1993.

ICASSP-93,1993 IEEE International Conference on, Volume: 2 ,27-30 April 1993 Page@): 387 -390 v01.2

2. Speaker identification using bidden Markov models, Inman, M.; Danforth, D.; Hangai, S.; Sato, K.; Signal Processing hceedmgs. 1998. ICSP'98.1998 Fo@ Intemational Conference on, 12-16 Oct. 1998 Page@): 609 -612 V0l.l

3. Speaker identification using Hidden Conditional Random Field-based speaker models, Wei-Tyng Hong, 2010 International Conference on Machine Learning and Cybernetics, Year: 2010 , Volume: 6, Pages: 2811 - 2816

4. H. C. Andrews, Mathematical Techniques in Pattern Recognition 5. E. Bunge, "AUROS—Automatic recognition of speakers by computers principles of the speaker recognition system", 9th

International Congress on Acoustics, 1977. 6. S. Furui, "An analysis of long-term variation of feature parameters of speech and its application to talker recognition",

Electr.and Comm. in Japan, vol. 57-A, pp. 34-42, 1974.

7. H. Ney, "Telephone-line speaker recognition using clipped autocorrelation analysis", Acoustics Speech and Signal Processing IEEE International Conference on ICASSP '81., vol. 6, pp. 188-192, 1981.

8. M. Sidorov, A. Schmitt, S. Zablotskiy, W. Minker, “Survey of automated speaker identification systems,” in Proc. 9th Int. Conf. Intell. Environ. , 236-239, 2013

9. Fuzzy-Clustering-Based Decision Tree Approach for Large Population Speaker Identification, Yakun Hu ; Dapeng Wu ; Antonio Nucci

10. SocialSense: A Collaborative Mobile Platform for Speaker and Mood Identification, Mohsin Y. AhmedSean KenkeremathJohn Stankovic

Text-independent speaker identification using Radon and discrete cosine transforms based features from speech spectrogram,

Pawan K.Ajmera, Dattatray V.Jadhav,Raghunath S.Holambe

15.

Authors: Chandramma, Sameena H S, Sandhya Soman, Dr.Piyush Kumar Pareek

Paper Title: Fast and Efficient Parallel Alignment Model for Aligning both Long and Short Sentences

Abstract:Recently, demand for fast and efficient translation system been widely seen. However,

translation model are dependent parallel corpora. However, it is challenging to obtain large parallel

corpora for resource starved language such as Kannada-Telugu pair. The existing Giza++ based word

alignment and Moses phrase based alignment model are efficient for aligning only short sentences.

However, for longer sentence the accuracy of model degrades. For performing alignment for longer

sentences, neural based alignment has been presented in recent times. However, these models are trained

using fixed vector length. Thus, induces memory and training overhead. For overcoming research

challenges, this work presents a parallel alignment model using recurrent neural network (RNN). Further,

to utilize memory efficiently and minimize training time parallel execution of RNN under GPU is

considered. For improving alignment accuracy presented a cost function by combing statistical and neural

based alignment method. Experiment are conducted to evaluate the proposed alignment model in terms

of accuracy, Word alignment error (WAE), memory utilization, and computation time.

Index terms—Neural alignment model, Phrase alignment, statistical alignment, Word alignment model.

References

1. Bollen, H. Mao, X. Zeng. 'Twitter mood predicts the stock market." Journal of Computational Science. Vol. 2(1), pp. 1-8,

March 2011.

2. G. Hinton, L. Deng , D. Yu, GE. Dahl, AR. Mohamed, N. Jaitly, et al. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups". IEEE Signal Processing Magazine, vol. 29(6), pp. 82-97, Nov. 2012.

3. [D. Ciregan, U. Meier, 1. Schmidhuber, "Multi-column deep neural networks for image classification." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference, pp. 3642-3649, Jun. 2012.

4. MA. Castano, F. Casacuberta, E. Vidal, "Machine translation using neural networks and finite-state models," Theoretical and Methodological Issues in Machine Translation, pp. 160-167, Jul. 1997.

5. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google’s neural machine translation system: Bridging the gap between

human and machine translation, arXiv:1609.08144, 2016.

6. Josep Crego, Jungi Kim, and Jean Senellart. Systran’s pure neural machine translation system, arXiv:1602.06023, 2016. 7. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le.Sequence to Sequence Learning with Neural Networks.In NIPS.page 9.

arxiv:1409.3215, 2014.

8. Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.In

EMNLP, 2014.

9. Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. Effective Approaches to Attention based Neural Machine Translation. In EMNLP, 2015.

10. Chandramma, P. Kumar Pareek, K. Swathi and P. Shetteppanavar, "An efficient machine translation model for Dravidian language," 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication

Technology (RTEICT), Bangalore, pp. 2101-2105, 2017.

11. Koehn, P., Och, F. J., and Marcu, D. Statistical phrase-based translation.In Conference of the North American Chapter of the Association for Computational Linguistics, 2003.

12. Devlin, J., Zbib, R., Huang, Z., Lamar, T., Schwartz, R. M., and Makhoul, J. Fast and robust neural network joint models for statistical machine translation. In ACL (1), Citeseer, pp. 1370–1380, 2014.

13. [Kalchbrenner, N., and Blunsom, P. Recurrent continuous translation models.In Conference on Empirical Methods in Natural Language Processing 2013.

14. Cho, K., van Merrienboer, B., Gülçehre, Ç., Bougares, F., Schwenk, H., and Bengio, Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Conference on Empirical Methods in Natural

Language Processing 2014.

15. Y. Bengio, "A neural probabilistic language model," The Journal of Machine Learning Research, Vol. 3, pp. 1137-1155, 2003.

16. [16] I. Sutskever, O. Vinyals, QV. Le, "Sequence to sequence learning with neural networks," InAdvances in neural

73-77

information processing systems, pp. 3104-3112, 2014. 17. T. Mikolov, "Statistical language models based on neural networks," Presentation at Google, Mountain View, April

2012.

18. LH.Son, A. Allauzen, and Fr. Yvon, "Continuous space translation models with neural networks." Proceedings of the 2012 conference of the north american chapter of the association for computational linguistics: Human language

technologies. Association for Computational Linguistics, pp. 39-48, Jun 2012.

19. M. Sundermeyer, 1. Oparin, JL. Gauvain, B. Freiberg, R. SchlUter, and H. Ney, "Comparison of feed forward and recurrent neural network language models." Acoustics, Speech and Signal Processing (lCASSP), IEEE International

Conference on. IEEE, pp. 8430-8434, May 2013

20. N. Kalchbrenner, and B. Phil, "Recurrent Continuous Translation Models," EMNLP, Vol. 3, p. 413, 2013. 21. M. Auli, M. Galley, C. Quirk, and G. Zweig, "Joint Language and Translation Modeling with Recurrent Neural

Networks," EMNLP.Vol. 3, pp. 1044-54, 2013.

22. K. Cho, B. Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation," In Proceedings of the Empiricial Methods in Natural

Language Processing, pp. 1724-34, Jun. 2014.

23. Pouget-Abadie, D. Bahdanau, B. van Merrienboer, K. Cho, and Y. Bengio, "Overcoming the curse of sentence length for neural machine translation using automatic segmentation," arXiv preprint arXiv:1409.1257. Sep. 2014.

24. H. Schwenk, "Continuous Space Translation Models for Phrase-Based Statistical Machine Translation," COLING (posters), pp. 1071-1080, Dec. 2012.

25. Devlin, R. Zbib, Z. Huang, T. Lamar, RM. Schwartz, and 1. Makhoul, "Fast and Robust Neural Network Joint Models for Statistical Machine Translation," InACL Vol. I , pp. 1370-1380, Jun. 2014.

26. [M. Schuster and KK. Paliwal, "Bidirectional recurrent neural networks," IEEE Transactions on Signal Processing, Vol. 45, pp. 2673-81 , Nov. 1997.

27. M. Sundermeyer, T. Alkhouli, 1. Wuebker, and H. Ney, 'Translation Modeling with Bidirectional Recurrent Neural Networks," InEMNLP 2014, pp. 14-25, Oct. 2014.

28. W. Chen, E. Matusov, S. Khadivi, JT. Peter, "Guided Alignment Training for Topic-Aware Neural Machine Translation," arXiv preprint arXiv:1607.01628, 2016.

29. Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. Maxout networks. In International Conference on Machine Learning, pages 1319– 1327, 2013.

30. Pascanu, R., Gulcehre, C., Cho, K., and Bengio, Y. How to construct deep recurrent neural networks.In Second International Conference on Learning Representations, 2014.

31. Zeiler, M. D. ADADELTA: An adaptive learning rate method. arXiv:1212.5701, 2012. 32. Bojar, Ondřej, Rajen Chatterjee, Christian Federmann, Barry Haddow, Matthias Huck, Chris Hokamp, Philipp Koehn,

Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Carolina Scarton, Lucia Specia, and Marco Turchi. Findings of the 2015 Workshop on Statistical Machine Translation.In Tenth Workshop on Statistical Machine

Translation, September 2015.

33. EmilleCorpus. http://www.lancaster.ac.uk/fass/projects/corpus/emille/. 34. Dakwale, Praveen & Monz, Christof.Convolutional over Recurrent Encoder for Neural Machine Translation.The Prague

Bulletin of Mathematical Linguistics.108. 10.1515/pralin-2017-0007, 2017. 35. Chandramma and Piyush Kumar Pareek.Fast and Accurate Parts of Speech Tagging for Kannada-Telugu

Pair.International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 10 (2018) pp. 7857-

7867, 2018.

16.

Authors: SeshaiahMerikapudi, Dr. Shrishail Math

Paper Title: Video Face Detection Using Bayesian Technique

Abstract:Now a days, security based applications are developed widely and these systems are adopted in

various real-time applications. Visual surveillance is considered as a most promising technique where

certain objects can be detected, tracked and recognized using computer vision based approaches. In this

field, face detection and recognition is considered as the important part of surveillance system. Several

approaches have been developed for face recognition but existing approaches are applied on the face data.

Recently, video face detection techniques are also introduced which provides more information to

improve the security system. In this work, we emphasize on the detection of face, along with tracking and

recognition using computer vision approach. In order to achieve this objective, first of all we utilized face

detection and tracking approach using Kalman filtering. After face detection, we extract the combined

features of the input image and stored the trained data. The learning process is developed using Bayesian

learning approach. The proposed approach is implemented on benchmark datasets such as IARPA Janus

Benchmark A (IJB-A), the YouTube Face repository and the Celebrity-1000 repository. A comparative

performance evaluation is carried out which shows the robust performance of proposed approach.

Index Terms: Bayesian learning, computer vision, face detection, kalman filtering, visual surveillance

References

1. Ranjan, R., Patel, V. M., &Chellappa, R. (2019). Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Transactions on Pattern Analysis and Machine

Intelligence, 41(1), 121-135.

2. Amos, B., Ludwiczuk, B., &Satyanarayanan, M. (2016).Openface: A general-purpose face recognition library with mobile applications.CMU School of Computer Science, 6.

3. Gangopadhyay, I., Chatterjee, A., & Das, I. (2019). Face Detection and Expression Recognition Using Haar Cascade Classifier

and Fisherface Algorithm. In Recent Trends in Signal and Image Processing (pp. 1-11).Springer, Singapore. 4. S. Wu, O. Oreifej, and M. Shah, Action recognition in videos acquired by a moving camera using motion decomposition of

Lagrangian particle trajectories, IEEE International Conference on Computer Vision, (Nov. 2011), 1419-1426.

5. R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, Statistic and knowledge-based moving object detection in traffic scenes, IEEE Intelligent Transportation Systems, (2000), 27-32.

6. E. N. Malamas, E. G. Petrakis, M. Zervakis, L. Petit, and J. D. Legat, A survey on industrial vision systems, applications and

tools, Image and vision computing, 21(2) (2003), 171-188. 7. W. Hu, T. Tan, L. Wang, and S. Maybank, A survey on visual surveillance of object motion and behaviors, IEEE Transactions

on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34(3), (2004), 334-352.

8. J. S. Kim, D. H. Yeom, and Y. H. Joo, Fast and robust algorithm of tracking multiple moving objects for intelligent video surveillance systems, IEEE Transactions on Consumer Electronics, 57(3), (2011), 1165-1170.

9. Wang, W., Shen, J., & Shao, L. (2018).Video salient object detection via fully convolutional networks. IEEE Transactions on

78-85

Image Processing, 27(1), 38-49. 10. Gajjar, V., Gurnani, A., &Khandhediya, Y. (2017). Human detection and tracking for video surveillance: A cognitive science

approach. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2805-2809).

11. Xu, R., Guan, Y., & Huang, Y. (2015). Multiple human detection and tracking based on head detection for real-time video surveillance. Multimedia Tools and Applications, 74(3), 729-742.

12. Javed, S., Mahmood, A., Bouwmans, T., & Jung, S. K. (2018). Spatiotemporal low-rank modeling for complex scene

background initialization. IEEE Transactions on Circuits and Systems for Video Technology, 28(6), 1315-1329. 13. Kulchandani, J. S., &Dangarwala, K. J. (2015, January). Moving object detection: Review of recent research trends. In 2015

International Conference on Pervasive Computing (ICPC) (pp. 1-5).IEEE.

14. Kang, K., Ouyang, W., Li, H., & Wang, X. (2016). Object detection from video tubelets with convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 817-825).

15. Morimitsu, H., Bloch, I., & Cesar-Jr, R. M. (2017).Exploring structure for long-term tracking of multiple objects in sports

videos. Computer Vision and Image Understanding, 159, 89-104. 16. Real, E., Shlens, J., Mazzocchi, S., Pan, X., &Vanhoucke, V. (2017). Youtube-boundingboxes: A large high-precision human-

annotated data set for object detection in video. In Proceedings of the IEEE Conference on Computer Vision and Pattern

Recognition (pp. 5296-5305). 17. Dornaika, F., &Ahlberg, J. (2004). Fast and reliable active appearance model search for 3-D face tracking. IEEE Transactions

on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(4), 1838-1853.

18. J Saragih, R Goecke, Monocular and Stereo Methods for AAM Learning from Video CVPR ‘07. IEEE Conference on Computer Vision and Pattern Recognition, pp:1-8,2007

19. Le, T. H. N., &Savvides, M. (2016). A novel shape constrained feature-based active contour model for lips/mouth

segmentation in the wild. Pattern Recognition, 54, 23-33. 20. Jairath, S., Bharadwaj, S., Vatsa, M., & Singh, R. (2016).Adaptive skin color model to improve video face detection.In

Machine Intelligence and Signal Processing (pp. 131-142).Springer, New Delhi.

21. Dahal, B., Alsadoon, A., Prasad, P. C., &Elchouemi, A. (2016, March). Incorporating skin color for improved face detection

and tracking system. In 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) (pp. 173-176).IEEE.

22. Ren, S., He, K., Girshick, R., & Sun, J. (2015).Faster r-cnn: Towards real-time object detection with region proposal

networks.In Advances in neural information processing systems (pp. 91-99). 23. Cai, Z., &Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE

Conference on Computer Vision and Pattern Recognition (pp. 6154-6162).

24. Jiang, H., & Learned-Miller, E. (2017, May). Face detection with the faster R-CNN. In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (pp. 650-657).IEEE.

25. Fan, Q., Brown, L., & Smith, J. (2016, June).A closer look at Faster R-CNN for vehicle detection.In 2016 IEEE intelligent

vehicles symposium (IV) (pp. 124-129).IEEE. 26. Parkhi, O. M., Vedaldi, A., &Zisserman, A. (2015, September).Deep face recognition.In bmvc (Vol. 1, No. 3, p. 6).

27. Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., & Hua, G. (2017).Neural aggregation network for video face

recognition.In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4362-4371). 28. Ding, C., & Tao, D. (2018). Trunk-branch ensemble convolutional neural networks for video-based face recognition.IEEE

transactions on pattern analysis and machine intelligence, 40(4), 1002-1014. 29. Cao, X., Zhang, C., Zhou, C., Fu, H., &Foroosh, H. (2015).Constrained multi-view video face clustering. IEEE Transactions

on Image Processing, 24(11), 4381-4393.

30. Zheng, J., Ranjan, R., Chen, C. H., Chen, J. C., Castillo, C. D., &Chellappa, R. (2018). An Automatic System for Unconstrained Video-Based Face Recognition.arXiv preprint arXiv:1812.04058.

31. Niu, G., & Chen, Q. (2018). Learning an video frame-based face detection system for security fields. Journal of Visual

Communication and Image Representation, 55, 457-463. 32. Chen, J. C., Ranjan, R., Sankaranarayanan, S., Kumar, A., Chen, C. H., Patel, V. M., ...&Chellappa, R. (2018). Unconstrained

still/video-based face verification with deep convolutional neural networks. International Journal of Computer Vision, 126(2-

4), 272-291. 33. Yang, S., Luo, P., Loy, C. C., & Tang, X. (2018). Faceness-net: Face detection through deep facial part responses. IEEE

transactions on pattern analysis and machine intelligence, 40(8), 1845-1859.

34. B. F. Klare, B. Klein, E. Taborsky, A. Blanton, J. Cheney, K. Allen, P. Grother, A. Mah, M. Burge, and A. K. Jain. Pushing the frontiers of unconstrained face detection and recognition: Iarpajanus benchmark a.In IEEE Conference on Computer

Vision and Pattern Recognition (CVPR), pages 1931–1939, 2015.2, 4, 5, 6.

35. L. Wolf, T. Hassner, and I. Maoz. Face recognition in unconstrained videos with matched background similarity. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 529–534, 2011.

36. L. Liu, L. Zhang, H. Liu, and S. Yan. Toward large population face identification in unconstrained videos. IEEE Transactions

on Circuits and Systems for Video Technology, 24(11):1874–1884, 2014. 37. D. Wang, C. Otto, and A. K. Jain. Face search at scale: 80 million gallery. arXiv preprint arXiv:1507.07242, 2015

38. J.-C.Chen, R. Ranjan, A. Kumar, C.-H.Chen, V. Patel, and R. Chellappa.An end-to-end system for unconstrained face

verification with deep convolutional neural networks. In IEEE International Conference on Computer Vision Work- shops, pages 118–126, 2015.

39. S. Sankaranarayanan, A. Alavi, C. Castillo, and R. Chellappa.Triplet probabilistic embedding for face verification and

clustering.arXiv preprint arXiv:1604.05417, 2016.

40. W. AbdAlmageed, Y. Wu, S. Rawls, S. Harel, T. Hassner, I. Masi, J. Choi, J. Lekust, J. Kim, P. Natarajan, et al. Face

recognition using deep multi-pose representations. In IEEEWinter Conference on Applications of Computer Vision (WACV),

2016. 41. J.-C.Chen, V. M. Patel, and R. Chellappa. Unconstrained face verification using deep cnn features. In IEEE Winter Conference

on Applications of Computer Vision (WACV), 2016.

42. J. Hu, J. Lu, J. Yuan, and Y.-P.Tan.Large margin multimetric learning for face and kinship verification in the wild. In Asian Conference on Computer Vision (ACCV), pages 252–267. 2014

43. J. Hu, J. Lu, and Y.-P.Tan.Discriminative deep metric learning for face verification in the wild. In IEEE Conference on

Computer Vision and Pattern Recognition (CVPR), pages 1875–1882, 2014 44. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf.DeepFace: Closing the gap to human-level performance in face verification.In

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1701–1708, 2014.

45. Y. Sun, X.Wang, and X. Tang. Deeply learned face representations are sparse, selective, and robust. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2892–2900, 2015

46. Y. Wen, K. Zhang, Z. Li, and Y. Qiao.A discriminative feature learning approach for deep face recognition. In European

Conference on Computer Vision (ECCV), pages 499–515, 2016 47. S V N, Murthy, B K, Sujatha, “A Novel Graph-Based Technique to Enhance Video Compression Algorithm” in Emerging

Research in Computing, Information, Communication and Applications, Springer, New Delhi , pp. 463-468.

48. L. Liu, L. Zhang, H. Liu, and S. Yan. Toward largepopulation face identification in unconstrained videos. IEEE Transactions on Circuits and Systems for Video Technology, 24(11):1874–1884, 2014.

49. X.-T. Yuan, X. Liu, and S. Yan. Visual classification with multitask joint sparse representation. IEEE TIP, 21(10):4349–4360,

2012.

17.

Authors: Abdul KhadarDr.Shrishail Math, H. Srinivasa Murthy

Paper Title: Shuffle-Selective-Search Process forMitigation of APTs with IKC

Abstract:Data forensics is a process of recognizing, protecting, recovering, evaluating, and presenting

features of magisterial digital information. This data could lead to sensitive information of an

organization or a person. The APTs are intended to invade the system or environment of this data and try

to be in the environment till the successful theft. Advanced Persistent Threats (APTs) follow the Intrusion

Kill Chain (IKC) to be successful. This paper proposes a prospecting “shuffle-selective-search” dissection

to be inducted in phases of IKC to identify the intrusion-point in the system. Where- in which an effort is

made to identify the APT attack as it follows the IKC, by the Shuffle-Selective-Search dissection when

there is an intrusion at the intrusion-point within the forensic data repository.

Key-words: shuffle-selective-search, APTs, IKC, intrusion-point

References

1. Malicious Data Leak Prevention and Purposeful Evasion Attacks: An Approach to advanced Persistent Threat (APT) Management, Tarique Mustafa, Founder & Chief Executive Officer / Chief Technology Officer, nexTier Networks,

Inc.2953, Bunker Hill Lane, Ste: 400, Santa Clara, CA-95054,USA 2. A Graph Analytic Metric for Mitigating Advanced Persistent Threat John R. Johnson and Emilie A. Hogan 3. Towards a Framework to Detect Multi-Stage Advanced Persistent Threats Attacks, Parth Bhatt, Edgar Toshiro Yano, Dr.

Per M. Gustavsson 4. A Game Model for Predicting the Attack Path of APT, Xupeng Fang, LidongZhai 5. Multi-Agent System for APT Detection,WimMees, ThibaultDebatty, Royal Military AcademyBrussels, Belgium 6. Incorporating the Human Element in Anticipatory and Dynamic Cyber DefenseAunshulRege, Department of Criminal

Justice, Temple University, Philadelphia, USA

7. Moving Target Defense against Advanced PersistentThreats for Cybersecurity Enhancement, MasoudKhosravi-Farmad, Ali AhmadianRamaki and Abbas GhaemiBafghi, Data and Communication Security Lab., Computer Engineering

Department, Ferdowsi University of Mashhad, Mashhad, Iran, m.khosravi@mail.um.ac.ir,

ali.ahmadianramaki@mail.um.ac.ir, ghaemib@um.ac.ir 8. Shuffle-selective search, Abdul KhadarA, ShrishailMath, Srinivasamurthy, Published 2017 9. Analyzing Targeted Attacks using Hadoop applied to Forensic Investigation, Parth Bhatt, Edgar Toshiro Yano, Dept. of

Electronics and Computer Engineering, InstitutoTecnológico de Aeronáutica,São José dos Campos,SP,Brasil, parthbhatt09@gmail.com1, yano@ita.br2

10. https://attack.mitre.org/groups/G0045/ 11. Detection ofvarious denial of service andDistributed Denial of Service attacks using RNN. 12. ensemble, A. B. M. Alim Al Islam ; Tishna Sabrina,2009 12th International Conference on Computersand Information

Technology, Year: 2009

86-89

18.

Authors: Shrihari M.R, Manjunath T.N, Archana R.A, Ravindra S Hegadi

Paper Title: A Key Management of Security to Design Enhanced Apache and Rhino Utilities in Big Data

Using Hadoop

Abstract:Hadoop is a dispersed information dispensation platform intended for investigate big data.

Information is emergent at a massive value in the current charity. Entity of the best part popular

knowledge existing intended for managing and dispensation to facilitate vast quantity of information is

the Hadoop environment. Present be disparate conduct to accumulate and development huge quantity of

information.Hadoop is broadly utilized, lonely of the majority popular strategy to accumulate enormous

quantity of information and progression them in equivalent. at the same time as store insightful

information, security performing an significant responsibility to stay it secure. Security is not that greatly

measured while Hadoop be primarily projected. The early utilize of Hadoop was association huge

quantity of shared network information so privacy of the accumulate information and. essentially user

services in Hadoop be not authenticated; Hadoop is projected code on a disseminated compilation of

technology so exclusive of correct authentication and any person might present and it would be

implement. The outstanding Utilities encompass in progress to extend the protection of Hadoop. These

utilities are using Enhanced Rhino Utility and Enhanced Sentry Utility. Enhanced Rhino develop

splittable crypto codec to deliver encryption intended for the information to facilitate is accumulate in

Hadoop dispersed conspirator organization. Moreover develop the essential authentication by execute

Hadoop single sign on which prevents repeated authentication of the users accessing the same services

with various times. While the authorization point of examination Enhanced Rhino utility deliver

severance based authorization designed for Hbase. utility and Enhanced Sentry utility, in provisions of

encryption, authentication, and authorization.Enhanced Sentry utility give fine-grained entrance organizes

by behind responsibility based authorization which different services can be bound to it to grant

authorization for their users. It is probable to merge security enhancements which cover the Enhanced

Rhino utility and Enhanced Sentry Utility to supporting get enhanced the presentation and offer enhanced

mechanism to secure Hadoop. In this paper, the security of the organization in Hadoop is assess and

different security enhancements to be proposed, enchanting into inspection security enhancement

comprehensive by the two utilities, Enhanced Rhino This paper proposes a number of sophisticated

security improvements on the federal authentication and organization implementation made by enhanced

90-97

Rhino Utility based on the HDFS data encryptionscheme using the ARIA encryption algorithm on

Hadoop.

Index Terms: Big Data,Hadoop, Security, Enhanced Rhino Utility, Enhanced Sentry Utility

References 1. FengXiaorong; JiaShizhun; Mai Songtao,” The research on industri al big data information security risks”,2018,19 – 23. 2. Christos Stergiou; Kostas E. Psannis; TheofanisXifilidis; Andreas P. Plageras; Brij B. Gupta “Security and privacy of big data

for social networking services in cloud”,2018, 438 – 443.

3. Hadeer Mahmoud ,AbdelfatahHegazy, Mohamed H. Khafagy” An approach for Big Data Security based on Hadoop Distributed File system” (ITCE2018),Aswan University,Egypt.

4. Jawwad A. Shamsi; Muhammad Ali Khojaye ” Understanding Privacy Violations in Big Data Systems”, 2018, Volume: 20, Issue: 3, 73 – 81.

5. Youngho Song, Young-Sung Shin, Miyoung Jang, Jae-Woo Chang ”Design and Implementation of HDFS Data Encryption Scheme using ARIA Algorithm on Hadoop”2017.

6. Nikunj Joshi; BintuKadhiwala “Big data security and privacy issues -A survey”, 2017, 1-5. 7. Tarakeswara Rao Balaga,SubbaRao,Reram, Lakshmikanth Pi,“ Hadoop techniques for concise investigation of big data in

multi-format data sets”, 2017, 490 – 495.

8. J ong-Hoon Lee; Young Soo Kim; Jong Hyun Kim; Ik Kyun Kim; Ki-Jun Han,”Building a big data platform for large-scale security data analysis”, 2017, 976 – 980.

9. Hegadi, R.S. et.al, Statistical Data Quality Model for Data Migration Business En terprise.International Journal of Soft Computing, 8: 340-351.DOI: 10.3923/ijscomp.2013.340.351.

10. Manjunath T.N et al, Data Quality Assessment Model for Data Migration Business Enterprise , International Journal of Engineering and Technology (IJET) , ISSN : 0975-4024 Vol 5 No 1 Feb-Mar 2013.

11. R. Behnia, A.A. Yavuz, and M.O. Ozmen, “High-speed high-security public key encryption with keyword search,” 2017. 12. M.R. Shrihari, R.A. Archana, T.N. Manjunath and Ravindra S. Hegadi,” A Review on Different Methods to Protect Big Data

Sets”, 2018,issue-12&page-4.

13. AbidMehmood, IynkaranNatgunanathan, Yong Xiang, Senior Member, IEEE,GuangHua, Member, IEEE, and Song Guo, Senior Member, IEEE”Protection of Big Data Privacy” 2169-3536 (c) 2016 IEEE.

14. Hongbing C, Chunming R, Kai H, Weihong W, Yanyan L. Secure Big Data Storage and Sharing Scheme for Cloud Tenants, China Communications, 2015, pp. 106–115.

15. Wang, H.; Jiang, X.; Kambourakis, G. Special issue on Security, Privacy and Trust in network-based Big Data.Inf. Sci. Int. J. 2015, 318, 48–50.

16. Thuraisingham, B. Big data security and privacy. In Proceedings of the 5th ACM Conference on Data and Application Security and Privacy, San Antonio, TX,USA, 2-4 March 2015; pp. 279–280.

17. Jones, “Hadoop data security and sentry,” 2014.

19.

Authors: Suma S, Bharati Harsoor

Paper Title: An Effective Congestion Control Approach through Route Delay Estimation Using Packet

Loss in Wireless Sensor Network

Abstract:In wireless sensor networks, congestion control and efficient routing are in much needed due to

the increase in the usage of sensor devices in the diverse applications. Even in the randomness in the

mobility of the devices creates dynamic topology which causes delay or packet loss in routing. The

occurrence of congestion mostly due to the high volume of traffic inflow and low outflow of the data

rate.This change in transmission rate results in routing delays and reduced throughput. The rate control is

the major concern in applications performing regular streaming, particularly in wireless networks. This

paper aims to propose an efficient Congestion Control approach through Route Delay Estimation (CC-

RDE) using packet loss to control data rate for effective to minimize the packet loss during congestion.

The CC-RDE mechanism will present an illustration for Delay Recognition (DR) and a method for

Routing Delay Estimation (RDE) to improve the throughput and reduce the routing delay. The experiment

evaluation is performed over a reactive routing protocol to improvise the throughput and reduce delay and

energy consumption in comparison with other protocols

Index Terms: Wireless sensor network, Congestion control, Route Delay estimation

Reference

1. A. Nicolaou, N. Temene, C. Sergiou, C. Georgiou, V. Vassiliou, "Utilizing Mobile Nodes for Congestion Control in

Wireless Sensor Networks", Networking and Internet Architecture, arXiv:1903.08989v1, 2019. 2. Y. Mai, F. M. Rodriguez, N. Wang, "CC-ADOV: An effective multiple paths congestion control AODV", IEEE 8th

Annual Computing and Communication Workshop and Conference (CCWC), Pp. 1000 - 1004, 2018.

3. H. Zare, F. Adibnia, V. Derhami, "A Rate-based Congestion Control Mechanism using Fuzzy Controller in MANETs", International Journal of Computer Communication, 2013.

4. M. Rath, U.P. Rout, N. Pujari, S.K. Nanda, S.P. Panda, "Congestion control mechanism for real-time traffic in mobile ad-hoc networks", In Computer Commun., Netw. and Internet Security, Springer, Singapore, pp. 149-156, 2017.

5. H. K. Molia, A.D. Kothari, "TCP variants for mobile ad-hoc networks: Challenges and solutions", Wireless Pers. Commun. Vol. 1 Pp. 1-46, 2018.

6. M. F. Stewart, R. Kannan, A. Dvir, "CASPaR: congestion avoidance shortest path routing for delay tolerant networks", IEEE international conference on computing, networking and communications, pp.1-5, 2016.

7. P. Sreekumari, S.H. Chung, "TCP NCE: A unified solution for noncongestion events to improve the performance of TCP over wireless networks", EURASIP Journal of Wireless Communication Network, Vol. 23(1), 2017.

8. [8].J. Govindarajan, N. Vibhurani, G. Kousalya, "Enhanced TCP NCE: A modified non-congestion events detection, differentiation and reaction to improve the end-to-end performance over MANET", In Advances in Intelligent Systems

and Computing, vol. 519, pp. 443-454., 2018 9. [9].S. Soundararajan, S. B. Raghuvel, "Multipath load balancing & rate-based congestion control for mobile ad hoc

networks", In IEEE Digital Information and Communication Technology and Applications (DICTAP), pp.30-35, 2012.

98-104

10. [10].S. Cen, P. Cosman, and G. Voelker, "End-to-end differentiation of congestion and wireless losses", IEEE/ACM Trans. Netw., vol. 11, no. 5, pp. 703-717, 2003.

11. [11].M. Coudron, S. Secci, G, Pujolle, "Differentiated pacing on multiple paths to improve one-way delay estimations", IEEE International Sym. on Integrated Network Management (IM), Pgs. 672 - 678, 2015.

12. [12].D. Souza R and Jose J. Routing approach in delay tolerant networks: a survey.International Journal of Comput Appl, Vol. 1(17): 8-14, 2010.

13. [13].M. Ikeda, E. Kulla, M. Hiyama, L. Barolli, M. Younas, M. Takizawa, "TCP Congestion Control in MANETS Traffic Considering Proactive and Reactive Routing Protocols". 15th International Conference on Network-Based

Information Systems, IEEE, 2012.

14. [14].B. Soelistijanto, M. P. Howarth, "Transfer Reliability and Congestion Control Strategies in Opportunistic Networks: A Survey", IEEE Communications Surveys & Tutorials, Vol. 16, Pgs. 538 - 555, 2014.

15. [15].N. Sharma, A. Gupta, S. S. Rajput, V. K. Yadav, "Congestion Control Techniques in MANET: A Survey", IEEE 2nd International Conference on Computational Intelligence & Communication Technology (CICT) Pgs. 280 - 282, 2016.

16. [16].D. C. Dobhal, S. C. Dimri, "Performance evaluation of proposed-TCP in Mobile Ad Hoc Networks (MANETs)", In IEEE International Conference on Inventive Computation Technologies (ICICT), Vol. 2 Pgs. 1 - 6, 2016.

17. [17].J. Wu, C. Yuen, M. Wang, J. Chen, "Content-Aware Concurrent Multipath Transfer for High-Definition Video Streaming over Heterogeneous Wireless Networks", IEEE Transactions on Parallel and Distributed Systems, Vol. 27,

Pgs. 710 - 723, 2016. 18. [18].M. Ikeda, E. Kulla, M. Hiyama, M. Takizawa, "Congestion Control for Multi-flow Traffic in Wireless Mobile Ad-

hoc Networks", IEEE Sixth International Conference on Complex, Intelligent, and Software Intensive Systems, pp. 290 -

297, 2012. 19. [19].Y. R. Yang, M.S. Kim, and S.S. Lam. "Transient behaviors of TCP-friendly congestion control protocols",

Computer Networks, 41(2):193-210, 2003.

20. [20].K. Chen and K. Nahrstedt."Limitations of equation-based congestion control in mobile ad hoc networks", In Distributed Computing Systems Workshops