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Volume-6 Issue-1: Published on March 05, 2016
08
Volume-6 Issue-1: Published on March 05, 2016

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S. No

Volume-6 Issue-1, March 2016, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Vusat Afandiyev, Firangiz Gasimova

Paper Title:

Problems of Urbanization of Azerbaijan

Abstract:  In the article the appearance of cities, urban development, socio-economic development, the concentration of production, population and settlement, based on scientific and technological progress is the result of industrial production, is talking about the problems arising in the process of urbanization. The regulation of agglomeration that is the result of urbanization development in spite of its being of current importance is a poor investigated and difficult-to-resolve problem. The thorough changes which have occurred in the economic, social and political life of the country in the course of last years from the geographical - territorial, industrial point of view are such an important, diversified and dynamic processes that the scientific perception and research on demand of time of the new realities connected with them has turned to a burning problem. The special role of urbanization stems out from its origin of occurrence of urban environment. Social-economic, cultural and demographic settling expands its positions on the historical stages of evolution processes and is developed qualitatively and quantitatively. Integration and planning, town studies from the urbanization viewpoint is a subject of inquiry by different sciences - economy, economic-social geography as well as urban planners. Within the circle of their attraction power and surrounded by unitary areas the towns cause the occurrence of tight reciprocal relationships, integration processes and promotes the vanish of insulation, isolation typical for the past.Nowadays, the increase of city agglomerations and transport-economic relationships, disappearance of major differences between towns and villages promotes economic-social housing in the territorial and regional system as well as migration relationships. Historically, Baku city was placed in area of oil-and-gas fields and has been surrounded with areas of their operation. Now because of an arrangement of Baku city and adjoining regions, oil fields, and also more than 50 densely populated settlements of city type and the working settlements located in an environment of crossed roads, are present very much limited opportunities for its horizontal extensive growth. Such situation in the certain district limits free ground territories for seaside recreational zones and developments of the agricultural industry in the area of city councils of Baku and Sumgait, especially in districts with dense built-on and compactness of communication lines. The mentioned process is conducted outside of intensively-urbanized Baku and Sumgait and has no opportunity for using recreational zones and the ground areas. The primary purpose in this work is conducting a research of activity and interrelation of development of cities, urbanization and the migratory processes connected to it in a certain territorial system from the position of the interconnected and functional system of social and economic, economically - geographical, ecological, housing aspects.The given work which for the first time is devoted to geographical problems of development of the urbanization of cities of Azerbaijan Republic, does not reflect completely multilateral and volumetric problems in the given area.

Keywords:
Settlement; Urbanization; Economic


References:

1.          G.M.Lappo.Urban geography (ЛаппоГ.М. Географиягородов). Moscow, Publishing House of ‘Vlados’.
2.          A.A.Nadirov.Stages of economic development in Azerbaijan Republic (Azərbaycan Respublikasının iqtisadi inkişaf mərhələləri).Baku: Elm. 2002.

3.          V.A.Afandiyev V.A. Sh.G.Damirgayayev. Geographical problems of development of cities in Azerbaijan Republic. (V.A,Əfəndiyev,Ş.Q.Dəmirqayayev. Azərbaycan Respublikası şəhərlərinin inkişafının coğrafi məsələləri). Baku: Nijat. 1995.

4.          V.A.Afandiyev. Characteristic features of the urbanization of Azerbaijan in the modern stage.Journal of the Black Sea Universities Network, V.l. Constanta, Romania. 2002.

5.          V.A.Afandiyev. Urbanization and urban settlements of Azerbaijan. (Əfəndiyev V.A. Urbanizasiya və Azərbaycanın şəhər yaşayış məskənləri). Baku. 2002.

6.          V.A.Afandiyev, F.E.Gasimova. Urban settlement in Azerbaijan. (V.Ə.Əfəndiyev, F.E.Qasımova. Azərbaycanda qəsəbə).  Baku. 2013.


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2.

Authors:

M. Ramakrishna, G. Naresh

Paper Title:

Design of Multi-Machine Power System Stabilizers using Gravitational Search Algorithm

Abstract:   Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system to damp electromechanical oscillations. This paper presents an approach based on the law of gravity and mass interactions called Gravitational Search Algorithm (GSA) fortuning the parameters of PSSs in a multi-machine power system.These stabilizers are tuned simultaneously to shift the lightly damped and undammed electromechanical modes of all plants to a prescribed zone in the s-plane. A multi objective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The performance of the proposed PSS under different disturbances, loading conditions, and system configurations is investigated on New England 10-machine, 39-bus power system. Non-linear time domain simulation results are presented under wide range of operating conditions and disturbances at different locations to show the effectiveness of the proposed GSA based PSS and their ability to provide efficient damping of low frequency oscillations.

Keywords:
  Power System Stabilizer, Electromechanical Oscillations, Gravitational Search Algorithm, Multi-machine Power System.


References:

1.          De Mello, F.P. and Concordia, C. “Concepts of Synchronous Machine Stability as Effected by Excitation Control", IEEE Transactions on Power Apparatus and Systems, Vol. PAS-88, No. 4, April 1969, pp. 316-329.
2.          Larsen, E.V. and Swann, D.A. “Applying Power System Stabilizers, I, II and III", IEEE Transactions on PAS, Vol. 100, No.6, June 1981, pp. 3017-3046.

3.          Y.L.Abdel-Magid, M.A. Abido, S.AI-Baiyat, A.H. Mantawy: Simultaneous stabilization of multi-machine power systems via genetic algorithms. In:  IEEE Transactions on Power Systems, Vol. 14, No. 4, November 1999, pp 1428-1439.

4.          M.A.Abido, Y.L.Abdel-Magid: Eigenvalue Assignments in Multimachine Power System using Tabu search Algorithm. In: Computers and Electrical Engineering 28 (2002) 527-545.

5.          M. A. Abido: Robust Design of Multimachine Power System Stabilizers Using Simulated Annealing. In: IEEE Transactions on Energy Conversion, Vol. 15, No. 3, September 2000, pp 297-304.

6.          Y.L.Abdel-Magid and M.A. Abido “Optimal Multiobjective Design of Robust Power System Stabilizers Using Genetic Algorithms", IEEE Transactions on Power Systems, Vol. 18, No. 3, Aug. 2003, pp. 1125-1132.

7.          M.A.Abido ““Optimal design of Power System Stabilizers Using Particle Swarm Optimization”, IEEE Transactions on Energy    Conversion, Vol. 17, No. 3, September 2002, pp. 406-413.

8.          D.B. Fogel, Evolutionary Computation Towards a New Philosophy of Machine Intelligence, IEEE, New York, 1995.

9.          Rini DP, Shamsuddin SM, Yuhaniz SS. Particle swarm optimization: technique, system and challenges. International Journal of Computer Applications 2011;14(1):19–27.

10.       S.Mishra, M. Tripathy, J. Nanda , “Multi-machine power system stabilizer design by rule based bacteria foraging” , Electric Power      Systems Research 77 (2007) pp. 1595–1607

11.       G.Naresh, M.RamalingaRaju, S.V.L.Narasimham “Design of multi-machine  power system stabilizer using bacterial foraging algorithm” Artificial Intelligence and Machine Learning (AIML)  ICGST-AIML Journal, Volume 11, Issue 2, December 2011, pp.39-48.

12.       G.Naresh,M.RamalingaRaju, M.Sai Krishna ”Design and Parameters Optimization of Multi-machine Power System Stabilizers Using Artificial Bee Colony Algorithm” IEEE 2012 International Conference on Advances in Power Conversion and Energy Conversion (APCET 2012), pp. 160-167

13.       G.Naresh,M.RamalingaRaju, S.V.L.Narasimham “Application of Harmony Search Algorithm for the Robust Design of Power System Stabilizers in Multi-machine Power Systems” Journal of Electrical Engineering (JEE), Romania, Vol.13/2013, Edition 2, Article 13.2.2, pp.9-19.

14.       P.W. Sauer, M.A. Pai, Power System Dynamics and Stability, Englewood Cliffs, Prentice Hall, NJ, 1998.

15.       K.R.Padiyar.:Power System Dynamic Stability and Control, 2nd Edition, BS Publications, 1994.

16.       Norlina MohdSabri, MazidahPuteh, and MohamadRusopMahmood, “AReview of Gravitational Search Algorithm”,Int. J. Advance. Soft Comput. Appl., Vol. 5, No. 3, November 2013, pp. 1-39.

17.       M.A. Pai, Energy Function Analysis for Power System Stability, Kluwer,Norwell, MA, 1989.

18.       A.Bazanella, A.Fischman, A. Silva, J. Dion, and L. Dugrad, “Coordinated robust controllers in power systems,” in Proc. IEEE Stockholm Power Tech Conference, 1995, pp. 256–261..


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3.

Authors:

Fadi N. Al-Ayed, Mohammed F. Al-Haqbani

Paper Title:

Representational State Transform: A Synopsis

Abstract:    This document describes and elaborates Representational State Transform (REST), utilization as well as a perspective towards future. REST is constantly on the achieve momentum since the most effective way intended for constructing web services, allowing numerous web architects to take into consideration regardless of whether and ways to include this strategy into their SOA as well as SOAP-dominated world. This paper discusses some of demystify the web as an application platform and to showcase how web architecture can be applied to common enterprise processing issues. Additionally, the overview of a data platform that is open and accessible to other application, which usually eschews integration in support of composition, nevertheless implements useful business behaviors such as a distributed, and hypermedia-driven system platform.

Keywords:
    Hyper Text Transfer Protocol (HTTP), Service-Oriented Architecture (SOA), Simple Object Access Protocol (SOAP), Uniform Resource Identifier (URI), Uniform Resource Locator (URI)


References:

1.           R.T. Fielding, Architectural Styles and the Design of Network-based Software Architectures, Dissertation, Doctor of Philosophy, University of California, Irvine, 2000.
2.           R. Kay, Quick Study: Representational State Transfer, 2007

3.           J. Webber, S. Parastatidis, and I. Robinson, REST in Practice, O’Reilly Media, Inc, 2010

4.           Dan and et al.: Business-to-Business Integration with TPAML and a Business-to-Business Protocol Framework, IBM System Journal, 2001

5.           Berners-Lee, T., Masinter, L. and Mc Cahill, M. RFC 1738:Uniform Resource Locators (URL). IETF, December 1994.

6.           R. Khare, and R. N. Taylor, Extending the Representational State Transfer (REST) Architectural Style for Decentralized Systems, University of California, Irvine, 2004

7.           Allamaraju, S. (2008). Describing REST ful Applications. Retrieved May 4, 2009, from

8.           http://www.infoq.com/articles/subbu-allamaraju-rest.

9.           Fielding, R. (2008a, March 22). On software architecture »Untangled. Retrieved June 2, 2009, from

10.        http://roy.gbiv.com/untangled/2008/on-software-architecture.


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4.

Authors:

D. Sudha Devi, K. Thilagavathy

Paper Title:

Towards Efficient ECC Based Provable Data Possession Protocol with Data Dynamics for Secure Cloud Storage

Abstract:     Cloud Computing is acquainted for its cost-effective on-demand services based on internet. The significant benefits of cloud services drive organizations and individuals to make hay by outsourcing data to cloud storages. Outsourcing data brings users’ an easy and economical way of data management and also relieves users’ from the burden of building and maintaining local data storage. However, data being residing in some third-party’s premises, users have no full control over their data which necessitates ensuring confidentiality and integrity of data stored in untrusted cloud storage servers. To verify the correctness of data in cloud storage, this paper proposes an efficient ECC based Provable Data Possession (EPDP) Protocol with data dynamics. The proposed protocol preserves confidentiality of data stored in cloud storage and allows data owner to verify the integrity of data without retrieving the whole original data. Also the protocol is designed to perform stateless auditing and supports data dynamics at block level retaining the same security assurance. Security and Performance analysis proves the proposed protocol to be secure and highly efficient for secure cloud storage.

Keywords:
    confidentiality, cloud data storage, data integrity, elliptic curve cryptography, integrity verification, secure storage


References:

1.              P.Mell and T.Grance,“Draft NIST Working Definition of Cloud Computig”[Online]. Available: http://csrc.nist.gov/groups/SNS/cloudcomputing/index.html, 2012.
2.              Amazon Web Services (AWS), [Online]. Available: http://aws.amazon.com/ec2.

3.              Google AppEngine, [Online]. Available: http://googcloudlabs.appspot.com.

4.              Rackspace Cloud sites, [Online]. Available: http://www.rackspace.com/cloud/sites.

5.              Microsoft Azure, [Online]. Available: https://azure.microsoft.com/en-in.

6.              Salesforce, [Online]. Available: http://www.salesforce.com/in.

7.              VMware vCloud, [Online]. Available: http://vcloud.vmware.com.

8.              Verizon, [Online]. Available: http://www.verizonwireless.com/support/verizon-cloud.

9.              Citrix Cloud Services, [Online]. Available: https://www.citrix.com/solutions/cloud-services.

10.           IBM Cloud, [Online]. Available: http://www.ibm.com/cloud-computing.

11.           D. Sudhadevi and K. Thilagavathy, “A novel approach to enhance cloud data defense,” Asian Journal of Information Technology, Vol. 12, No. 9, 2013, pp. 305–311.

12.           D. Sudha Devi, K. Thilagavathy, “An Elliptic Curve Cryptography based adaptive and secure protocol to access data outsourced to cloud server”, International Journal of Applied Engineering Research, Vol. 10, No. 18, 2015, pp 39443-39450.

13.           H. Shacham and B.Waters, “Compact Proofs of  Retrievability”, Proc.14th International Conference Theory and Application of Cryptology and Information Security: Advances in Cryptology (ASIACRYPT), LNCS 5350,2008, pp.90-107.

14.           C. Erway, A. Kupcu, C. Papamanthou, R. Tamassia, “Dynamic provable data possession”, In: Proceedings of the 16th ACM conference on computer and communications security, CCS 2009, pp. 213–22.

15.           G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson,and D. Song, “Remote Data Checking using Provable Data Possession”, ACM Transactions on Information and System Security,Vol. 14, No. 1, 2011, pp. 12.1–12.34.

16.           Q. Wang, C. Wang, K. Ren W. Lou, and J. Li, “Enabling  public verifiability and data dynamics for storage security in cloud computing,” IEEE Transaction on Parallel and Distributed Computing, Vol.22, No.5, 2011, pp.847-859.

17.           Z. Hao, S. Zhong, N. Yu. “A privacy-preserving remote data integrity checking protocol with data dynamics and public verifiability”, IEEE Transaction on Knowledge Data Engineering, Vol.23, Issue. 9, 2011, pp.1432-1437.

18.           M. van Dijk, A. Juels, A. Oprea, R. L. Rivest, E. Stefanov, and N. Triandopoulos, “Hourglass schemes: how to prove that cloud files are encrypted” In Proceedings of the 2012 ACM conference on Computer and communications security,  2012,pp. 265–280.

19.           Q. Zheng, S. Xu,“Secure and efficient proof of storage with deduplication”, In: Proceedings of the second ACM conference on data and application security and privacy, CODASPY 2012,pp. 1–12.

20.           P. Williams and R. Sion, “Single round access privacy on outsourced storage”, In Proceedings of the 2012 ACM conference on Computer and communications security, 2012, pp. 293–304.

21.           C. Wang, Q. Wang, K. Ren, N. cao and W. Lou , “Towards Secure and Dependable Storage Services in Cloud Computing”, IEEE Transaction on Cloud Computing, Vol.5, Issue.2, 2012, 220-232.

22.           Y. Zhu, H. Hu, G. Ahn and M. Yu, “Cooperative Provable Data Possession for Integrity Verification in Multicloud Storage”, In Proceedings of IEEE Transactions on Parallel Distributed Systems, 2012, pp. 2231-2244.

23.           Y. Ren, J. Xu, J. Wang and J. Kim, “Designated-Verifier Provable Data Possession in Public Cloud Storage”,International Journal of Security and Its Applications, Vol.7, No.6, 2013, pp.11-20.

24.           D. Koo, J. Hur, H. Yoon,“Secure and efficient data retrieval over encrypted data using attribute-based encryption in cloud storage”,Computers and Electrical Engineering, 39 (2013), pp. 34–46.

25.           G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, “Provable data possession at untrusted stores”, In: Proceedings of the 14th ACM conference on computer and communications security, 2007, pp. 598–609.

26.           C. Wang, Q. Wang, K. Ren, W. Lou,” Privacy-preserving public auditing for data storage security in cloud computing”,IEEE proceedings, INFOCOM, 2010,pp. 1–9.     

27.           J. Yang, H. Wang, J. Wang, C. Tan and D. Yu, “Provable Data Possession of Resource-constrained Mobile Devices in Cloud Computing”, Journal of Networks, 
28.           Y. Zhu, H. Hu, G. Ahn, S. Yau, “Efficient audit service outsourcing for data integrity in clouds”, The Journal of Systems and Software, 85 (2012), pp. 1083– 1095.
29.           P.Natu, S.Pachouly, “A Comparative Analysis of Provable Data Possession Schemes in Cloud”, International Journal of Computer Science and Information Technologies, Vol. 5, No.6 , 2014, pp.7927-7931.

30.           H. Liu, P. Zhang, J. Liu, “Public Data Integrity Verification for SecureCloud Storage”, Journal of Networks, Vol.8, No.2, 2013, pp.373-380.

31.           S.Worku, C. Xu, J. Zhao, X. He, “Secure and efficient privacy-preserving public auditing scheme for cloud storage”, Computers and Electrical Engineering, 40 (2014), pp. 1703–1713.

32.           M. Vanitha, T. Jayapratha, K. Subramani, T. Pradeepa, “Elliptic Curve Cryptography Digital Signature Algorithm For Privacy-Preserving Public Auditing For Shared Data In The Cloud”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 3 Issue. 3, pp.1497 – 1502.

33.           D. SudhaDeviand K. Thilagavathy, “An Adaptive Multilevel Security Framework for the Data Stored in Cloud Environment,” The Scientific World Journal, (2015), pp. 1-11.

34.           A.J. Menezes, V. Oorschot, and S.A. Vanstone, “Handbook of Applied Cryptography”, CRC Press Series on Discrete Mathematics and its Applications, CRC Press, Boca Raton, FL, 1997, pp. 320- 383.

35.           N. Koblitz, A. Menezes, and S.A. Vanstone, “The state of elliptic curve cryptography”, Designs, Codes and Cryptography (19), 2000,  pp. 173-193.

36.           D. Hankerson, A.J. Menezes, and S. Vanstone, “Guide to Elliptic Curve Cryptography”, Springer-Verlag, New York, USA, 2004, pp.78-80.

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5.

Authors:

Ahmed Saeed Obied, Hind Mowafaq Taha

Paper Title:

Noise Cancellation using NLMS Adaptive Filter

Abstract:    This paper presents the simulation of noise canceller system which contains adaptive filter and using adaptive algorithm. The objective of noise cancellation is to produce the estimate of the noise signal and to subtract it from the noisy signal and hence to obtain noise free signal. This work basically focuses on using NLMS filter in noise cancellation. We used Matlab to simulate our adaptive filter.

Keywords:
 Noise canceller; adaptive algorithm; NLMS


References:

1.             Emmanuel C.ifehor, Barrie W.jervis “Digital Signal Processing A Practical Approach” II edition, Pearson Education. ISBN81-7808 609-3.
2.             Bernard widrow, Samuel D.Stearns.” Adaptive Signal Processing” third Impression, Pearson Education. 0532-2, (2009).

3.             A.Bhavani Sankar; D.Kumar; K.Seethalakshmi “Performance Study of Various Adaptive filter algorithms for Noise Cancellation in Respiratory Signals” Signal Processing:  An International Journal   (SPIJ) Volume:  4    Issue:  5 Pages:  247-303 December 2010.

4.             Md Zia Ur Rahman, ; Sk. Khaja Mohedden, ; Dr. B V Rama Mohana Rao ; Y. Jaipal Reddy ; G.V.S. Karthik “Filtering Non-Stationary Noise in Speech Signals using Computationally Efficient Unbiased and  Normalized Algorithm” International Journal on Computer Science and Engineering Volume:  3    Issue:  3  Pages:  247-303 December 2011.

5.             M. Z. U. Rahman, S. R. Ahamed and D. V. R. K. Reddy, “Noise Cancellation in ECG Signals using Computationally Simplified Adaptive Filtering Techniques: Application to Biotelemetry”, Signal Processing: An International Journal, CSC Journals, Volume 3, Issue 5, pp. 1-12, November 2009.

6.             Z. Chen “Design and Implementation on a Sub-band based Acoustic  Echo Cancellation Approach” IJACSA International Journal of Advanced Computer Science and Applications (2011) , Volume  2, Issue 6, pp. 43-48, 2009.

7.             S. Haykin, Adaptive Filter Theory, 3rd ed., Upper Saddle River, New Jersy: Prentice Hall, 1996.

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6.

Authors:

K. K. Myithili, R. Parvathi

Paper Title:

Chromatic Values of Intuitionistic FuzzyDirected Hypergraph Colorings

Abstract:     A hypergraph is a set V of vertices and a set E of non-empty subsets of V, called hyperedges. Unlike graphs, hypergraphs can perform higher-order interactions in social and communication networks. Directed hypergraphs are much like directed graphs. Colors are used to distinguish the classes. Coloring a hypergraph H must assign atleast two different colors to the vertices of every hyperedge. That is, no edge is monochromatic. In this paper, upper and lower truncation, core aggregate of intuitionistic fuzzy directed hypergraph (IFDHG), conservative K-coloring of IFDHG, chromatic values of intuitionistic fuzzy colorings, elementary center of intuitionistic fuzzy coloring, f-chromatic value of intuitionistic fuzzy coloring, intersecting IFDHG, K-intersecting IFDHG, strongly intersecting IFDHG were studied. Also it has been proved that IFDHG H is strongly intersecting if and only if it is K-intersecting.

Keyords:
   Core aggregate of IFDHG, intuitionistic fuzzy colorings (IFC), elementary center, f -chromatic value of IFC, intersecting IFDHG, K-intersecting, strongly intersecting IFDHG.


References:

1.          Atanassov. K. T, Intuitionistic fuzzy sets - Theory and      Applications, New York, Physica-verlag, Berlin(1999).
2.          Berge .C, Graphs and Hypergraphs, North-Holland, NewYork,      1976.

3.          Mordeson N. John, Nair S. Premchand, Fuzzy graphs and fuzzy     hypergraphs, New York, Physica-verlag, (2000).

4.          Parvathi. R and Karunambigai. M. G, Intuitionistic fuzzy     graphs, Proceedings of 9th Fuzzy Days International Conference      on Computational Intelligence, Advances in soft computing:      Computational Intelligence, Theory and Applications, Springer-      Verlag, 20(2006), 139-150.

5.          Parvathi. R, Thilagavathi. S and Karunambigai. M. G,       Intuitionistic fuzzy hypergraph, Bulgarian Academy of Sciences, Cybernetics and Information Techonoliges, Vol.9 (2009) , No.2,46-53.

6.          Parvathi. R, Thilagavathi. S , Intuitionistic fuzzy directed  hypergraphs, Advances in Fuzzy Sets and Systems 14(1), 2013, 39-52.

7.          Rosenfeld. A, Fuzzy graphs, Fuzzy sets and their applications, L.A.Zadeh, K.S.Fu and M.Shimura Eds., Academic Press,  NewYork, 1975, 77-95.

8.          Roy H. Goetschel Jr., Introduction to fuzzy hypergraphs and hebbian structures, Fuzzy Sets and Systems, 76 (1995), 113-130.

9.          Roy H. Goetschel Jr., William L.Craine and William Voxman       Fuzzy transversals of fuzzy hypergraphs, Fuzzy Sets and        Systems, 84 (1996), 235-254.

10.       Roy H. Goetschel Jr., Fuzzy colorings of fuzzy hypergraphs,        Fuzzy Sets and Systems, 94 (1998), 185-204.

11.       Myithili. K. K, Parvathi. R, Akram. M, Certain types of         intuitionistic fuzzy directed hypergraphs, International Journal of Cybernatics and Machine Learning,             http://link.springer.com/article/10.1007/s13042-014-0253-1 (2014), 1-9.

12.       L.A.Zadeh, Fuzzy sets, Information and Control, 8, (1965), 338-353.


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7.

Authors:

Cyrus BabuOng`ondo

Paper Title:

Investigating Pre-Construction Planning in the Construction Industry of Kenya: Practitioners Perspective

Abstract:      The subject of pre-construction planning is central to the success of project controls and performance during the implementation of construction projects. However, projects have been failing pointing to the need to relook at the planning for projects implementation. In the construction industry of Kenya, performances challenges during projects implementation is a chronic problem. Projects do not achieve their planned cost, time and quality objectives among other performance measures. This study sought to investigate pre-construction planning in the construction industry of Kenya, with emphasis on its adequacy. This cross-sectional research adopted a mixed-method design consisting of analysis of a questionnaire survey administered to active 95No. (NCA1, NCA2 and NCA3) contractors selected by way of stratified random sampling. A similar approach was also used to select 92No.Consultants with a response rate of 54.73% and 46.73% respectively. In addition, 11No.practitioners wereinterviewed in the currentstudy. Data analysis techniques employed include descriptive statistics and thematic analysis.The study established five (5No.) issues that need to be given careful attention when planning for projects implementation. The issues in order of importance include; Clarity of Scope Statement (RII=0.896), Clarity of performance benchmarks (RII=0.865), Competency of the project team (RII=0.0.682), Clarity of roles definition (RII=0.764) and Contractors selection criteria (RII=0.726). The study concludes by compiling views of the practitioners on what they consider good practice in improving the pre-construction planning practice. The study recommends the use of the good-practice checklist developed for better projects performance.

Keywords:
    Pre-construction planning, construction industry, good-Practice checklist, Kenya .


References:

1.             Abd El-Razek, M. (2008). Causes of Delay in Building Construction Projects In Egypt. Journal of Construction Engineering & Management, , 134(11),831-841.
2.             Akintoye, A. (2007). Collaborative relationships in construction-The UK contractor`s perception. Engineering,Construction and Architectural Management.

3.             Atkinson, R. (1999). Project management: Cost,time and quality,two best guesses and a phenomena.its time to accept other success criteria. International Journal of Project Management, Vol 17,Issue 6 December,1999,Pages 337-342..

4.             Chandara, P. (2002). Projects Planning,Financing,Implementation and Review. Tata: McGraw-Hill Publishing Company.

5.             Chitkara, K. (2002). Construction Project Management Planning,Scheduling and Control. Hill Publishing Company Ltd.

6.             Christenson, D. (2008). Using vision as a critical Success element in Project Management. International Journal of Project Management.

7.             Cooke, B. W. (2004). Construction Planning Programming and Control. Oxford: Blackwell Publishing.

8.             Cooke-Davies, T. (2002). "The real success factors on projects. International Journal of Project Management.

9.             Egbu, C. (1998). "Planning and Control processes and techinques for refurbishment management.". Construction Management and Economics, 16(3),315-325.

10.          Fena-Mora, F. (2001). Dynamic Planning and control methodologyfor design/build fast-track construction projects. Journal of Construction Engineering and Management, 127(1),1-17.

11.          Floyd, L. (2004). " Application of appropriate control tools for contract type". Cost Engineering, 46(2),25-30.

12.          Forsythe, P. (2008). Modelling customer perceived quality in housing. International journal of project management,Elsevier Science Ltd and IPMA.

13.          Fortune, J. (2006). Framing of critical success factors by a systems model. International Journal of Project Management,Elsevier Science Ltd and IPMA.

14.          Frimpong, Y. (2003). Delay and cost overruns in Construction of Ground water Projects in developing countries. International Journal of Project Management, 21,321-326.

15.          George, R. (2008). Critical activities in front End planning process. Journal of Management of Engineering.

16.          Gichunge, H. (2000). Risk management in the Building Industry in Kenya. Unpublished PHD.Thesis.University of Nairobi.

17.          Goodman, L. (1988). Project Planning and Management-an integrated system for improving productivity. New York: Van Nostrand Reinhold Company Inc.

18.          Greer, M. (1999). Handbook of Human performance Technology. San Francisco: Jossey-Bas.

19.          Gwaya, A. (2014). Development of appropriate project management factors for the construction industry in Kenya. International Journal of Soft Computing and Engineering (IJSCE), ISSN:2231-2307,Vol 4,Issue 1.

20.          Hendrickson. (1999). Causes of Delay in Construction. Journal of Construction Engineering and Management, Vol 134,issue 11,p831.

21.          Hillebrandt, P. (2000). Economic theory and the construction Industry.3rd Edition. London: Macmillan.

22.          Iyer, K. J. (2005). Factors affecting cost performance evidence from indian construction projects. International journal of project management,, 23 (4),283-295.

23.          Jackson, B. (2004). Construction Management Jump Start. CA: Sybex Incorporated Alaneda.

24.          Johnson, G. (2006). Exploring Corporate Strategy 7th Edition. London: Pearson Education.

25.          Kagiri, N. (2005). Time and Cost overrun in Power projects in Kenya: A case study of Kenya Electricity Generating Company Ltd. Unpublished MBA Thesis.University of Nairobi.

26.          Kaming, P. (1997). Factors Influencing Construction Time and Cost Overruns on High-Rise Projects in Indonesia. Journal of Construction Management and Economics, 7,83-94.

27.          Kenny, C. (2007). Construction,Corruption and developing countries. World Bank policy Research working paper.

28.          Kerzner, H. (2006). Project Management: A systems Approach to Planning,Scheduling and Controlling 9th Edition. John Wiley & Sons publications.

29.          Kongere, N. S. (2010). Project Management,From Design to Implementation. Nairobi: Richmond Designers and Printers.

30.          Lester, A. (2000). Project Planning and Control. Oxford: Butterworth Heinemann.

31.          Lindahl, G. (2007). Client`s goals and the Construction Project Management Process. Journal of Construction Management and Economics.

32.          Ling, F. (2009). How Project Managers can better control the performance of design build projects. International Journal of Project Management, 22(6),477-488.

33.          Masu, S. (2006). An investigation into the causes and impact of resource mix practices in the performance of construction firms in Kenya. Nairobi: Unpublished Phd.Thesis.University of Nairobi.

34.          Morris, S. (1990). Cost and Time Overruns in Public Sector Projects.

35.          Muchungu, P. (2012). The contribution of human factors in the performance of construction projects in kenya. Nairobi: Unpublished Phd.Thesis.University of Nairobi.

36.          Munano, A. (2012). Pre-constrcution Planning: Exploring the factors that influence timelines of project completion for public sectors buildings in Kenya.
Unpublished Master of Construction Management Thesis.Jomo Kenyatta University.

37.          Musa, G. (1999). Determination of Factors Influencing Projects Delays in Water Projects in Kenya: The case of Government Funded Projects. Nairobi: Unpublished MBA Thesis University of Nairobi.

38.          Mwandali, D. (1996). Analysis of Major Factors that affect Projects Management: A Case of Kenya Railways Projects. Nairobi: Unpublished MBA Thesis,University of Nairobi.

39.          Nguyen, A. (2004). A study on Project success factors in large construction projects in Vietnam.

40.          Nicholas, J. (2001). Project Management for Business and Technology. New Jersey: Prentice Hall.

41.          Olawale, Y. a. (2010). "Cost and time control of construction projects: Inhibitng factors and mitigating measures in practice". Construction Management and Economics, 28 (5),509-526.

42.          Pellicer, E. (2005). Cost control in Consulting engineering firms. Journal of Management in Engineering, 21 (4),189-192.

43.          Project Management Institute. (2013). PMBOK: A guide to the Project Management Book of Knowledge. Project Management Institute.

44.          Rozenes, S. (2006). "Project Control: Literature review". Project Management Journal, 37(4) 4-14.

45.          Samuelson, W. (2006). Managerial Economics.5th Edition. New Jersey: John Wiley & Sons.

46.          Talukhaba, A. (1998). Time and Cost Performance of Construction Projects. Nairobi: Unpublished M.A.Thesis,University of Nairobi.

47.          Tucker, L. A. (1987). Is Construction Project planning really doing its job?.A critical focus,role and progress in the construction management economic. Vol 5,243-266.

48.          Wanyona, G. (2005). Risk Managment in the cost planning and control of building projects.The case of quantity Surveying profession in Kenya. Unpublished PhD Thesis.University of Cape Town.

49.          White, D. F. (2002). Current practice in project management-An Emperical study. International Journal of Project Management, 20(2),1-11.

50.          Yakubu, O. a. (2009). Cost and time control of construction projects: A survey of Contractors and Consultants. Construction Information Quarterly, , 11(2),53-59.

51.          Zhen Yu, Z. (2010). Application of innovative Critical Chain Method for project planning and control. Journal of Construction Engineering and Management.


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8.

Authors:

Pavan Kumar K, Daniel Raju Paga, V. Bagyaveereswaran

Paper Title:

I2C Master Protocol Implementation in VHDL for Data Acquisition From CDC

Abstract:   Communication between devices plays an important role in data acquisition. Proper selection of communication method or protocols is an important task. For any process, hardware and software components are used to perform many tasks like controlling the process and actuators, data conversion, data acquisition etc. A large amount of hardware or software components cannot be used for data acquisition. A proper and efficient approach is intended to be made such that they are used in least possible numbers, also keeping in mind that the different existing devices are compatible without any major modifications. This paper presents an approach to acquire data through I2C protocol in an efficient way. The AD7746 which is capacitance to digital convertor uses I2C protocol for providing the digital data. The digitized information is acquired from the AD7746 by a FPGA which is designed using modular flow method.

Keywords:
     AD7746, FPGA, I2C protocol, Master, FSM, Serialdata communication..


References:
1.           Designing Embedded Systems with PIC Microcontrollers Principles and applications by Tim Wilmshurst Amsterdam, Newnespublications  2nd edition, 2009
2.           Digital Design Principles and Practices 4th Edition by John F Wakerly,  Prentice Hall publication, 2005

3.           AD7746 data sheet by Analog Devices

4.           HDL Programming VHDLand Verilog  byNazeih M Botros,  Charles River Media, 2005

5.           Circuit Design and Simulation with VHDL second edition Volnei A. Pedroni, The MIT press publication, 2010

6.           AN10216-01 I2C Manual, Philips Semiconductor, Mar 2003

7.           I2C Bus Specification, Philips Semiconductor, version 2.1, January 2000.
8.           Model sim SE user manual by model technology.
9.           Design and implementation of FPGA based interface model for scale-free network using I2C bus protocol on Quartus II 6.0 by VenkateswaranP.,Dept. of Electron. & Tele-Commun. Eng., JadavpurUniv.,Kolkata,India, Mukherjee M.; SanyalA.;Das S., Computers and Devices for communication ,2009 .CODEC 2009.

10.        Design and implementation of a BIST embedded inter-integrated circuit bus protocol over FPGA by Saha, S. ; Dept. of Electron. &Commun. Eng., Khulna Univ. of Eng. & Technol., Khulna, Bangladesh ; Rahman, M.A. ; Thakur, A., Electrical Information and Communication Technology, (EICT) 2013.

11.        A review on effectuation of serial communication Inter-IC Protocol by Ashwin Bhandekar, S.S.Thakare, D.S.Chaudhar, International Journal of Advanced Research in Computer Science and Software Engineering ,Volume 3, Issue 3, March 2013.

12.        Design and Implementation of I2C Master Controller Interfaced with RAM Using VHDL by Sansar Chand Sankhyan, Research Article, International Journal of Engineering Research and Applications, Vol.4, Issue 7, July 2014, pp.67-70.

13.        Designing of Inter Integrated Circuit using Verilog by Disha Malik, International Journal of Science, Engineering and Technology, Volume 02, Issue 6, July 2014. 

14.        Design and Simulation of I2C Protocol by Tripti Singh, International Journal for Research in Applied Science and Engineering Technology(IJRASET) , Volume 2, Issue XII, December 2014.

15.        Design and Implementation of I2C Bus Controller using Verilog by Mr. J.J.Patel, Prof. B.H.Soni, Journal of Information ,Knowledge and Research in Electronics and Communication Engineering, Vol. 2, Issue 2, November 12 to October 13.


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9.

Authors:

G. Thamizhendhi, R. Parvathi

Paper Title:

Intuitionistic Fuzzy Tree Center-Based Clustering Algorithm

Abstract:    In this paper, the concepts of distance, eccentricity, radius, diameter and center of an intu-itionistic fuzzy tree are defined. Some of the domination parameters like independent domina-tion, connected domination and total domination on intuitionistic fuzzy trees are investigated. The procedure for intuitionistic fuzzification for numerical data set is proposed. Further, in-tuitionistic fuzzy tree center-based clustering algorithm is designed. The e ectiveness of the algorithm is checked with a numerical dataset and compared with two existing clustering meth-ods.

Keywords:
 Intuitionistic fuzzy tree, distance, eccentricity, center, connected domination, connec-tivity, clustering.


References:

1.             K. Atanassov, Intuitionistic fuzzy sets: Theory and applications, Physica-Verlag, New York (1999).
2.             K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1) (1986), 87-96.

3.             M.Akram and N.O. Alshehri, Intuitionistic fuzzy cycles and intuitionistic fuzzy trees, The Scientific Word Journal, 1(2014), 1-11.

4.             M.Akram and B.Dawaz, Strong Intuitionistic fuzzy graphs, FILOMAT, 26(1)(2012), 177-196.

5.             M.Akram and W.A.Dudek, Intuitionistic fuzzy hypergraphs with applications, Information Sci-ences, 2(18)(2013), 182-193.

6.             R. Cai, Y.J. Lei and X.J. Zhao, Clustering method based on intuitionistic fuzzy equivalent dissimilarity matrix, Journal of Computer Applications 29, (2009), 123 - 126.

7.             P. Chountas, A. Shannon, R.Parvathi and K.Atanassov, On intuitionistic fuzzy trees and their index matrix interpretation, Notes on Intuitionistic Fuzzy Sets, 15(4)(2009),52-56.

8.             Y. H.Dong, Y. T.Zhuang, K.Chen and X. Y.Tai, (2006)A hierarchical clustering algorithm based on fuzzy graph connectedness, Fuzzy Sets and Systems, 157(11) (2006), 1760-1774.

9.             M. G. Karunambigai and R. Parvathi, Intuitionistic fuzzy graphs, Proceedings of 9thFuzzy Days International Conference on Computational Intelligence, Advances in soft computing: Computational Intelligence, Theory and Applications, Springer-Verlag, 20 (2006), 139-150.

10.          John N.Mordeson and Y.Y.Yao, Fuzzy cycles and fuzzy trees, The Journal of Fuzzy Mathe-matics, 10 (1) (2002), 189 - 201.

11.          R. Parvathi and G. Thamizhendhi, Domination in intuitionistic fuzzy graphs, Notes on Intu-itionistic Fuzzy Sets, 12 (2010), 39-49.

12.          R. Parvathi, and S. Thilagavathi, Intuitionistic fuzzy directed hypergraphs, Advances in fuzzy Sets and System, 14 (1) (2013), 39-52.

13.          R. Parvathi, S. Thilagavathi, G. Thamizhendhi and M. G. Karunambigai, Index matrix rep-resentation of intuitionistic fuzzy graphs, Notes on Intuitionistic Fuzzy Sets, 20 (2) (2014), 100-108.

14.          R. Parvathi and G.Thamizhendhi, Some results on domination number in products of intu-itionistic fuzzy graphs, Annals of Fuzzy Mathematics and Informatics 9 (3) ( 2015), 403-419.

15.          A.Rosenfeld, Fuzzy graphs.In: fuzzy sets and their applications(L. A. Zadeh, K. S. Fu and M. Shimura, eds.), Academic Press, New York, 1975, 77-95.

16.          Somasundram and S. Somasundaram, Domination in fuzzy Graphs-I, Pattern Recognition Letters, 19 (1998), 787-791.

17.          G. Thamizhendhi and R. Parvathi, Some domination parameters of intuitionistic fuzzy graphs, Far East Journal of Mathematical Sciences, 55(1) (2011), 65-74.

18.          Z.S. Xu and R.R. Yager, Some geometric aggregation operators based on intuitionistic fuzzy sets, International Journal of General Systems, 35, (2006), 417 - 433.

19.          L.A. Zadeh, Fuzzy sets, Information and control, 8(3)(1965), 338-353.

20.          Zeshui Xu, Jian Chen, Junjie Wu, Clustering Algorthim for intuitionistic fuzzy sets, Informa-tion Sciences, 178 (19) (178)(2008), 3755-3790.

21.          H.Zhao, Zeshui Xu, Shousheng Liu and Zhong Wang, intuitionistic fuzzy MST clustering al-gorithms, Computers and Industrial Engineering, (2012), 1130-1140.

22.          C. T.Zahn, Graph-theoretical methods for detecting and describing gestalt clusters, IEEE Trans-actions on Computers, 20 (1)(1971), 68-86.

23.          H. M. Zhang and Z.S.Q. Chen, On clustering approach to intuitionistic fuzzy sets, Control and Decision 22, (2007), 882 - 888.

24.          Zhong Wang, Zeshui Xu, Shousheng Liu and Jian Tang, A netting clustering analysis method under intuitionistic fuzzy environment, Applied Soft Computing, (11),(2011), 5558 - 5564.

25.          http://www.uni-koeln.de/themen/statistik/data/cluster/


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10.

Authors:

Mukesh Kumar Dey

Paper Title:

Improved Design of Obstacle Avoidance Robot using three Ultrasonic Sensors and ATMEGA328P Microcontroller

Abstract:     This paper proposes an alternative design for a cost effective and simplified version of the obstacle avoidance robot using three ultrasonic sensors. It also provides a dynamic algorithm which directs the robot to navigate smoothly in different environments, avoiding obstacles. The main objective of this paper is to improve the accuracy of the robot in detection and avoidance of obstacles at various angles. The paper talks about the design of the robot which is followed by the working principle of sensors and algorithm for the microcontroller. The paper finally makes a detailed explanation of the code for the obstacle avoidance strategy and draws a conclusion based on the designed robot.

Keywords:
 ATMEGA328P, Binary Logic, HC-SR04, Dynamic algorithm..


References:

1.          Engelberger, J., Transitions Research Corporation, private communication, 1986.
2.          Borenstein, J. and Koren, Y., "Obstacle Avoidance With Ultrasonic Sensors." IEEE Journal of Robotics and Automation. Vol. RA-4, No. 2, 1988, pp. 213-218.

3.          Borenstein, J., "The Nursing Robot System." Ph. D. Thesis, Technion, Haifa, Israel, 1987.

4.          Vivek Hanumante et al., “Low Cost Obstacle Avoidance Robot”, IJSCE, ISSN: 2231-2307, Volume-3, Issue-4, September 2013.

5.          Paul Kinsky and Quan Zhou, “Obstacle Avoidance Robot.” Major Qualifying Project Report, WORCESTER POLYTECHNIC INSTITUTE, Project Number: YR-11E1.

6.          Elec Freaks, Ultrasonic Ranging Module HC - SR04. Available:   http://www.micropik.com/PDF/HCSRO4.pdf (Last Accessed on 16th February, 2016).
7.          Adafruit Motor Shield – Arduino Playground. Available: http://www.playground.arduino.cc/Main/AdafruitMotorShield (Last Accessed on 31st January, 2016).
8.          Jnaneshwar Das, Mrinal Kalakrishnan and Sharad Nagappa, “Obstacle Avoidance for a Mobile Exploration Robot with Onboard Embedded Ultrasonic Range
Sensor”, PES Institute of Technology, Bangalore.


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11.

Authors:

Amit R. Khaparde, M. M. Raghuwanshi, L. G. Malik

Paper Title:

Industrial Application of Differential Evolution Algorithm

Abstract:    Here, we presented, the use of differential evolution algorithm in industrial applications. The differential evolution algorithm is widely used to solve direct, continuous space optimizations problems. The application of DE is present in almost all fields of engineering problems. One real life problem, from the field of electronic engineering name frequency modulation for sound waves synthesis is used to show the efficiency of differential evolution algorithm. The results are further evaluated on the parameter name success rate and success performance , they shows that the DE have very fast convergence rate and having ability to solve the given problem

Keywords:
 Evolutionary algorithm, sound waves, multi-model problem 


References:
1.             R. Storn , K. Price “Differential Evolution – Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces” Journal of Global Optimization 11: 341–359, 1997.
2.             R. Storn , K. Price “Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces” TR-95-012 March 1995
3.             D. KARABOGA , S. OKDEM –“A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution algorithm” Turk J Elec Engin, VOL.12, NO.1 2004
4.             QIAO Feng, LIN Ping –“A differential evolutionary based algorithm for multiuser OFDMA system adaptive resource allocation” Journal of Communication and Computer, ISSN1548-7709, 2008.

5.             M.Maximiano, Miguel A. Vega-Rodríguez, Juan A. Gómez Pulido, Juan M. Sánchez-Pérez ,”A Hybrid Differential Evolution Algorithm to Solve a Real-World
Frequency Assignment Problem”IEEE,2008.

6.             Draa, S. Meshoul, H.Talbi, M. Batouche “A Quantum-Inspired Differential Evolution Algorithm for Solving the N-Queens Problem “The International Arab Journal of Information Technology, Vol. 7, No. 1, January 2010
7.             Chuan-wang Song, Xue-song Chu, Liang Li, Jing Wang “An improved differential evolution algorithm for the slope stability analysis “ ,IEEE , 2011.
8.             A.K.M. Khaled Ahsan Talukder, Michael Kirley and Rajkumar Buyya “Multiobjective Differential Evolution for Workflow Execution on Grids”, MGC 2007.\
9.             Samir SAYAH, Khaled ZEHAR ,Using Evolutionary Computation to Solve the Economic Load Dispatch Problem, Leonardo Journal of Sciences, 2008.
10.          Radha Thangaraj Millie Pant Ajith Abraham “New mutation schemes for differential evolution algorithm and their application to the optimization of directional over-current relay settings” Applied Mathematics and Computation , 532–544.,2010.

11.          S. K. Goudos, K.Siakavara, T. Samaras, E. Vafiadis, , J. N. Sahalos, “Self-Adaptive Differential Evolution Applied to Real-Valued Antenna and Microwave Design Problems “IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 59, NO. 4, APRIL 2011

12.          M. Vasile, , E Minisci, M. Locatelli “An Inflationary Differential Evolution Algorithm for Space Trajectory Optimization” IEEE TRANSACTIONS ON EVOLUTIONARYCOMPUTATION, VOL. 15, NO. 2, APRIL 2011

13.          X. Tong-yu, SUN Yan- Substation Locating and Sizing in Rural Power System Based on GIS and Modified Differential Evolution Algorithm” , IEEE,2009.

14.          M. Fatih Tasgetiren, P.N. Suganthan, Tay Jin Chua and Abdullah AlHajri” Differential Evolution Algorithms for the Generalized Assignment Problem”!EEE,2009.

15.          Wen-Hong Wang, Feng-Rui Wang, Quan-Ke Pan, Feng-Chao Zuo “Improved Differential Evolution Algorithm for Location Management in Mobile Computing”,IEEE,2009.

16.          YAN LI, DONG-FENG WANG, PU HAN”SELECTIVE ENSEMBLE USING DISCRETE DIFFERENTIAL EVOLUTION ALGORITHM FOR SHORT-TERM LOAD FORECASTING “,IEEE,2009.
17.          Wang Zhiqiang1, Zhang Xin1, Liu Wenxia1 and LIU Boliang2” Substation Planning Based on Geographic Information and Differential Evolution Algorithm
18.          Feng-rui WANG, Wen-hong WANG, Hua-qing YANG, Quan-ke PAN,”A Novel Discrete Differential Evolution Algorithm for Computer-aided Test-sheet Composition Problems”,IEEE,2009

19.          Jun Wang, Xingguo Cai, Jun Wang, Dongdong Wang”Study of Dynamic Available Transfer Capability with the Improved Differential Evolution Algorithm”,IEEE,2009

20.          SiBo Ding”Logistics Network Design Optimization Based on Differential Evolution Algorithm”,IEEE,2010.

21.          Sibo Ding” Logistics Network Design and Optimization Based on Fuzzy Adaptive Differential Evolution Algorithm”,IEEE,2010.

22.          Meng Zhang1 , Peiyong Sun 2 , Ruiting Cao1 , Jiangle Zhu1” LQG/LTR Flight Controller Optimal Design Based on Differential Evolution Algorithm”IEEE,2010.

23.          Kun Chao1, 2, Zhenwei Zhao1, Zhensen Wu2 and Rui Lang1”Application of the Differential Evolution Algorithm to the Optimization of Two-Dimensional Synthetic Aperture Microwave Radiometer Circle Array “,IEEE,2010.

24.          Adam Slowik”Application of Adaptive Differential Evolution Algorithm with Multiple Trial Vectors to Artificial Neural Networks Training” IEEE,2010


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12.

Authors:

Parveen Sadotra, Chandrakant Sharma

Paper Title:

Cyber Education: A Need of the Time

Abstract:     In the last two decades Internet has changed our lives in a great way. Almost everything is dependent on internet. Vast use of internet has created a new kind of threat to human civilization. Dependency of our day to day work in cyber world has become indispensable in present time. This revolution of internet has made our life much easy on one hand but at the other hand we need to be very much cautious. If we are not familiar of the pros and cons of the cyber world it may cost a huge loss sometimes to even life. So it is the need of time that every one of this earth should educated well about this beautiful cyber world along with all the security aspects and it needs a well-planned strategy to achieve 100 % cyber literacy.

Keywords:
   Cyber Education, Cyber Security, Cyber Theft, Cyber World, Literacy.


References:

1.          <http://ijcse.academic-publication.org/>
2.          <http://www.nativeintelligence.com/ni-programs/whyaware.asp>

3.          Parveen Sadotra (CEH), Prof. Vinus Sharma, 2015, “Measuring And Combating Spam on Social Networks”, ‘Cyber Times International Journal of Technology & Management’, Vol 8. Issue 1. Pg.28-32

4.          <http://deity.gov.in/content/information-security-education-and-awareness-project

5.          <https://tdloffice.wordpress.com/2013/10/21/trust-in-digital-life-introduction/>

6.          Parveen Sadotra (CEH), Dr. Anup Girdhar, 2015, “Role Of Cyber security In Private sector Domains”, ‘Cyber Times International Journal of Technology & Management’, Vol 8. Issue 1. Pg.7-11.

7.          <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.159.6388&rep=rep1&type=pdf>

8.          <http://www.witdom.eu/tdw2015>

9.          <http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?reload=true&punumber=6883276>

10.       <http://www.pewinternet.org/2014/03/11/digital-life-in-2025/>

11.       Parveen Sadotra, 2015. Research Challenges and Issues in Web Security. International Journal of Computer Engineering & Technology (IJCET).Volume:6, Issue: 5, Pages: 1-7


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13.

Authors:

Samuel A. Daramola, Abdulkareem Ademola

Paper Title:

Vehicle Classification Algorithm using Size and Shape

Abstract:      Automatic classification of vehicles into different classes based on their sizes and shapes is very useful for traffic control and toll collection process. Effective intelligent transportation system that incorporates vehicle classification technique is needed in many cities to prevent road accident and traffic congestion caused by illegal movement of vehicles. This work presents method of getting structural information from detected vehicle images and then uses it to classify vehicles into different classes. The technique involves extraction of contour features from vehicle images side view using morphological operations.  The extracted features were combined and used to generate feature vector that serve as input data to vehicle classification algorithm based on Euclidean distance measure.  Impressive result was achieved from the proposed vehicle classification method.

Keywords:
   Vehicle, Shape, Size, Features, Boundary, Classification.


References:

1.          S. Adebayo Daramola, E. Adetiba, A. U. Adoghe, J. A. Badejo, I. A Samuel and T. Fagorusi, Automatic Vehicle Identification System using License Plate, International Journal of Engineering Science and Technology, Vol. 3, No. 2, 2011, pp 1712-1719.
2.          Kaewkamnerd, S., Chinrungrueng, J., Pongthornseri, R., Dumnin, S, Vehicle classification based on magnetic sensor signal, Information and Automation (ICIA),  IEEE International Conference, 2010.pp.935-939.

3.          Kaewkamnerd, S., Pongthornseri, R., Chinrungrueng, J., Silawan, T, Automatic vehicle classification using wireless magnetic sensor, Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IEEE International Workshop, 2009, pp.420-424.

4.          Zhang, W., Tan, G.Z., Nan, D., Yao, S., Lin, M.W, Vehicle Classification Algorithm based on Binary Proximity Magnetic Sensors and Neural Network, Intelligent Transportation Systems, International IEEE Conference  pp.145-150, 2008.

5.          Guohui Zhang, Ryan P. Avery, Yinhai Wang,  A Video-based Vehicle Detection and Classification System for Real-time Data Collection Using Uncalibrated Video Cameras, National Research Council, Washington, DC , 2007.

6.          Ma, X. and Grimson, W. E. L, Edge-based rich representation for vehicle classification, .Computer Vision,  Tenth IEEE International Conference  Vol.2,  2005, pp.1185-1192. 

7.          Mehran Kafai and Bir Bhanu,  Dynamic Bayesian Networks for Vehicle Classification in Video, IEEE Transactions on Industrial Informatics, 2011, pp1-9.

8.          Jun Yee Ng, Yong Haur, Image based Vehicle Classification System, The11th Asian-Pacific ITS Forum and exhibition, 2011, pp1-11.

9.          Saeid Fazli , Shahram Mohammadi , Morteza Rahmani, Neural Network based Vehicle Classification for Intelligent Traffic Control, International Journal of Software Engineering & Applications, Vol.3, No.3, 2012, pp17-22.

10.       Otsu, N, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.


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14.

Authors:

S. Vadivel, B. Baskaran

Paper Title:

Comparison of Responses of the PI and the Fuzzy Logic Controlled Closed Loop DPFC Systems

Abstract:     The proposed work deals with comparison of the PI and the fuzzy logic controlled closed loop DPFC systems. The voltage across the load decreases due to the addition of extra load and the load voltage are restored back to normal value by using closed loop system. The ability of closed loop system to bring the voltage and reactive power back to the set value is represented in this paper. The simulation studies for open loop and closed loop systems are performed on a standard ten bus radial test system.

Keywords:
   DPFC - Distributed power flow control, DVR - Dynamic Voltage Regulator, ESS - Steady State Error, FACTS - Flexible AC Transmission System, Q - Reactive Power, STATCOM - Static Compensator, Ts - Settling Time, UPFC - Unified power flow control, V – Voltage, VAR - Voltage Ampere Reactive, VP - Peak Over Shoot Voltage.


References:

1.          S. Adebayo Daramola, E. Adetiba, A. U. Adoghe, J. A. Badejo, I. A Samuel and T. Fagorusi, Automatic Vehicle Identification System using License Plate, International Journal of Engineering Science and Technology, Vol. 3, No. 2, 2011, pp 1712-1719.
2.          Kaewkamnerd, S., Chinrungrueng, J., Pongthornseri, R., Dumnin, S, Vehicle classification based on magnetic sensor signal, Information and Automation (ICIA),  IEEE International Conference, 2010.pp.935-939.

3.          Kaewkamnerd, S., Pongthornseri, R., Chinrungrueng, J., Silawan, T, Automatic vehicle classification using wireless magnetic sensor, Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IEEE International Workshop, 2009, pp.420-424.

4.          Zhang, W., Tan, G.Z., Nan, D., Yao, S., Lin, M.W, Vehicle Classification Algorithm based on Binary Proximity Magnetic Sensors and Neural Network, Intelligent Transportation Systems, International IEEE Conference  pp.145-150, 2008.

5.          Guohui Zhang, Ryan P. Avery, Yinhai Wang,  A Video-based Vehicle Detection and Classification System for Real-time Data Collection Using Uncalibrated Video Cameras, National Research Council, Washington, DC , 2007.

6.          Ma, X. and Grimson, W. E. L, Edge-based rich representation for vehicle classification, .Computer Vision,  Tenth IEEE International Conference  Vol.2,  2005, pp.1185-1192. 

7.          Mehran Kafai and Bir Bhanu,  Dynamic Bayesian Networks for Vehicle Classification in Video, IEEE Transactions on Industrial Informatics, 2011, pp1-9.
8.          Jun Yee Ng, Yong Haur, Image based Vehicle Classification System, The11th Asian-Pacific ITS Forum and exhibition, 2011, pp1-11.
9.          Saeid Fazli , Shahram Mohammadi , Morteza Rahmani, Neural Network based Vehicle Classification for Intelligent Traffic Control, International Journal of Software Engineering & Applications, Vol.3, No.3, 2012, pp17-22.

10.       Otsu, N, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.


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15.

Authors:

Vaibhav V. Shah, Smitkumar J. Mirani, Yashvardhan V. Nanavati, Vishal Narayanan, Sheetal I. Pereira

Paper Title:

Stock Market Prediction using Neural Networks

Abstract: In this paper we present our efforts to predict the stock market using Artificial Neural Networks. We study different types of Neural Networks, their salient features along with the internal working of these networks and the various configurations that they can be run with. We go to comment on the advantages and disadvantages of these networks. Finally we select the one network with specific configurations and use it to predict the stock prices of a few selected companies from the National Stock Index. We achieve best case accuracy of 98% on the dataset.

Keywords:
   Artificial Neural Networks, Neurons, Back propagation algorithm, Prediction methods, Stock markets.


References:

1.        http://wsc10.softcomputing.net/ann_chapter.pdf
2.        https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/History/history1.html

3.        https://en.wikipedia.org/wiki/AlphaGo

4.        http://gizmodo.com/gmail-now-uses-artificial-neural-networks-to-sniff-out-1716975952

5.        http://gizmodo.com/googles-neural-network-can-now-reply-to-gmail-messages-1740260404

6.        https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/Neuron/index.html

7.        https://www.google.co.in/imgresimgurl=http://www.extremetech.com/wpcontent/uploads/2015/07/NeuralNetwork.png&imgrefurl=
http://www.extremetech.com/extreme
/215170-artificial-neural-networks-are-changing-the-world-what-arethey&h=590&w=1008&tbnid=ru1iSgMkF619M:&docid=uw9rq85fHW8ClM&ei=LtW1Vrb 7IoqeugTZ85fwDg&tbm=isch&ved=

8.        0ahUKEwi2ypfhgOPKAhUKj44KHdn5Be4QMwhCKBEwEQ

9.        http://mnemstudio.org/neural-networks-backpropagation.htm

10.     http://www.frank-dieterle.de/phd/images/image016.gif

11.     http://in.mathworks.com/help/nnet/ref/trainlm.html?refresh=true

12.     https://en.wikipedia.org/wiki/Gradient_descent
13.     http://in.mathworks.com/help/nnet/ref/logsig.html
14.     http://matlab.izmiran.ru/help/toolbox/nnet/logsig.gif

15.     https://www.quandl.com/data/NSE?keyword=

16.     http://www.measuringu.com/zcalc.htm

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16.

Authors:

Rajesh B. Mapari, Govind Kharat

Paper Title:

Performance Analysis of Different Classifiers for American Sign Language Recognition

Abstract:    American Sign Language alpha-numeric character recognition without using any embedded sensor, color glove or without the constraints of an environment is a really difficult task. This paper describes a novel method of static sign recognition using a leap Motion sensor by obtaining feature set based on hand position, distance and angle between different points of hand. A feature set is later trained and tested using different classifiers like MLP (Multilayer Perceptron), GFFNN (Generalized Feed forward Neural Network), SVM (Support Vector Machine). We have collected dataset from 146 people including students of age 20-22 years and few elders age between 28-38 who have performed 32 signs resulting in total dataset of 4672 signs. Out of this 90% dataset is used for training and 10% dataset is used for testing/Cross validation. we have got maximum classification accuracy as 90% on CV/testing dataset using MLP Neural Network.

Keywords:
 ASL, MLP, GFFNN, SVM.


References:

1.          Fu-Hua Chou, Yung-Chun Su., "An Encoding and Identification Approach for the Static Sign Language Recognition", The 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics , pp. 885-889, July 11-14, 2012.
2.          M.S. Sinith, Soorej G Kamal, Nisha B. ,Nayana S, Kiran Surendran, Jith P S, "Sign Language Recognition Using Support Vector Machine", International Conference on Advances in Computing and Communications, pp. 122-125, Aug.  9-11, 2012.
3.          Hee-Deok Yang, Seong-Whan Lee, "Combination of Manual and Non-Manual Features for Sign Language Recognition Based on Conditional Random Field and Active Appearance Model", Proceedings of the 2011 International Conference on Machine Learning and Cybernetics, pp. 1726-1731, July 10-13, 2011.
4.          Fahad Ullah, “American Sign Language Recognition System for Hearing Impaired People Using Cartesian Genetic Programming”, 5th  International Conference on Robotics and Application, p.p. 96-99, Dec. 6-8, 2011.

5.          Asha Thalange, Dr. Shantanu Dixit, "Effect of Thinning Extent on ASL Number Recognition Using Open-finger Distance Feature Measurement Technique", International Conference on Signal Processing And Communication Engineering Systems (SPACES), pp.39-43, Jan. 2-3, 2015.

6.          Priyanka Mekala, Ying Gao, "Real-time Sign Language Recognition based on Neural Network Architecture", in Proceedings IEEE, pp.195-199, March 14-16, 2011.

7.          Taehwan Kim, Karen Livescu, Gregory Shakhnarovich, "American Sign Language Fingerspelling Recognition with Phonological Feature based Tandem Models", Proceedings IEEE , pp.119-124, Dec. 2-5, 2012.

8.          Dominique Uebersax, Juergen Gall et al., "Real-time Sign Language Letter and Word Recognition from Depth Data", IEEE International Conference on Computer Vision Workshops, pp. 383-390, Nov. 6-13, 2011.

9.          Jerome M. Allen, Pierre K. Asselin, Richard Foulds,  “American Sign Language Finger Spelling Recognition System” Proceedings of IEEE 29th Annual Northeast Bioengineering Conference, pp.285-286, March  22-23, 2003.

10.       Vasiliki E. Kosmidou, Leontios J. Hadjileontiadis , Stavros M. Panas “Evaluation of surface EMG features for the recognition of American Sign Language gestures”  Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, pp. 6197-6200, Aug. 30-Sept. 3, 2006.

11.       C. S. Weerasekera, M. H. Jaward, N. Kamrani, "Robust ASL Fingerspelling Recognition Using Local Binary Patterns And Geometric Features", Proceedings of IEEE, pp. 1-8, Nov. 26-28,2013.

12.       Lucas Rioux-Maldague, Philippe Gigu`ere, "Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Network", Canadian Conference on Computer and Robot Vision, pp. 92-97, May 6-9, 2014.

13.       A.S. Elons, Menna Ahmed, Hwaidaa Shedid and M.F.Tolba, “Arabic Sign Language Recognition Using Leap Motion Sensor” 9th International Conference on Computer Engineering & Systems (ICCES), pp. 368-673, Dec. 22-23,  2014.

14.       L. Nanni, A. Lumini, F. Dominio, M. Donadeo, and P. Zanuttigh. "Ensemble to improve gesture recognition", International Journal of Automated Identification Technology, 2014.
15.       Cao Dong, Ming C. Leu, Zhaozheng Yin, "American Sign Language Alphabet Recognition Using Microsoft Kinect", The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (Boston, MA, , 2015).,  pp. .44-52, June 7-12, 2015.
16.       Giulio Marin, Fabio Dominio, Pietro Zanuttigh, "Hand gesture recognition with leap motion and kinect devices", IEEE International Conference on Image Processing (ICIP), pp. 1565 – 1569, Oct. 27-30, 2014.

17.       Makiko Funasaka, Yu Ishikawa, Masami Takata, and Kazuki Joe, " Sign Language Recognition using Leap Motion Controller",  Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA). pp. 263-269,  2015.

18.       Wikipedia. “Leap Motion” Wikipedia.org. [Online]. Available https://en.wikipedia.org/wiki/Leap_Motion [Last Modified: 26 July 2015, 07:16].Wikipedia. “Leap Motion” Wikipedia.org. [Online]. Available https://en.wikipedia.org/wiki/Leap_Motion [Last Modified: 26 July 2015, 07:16].


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