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Volume-2 Issue-11: Published on October 20, 2014
19
Volume-2 Issue-11: Published on October 20, 2014

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Volume-2 Issue-11, October 2014, ISSN: 2319-9598 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Chetan S. Deshpande, N. V. Deshpande

Paper Title:

Experimental and Analytical Investigation of Ferro Cement Silo for Various H/D Ratios and Wall Thickness

Abstract: Ferrocement being a versatile construction material, its applicability in case of silos which can be used for food grain storage creates an area of research. Its cracking or tensile strength and corresponding strains are the key parameters of ferrocement silo designing. In this paper an attempt has been made to investigate tensile strength and strains of ferrocement flats of particular thickness, reinforced with single layer of square welded wire mesh and to calculate, predict and compare the hoop tensions for different silo wall thicknesses and H/D ratios using the equations developed graphically which are based on analytically developed parameters derived from the test results.

Keywords:
Ferrocement, Hoop tension, Silo, Tensile strength.


References:

1.        ACI 549R – 97, State – of – the – Art Report on Ferrocement
2.        Azad  A. Mohammed and Dunyazad K. Assi, Tensile Stress – Strain Relationship For Ferrocement Structures, Al – Rafidain Engineering, Volume 20, No. 2, March 2012, pp 27 – 40.

3.        Gangadharappa B. M., Prakash K. E., Suresh G. S. and Shesha Prakash M. N. “Studies of Light Weight Ferrocement Subjected to Axial Tension”, International Journal of Emerging Technologies in Computational and Applied Sciences, 2013, pp 239 – 245.

4.        Sayyed Shoheb Navid, Swayambhu S. Bhalsing and Pankaj B. Autad “Tensile Strength of Ferrocement with Respect to Specific Surface”, International Journal of Engineering and Advanced Technology, December 2013,Volume - 3, Issue – 2,  pp 473 – 475.

5.        “A report on Ferrocement: Applications in Developing Countries”, National Academy of Sciences.

6.        Chetan S. Deshpande, Dr. N. V. Deshpande, “Study of Ferrocement Silo used for Bulk Material Storage”, International Journal of Application or Innovation in Engineering & Management”, Special Issue for National Conference on Recent Advances in Technology and Management for Integrated Growth 2013.

7.        I.S. 4995 (I) – 1974, “Criteria for Design of Reinforced Concrete Bins for the Storage of Granular & Powdery Materials, General Requirements and Assessment of Bin Loads”.

8.        I.S. 4995 (II) – 1974, “Criteria for Design of Reinforced Concrete Bins for the Storage of Granular & Powdery Materials, Design Criteria”.


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2.

Authors:

S. Arun, N. Yashwanth, R. Adharsh

Paper Title:

Experimental and Comparison Studies on Drying Characteristics of Tomatoes in a Solar Tunnel Greenhouse Dryer Coupled with and without Biomass Backup Heater

Abstract: A natural convection solar tunnel greenhouse dryer coupled with biomass heater was designed and developed in Nallampalli region of Pollachi, Tamil Nadu (India) and also a natural convection solar tunnel greenhouse dryer without biomass heater was designed and developed in Negamam region of Pollachi, Tamil Nadu (India) for carrying out the experimental and comparison studies of drying characteristics of tomatoes during the month of May, 2014. About 50kgs of fresh and good quality tomatoes were loaded into those two respective dryers and it was repeated for three trails. The mass of fuel added to the biomass heater was about 7.5kg/hr. The biomass heater was ignited when there is a fall in sunshine (after 5PM) in order to maintain the temperature inside the dryer. The solar tunnel dryer coupled with the biomass heater dried the tomatoes which has an initial moisture content of 90% (w.b.) to a final moisture content of 9.5% (w.b.) over a time period of 24 hours whereas the solar tunnel greenhouse dryer without the biomass heater took 49 hours for reducing the moisture content of the tomatoes to the same level. The reduced drying time in the solar tunnel greenhouse dryer coupled with the biomass heater than that of the dryer without the biomass heater is due to the effect of biomass heater that is responsible for the steady increase in temperature inside the dryer by supplying sufficient heat during the night time (after 5PM) where there would be a drop in sunshine. Also the quality of the tomatoes obtained from the solar tunnel greenhouse dryer coupled with biomass heater was found to be superior to that of the tomatoes obtained from the solar tunnel greenhouse dryer without the biomass heater which is due to the high temperature and low relative humidity prevailed all the time inside the dryer irrespective of fall in sunshine.

Keywords:
Biomass heater, drying time, moisture content, open sun drying, quality, solar tunnel greenhouse dryer, sunshine, temperature.  


References:

1.        H. A. Ensminger, E. M. Ensminger , E. J.Kolande , and K. R. Robson, (1994) . Florida, USA: CRC  Press. vol.2, 2nd ed. pp.2111-2114
2.        P.N. Sarsavadia, R. L Sawhney, D.R. Pangavhane, and S.P. Singh,“Drying behaviour of brined onion slices”, Journal of food Engineering, 1999, vol.40, pp. 219-226.

3.        I. Doymaz, “Air-drying characteristics of tomatoes”, Journal of Food Engineering, 2007, vol. 78(4), pp. 1291-1297.

4.        B. Zanoni, C. Peri, R. Nani and V. lavelli, “Oxidative damage of tomato halves as Effected by drying”, Food Research   International, 1999, vol.31 (5), pp. 359-401.

5.        A. Heredia, C. Barrera, and A. Andres, “Drying of cherry tomato by a Combination of different Comparison kinetics and other related properties”, Journal of Food Engineering, 2006.

6.        A. Midilli, and H. Kucuk., “Mathematical modeling of thin layer drying of pistachio by using solar energy”, Energy Conversion and Management, 2003, vol. 44(7), pp. 1111-1122.

7.        E. K. Akpinar, Y. Bicer, and C. Yildiz, “Thin layer drying of red pepper”, Journal of Food Engineering, 2003, vol. 59(1), pp. 99-104.

8.        E. K. Akpinar, “Drying of mint leaves in a solar dryer and under open sun: Modelling, performance analyses”, Energy Conversion and Management, 2010, vol. 51(12), pp. 2407-2418.

9.        M. Aghbashlo, M. H. Kianmehr, and A. Arabhosseini, “Modeling of thin-layer drying of potato slices in length of continuous band dryer”, Energy Conversion and Management, 2009, vol. 50(5), pp.1348-1355.

10.     T. Y. Tunde-Akintunde, “Mathematical modeling of sun and solar drying of chilli pepper”, Renewable Energy, 2011, vol. 36(8), pp. 2139-2145.

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3.

Authors:

S. Arun, N. Yashwanth, R. Adharsh

Paper Title:

Experimental and Comparison Studies on Drying Characteristics of Red Chillies in a Solar Tunnel Greenhouse Dryer and in the Open Sun Drying Method

Abstract: A natural convection solar tunnel dryer was designed and developed for carrying out the experimental and comparison studies of drying characteristics of red chillies during the month of April, 2014 in Negamam region of Pollachi, Tamil Nadu (India). About 50 kgs of red chillies were loaded into the dryer and is repeated for three trails. The drying parameters such as drying time and product quality were taken into account to find out the best drying method for red chillies. The red chillies which has an initial moisture content of 72.98% (w.b.) was reduced to a final moisture content of 7.5% (w.b.) over a time period of 56 hours in the solar tunnel greenhouse dryer whereas the open sun drying method took 122 hours for reducing the moisture content of red chillies to the same level. Also, the quality of red chillies produced from the solar tunnel greenhouse dryer was found to be superior to that of the open sun drying method.

Keywords:
Drying time, moisture content, open sun drying, product quality, red chillies, solar tunnel greenhouse dryer.


References:

1.        S. Desai, V. Palled, and M. Anantachar, “Performance evaluation of farm solar dryer for chilly drying”, Karnataka Journal of Agricultural Sciences, 2009, vol. 22(2), pp. 382–384.
2.        S. Mangaraj, A. Singh, D. V. K. Samuel, O. P. Singhal, “Comparative performance evaluation of different drying methods for Chillies”. Journal of Food Science and Technology, 2001, vol. 38 (3), 296–299.

3.        M. A. Hossain, J. L. Woods , B. K. Bala, “Thin layer drying of Thai red chilli” , ADC 333-335.

4.        B. K. Bala, M. R. A.  Mondol, B. K. Biswas, B. L. Das Chowdury, &   S. Janjai, “ Solar drying of pineapple using solar tunnel drier”,  Renewable Energy, 2003, vol. 28, pp.183-190.

5.        T. Y. Tunde-Akintunde, “Mathematical modeling of sun and solar drying of chilli pepper”, Renewable Energy, 2011, vol. 36 (8), pp. 2139–2145.

6.        J. Kaewkiew, S. Nabneaan, and S. Janjai, “Experimental investigation of the performance of a large-scale greenhouse type solar dryer for drying chilli in Thailand”, Procedia Engineering, 2012, vol. 32, pp. 433–439.

7.        M. A. Hossain and B. K. Bala, “Drying of hot chilli using solar tunnel drier”, 2007, Solar Energy, vol. 81 (1), pp. 85-92.

8.        A. O. Dissa, J. Bathiebo, S. Kam, P. W. Savadogo, H. Desmorieux, and J. Koulidiati, “Modelling and experimental validation of thin layer indirect solar drying of mango slices”, Renewable Energy, 2009, vol. 34(4), pp. 1000–1008.

9.        M. Aktas¸, I. Ceylan, and S. Yilmaz, “Determination of drying characteristics of apples in a heat pump and solar dryer”, Desalination, 2009, vol. 238, pp. 266–275.

10.     R. P. F. Guin´e, D. M. S. Ferreira, M. J. Barroca, and F. M. Gonc¸alves, “Study of the drying kinetics of solar-dried pears”, Biosystems Engineering, 2007, vol. 98(4), pp. 422–429.


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4.

Authors:

Kirandeep Kaur, Vinay Chopra

Paper Title:

Review of Automatic Test Case Generation from UML Diagram using Evolutionary Algorithm

Abstract: Software testing plays a vital role in software development life cycle. An approach of testing which takes place at design phase can remove errors in the system and improvise the developed project. Automatic test case generation can be used for testing software or real time applications. Many evolutionary algorithms are used for generating test case automatically. This paper represent review of approach of  automatic test case generation by analyzing the dynamic behaviour of  UML diagram which takes place at design phase of SWDLC by using evolutionary algorithm multi objective genetic algorithm. Single objective genetic algorithm has been already used for automatic testing.

Keywords:
UML diagram, Test cases, MOGA, DFS, Tree structure.


References:

1.        A. Balieiro, P. Yoshioka, K. Dias, C. Cordeiro and D. Cavalcanti,” Adaptive spectrum sensing for cognitive radio based on multi-objective genetic optimisation”, ELECTRONICS LETTERS ,Vol. 49 No. 17, 2013.
2.        Alessandra Cavarra, Thierry Jeron, Alan Hartman “Using UML for Automatic Test Generation”, ISSTA,2002.

3.        Ashalatha Nayak and Debasis Samanta “Automatic Test Data Synthesis Using UML Sequence Diagram”, Journal of Object Technology, vol. 09, no. 2, March-April, pp. 75-104, 2010.

4.        A.V.K Shanthi and G. Mohan Kumar “A Heuristic Technique for Automated Test Cases Generation for UML Activity Diagram”, JCSA, vol. 4, no. 2, pp. 75-86, 2012.

5.        A.V.K Shanthi and Dr. G. Mohan Kumar, “A Novel Approach for Generating Test Cases Using Tabu Search Algorithm”, UNIASCIT, vol. 2, no. 2, pp. 222-224, 2012.

6.        A.V.K.Shanthi and G. Mohan. Kumar, ” Automated Test Cases Generation from UML Sequence Diagram”, IJSCA, vol. 41, Singapore, pp. 83-89, 2012.

7.        A.V.K.Shanthi and Dr. G. Mohan Kumar “Automated Test Cases Generation For Object Oriented Software”, Indian Journal of Computer Science and Engineering, vol. 2, no. 4, Aug-Sep, pp. 543-546, 2011.

8.        A.V.K.Shanthi and G. Mohan Kumar, “A Heuristic Approach for Automated Test Cases Generation for Sequence Diagram Using Tabu Search Algorithm”, European Journal of Scientific Research, vol. 85, no. 54, September, pp. 534-540, 2012.

9.        Aysh Alhroob, Keshav Dahal, Alamgir Hossain,“ Automatic Test Cases Generation from Software Specification”, ISE Journal, vol. 4, issue 1, pp. 109-121, 2010.

10.     Deb, K. “Multi-Objective Optimization Using Evolutionary Algorithms”. Reading, John Wiley & Sons, Ltd, Reprinted April 2002.

11.     Fonseca C M, Fleming P J, “Multi-objective optimization and multiple constrains handing with evolutionary algorithms-Part I: a unified formulation”, IEEE Transactions on systems, Man and cybernetics, vol. 28, pp. 26-37, 1998.

12.     Gu Lei, Yu-geng Xi, “A Parellel Multi-Objective Genetic Local Search Algorithm ”, Control Engineering of China, Shenyang, vol. 16, pp. 738- 742, Nov. 2009

13.     Heidi A. Taboada, Jose F. Espiritu, and David W. Coit,” MOMS-GA: A Multi-Objective Multi-State Genetic Algorithm for System Reliability Optimization Design Problems”, IEEE TRANSACTIONS ON RELIABILITY, VOL. 57, NO. 1, MARCH 2008.

14.     Hitesh Tahbildar and Bichitra Kalita , “Automated Software Test Data Generation: Direction of Research”, IJCSE Survey, vol. 2, no. 1, Feb, PP. 99- 120, 2011.

15.     Huang Wei, Fengli, He Zijun, Cui Junzhao, Zhang Li,” Transmission Network Planning With N-1 Security Criterion Based On Improved Multi-objective Genetic Algorithm”, IEEE( 978-1-4577-0365-2) , pp-1250-1254,2011.

16.     L.C.Braind, Y.Labiche, She,  “Automating Regression Test Selection Based on UML designs”, Information and Software Technology, vol. 51, issue 1, January, pp. 16-30, 2009.

17.     M. Prassana and K.R. Chandran ,“Automatic Test Cases Generation for UML Object Diagrams Using Genetic Algorithm”, Int.J.Advance.Soft Comput. Appl., vol. 1. No. 1, July, pp. 19-32, 2009.

18.     Manoj Kumar, Dr. Mohammad, Gyanendra K.Gupta, Amarjeet Singh ,“An Efficient Algorithm for Evaluation of Object Oriented Models”, IJCA, vol. 24, no. 8, June, pp. 11-15, 2011.

19.     Mr. Kashif Waqas1 Dr. Rauf Baig1and Dr. Shahid Ali2,” Feature Subset Selection Using Multi-Objective Genetic Algorithms”, IEEE, ISSN- 978-1-4244-4873-9,2009.

20.     Pakinam N.Boghdady, Nagwa L.Badr, Mohmed Hashen and Mohnamed F.Tolba,“Test Case Generation and Test Extraction Techniques”, IJECS, vol. 11, no. 3, June, pp. 87-94, 2011.

21.     Puneet Patel and Nitin N. Patil ,“Test Case Formation Using UML Activity Diagram”, World Journal of Science and Technology, vol. 2, no. 3, April, pp. 57-62, 2012.

22.     Rakesh Kumar, Surjeet Singh, Girdhar Gopal,” Automatic Test Case Generation Using Genetic Algorithm”,IJSCR, Volume 4, Issue 6, June, pp-1135-1141, 2013.

23.     Ranjit Swain, Vikas Panthi, Prafulla Kumar Behera and Durga Prasad Mohapatra , “ Automatic  Test Case Generation from UML Sate Chart Diagram”, IJCA, vol. 42, no. 7, March, pp. 26-36, 2012.

24.     Ranjit Swain, Vikas Panthi, Prafulla Kumar Behera and Durga Prasad Mohapatra,“Test Case Generation Based on State Machine Diagram”, IJCIS, vol. 4, no. 2, Feb, pp. 99-110, 2012.

25.     Swati, Thilian, Pallavi Pandit, “A Survey of UML Based Approaches to Testing”, International Journal of Computational Engineering Research, vol. 2, issue 5, September, pp. 1396-1401, 2012.

26.     Santosh Kumar Swain, Durga Prasad Mohapatra and Rajib Mall ,“Test Case Generation Based on State and Activity Diagram”, Journal of Object Technology, vol. 9, no. 5, pp. 1-27, 2010. 

27.     Santosh Kumar Swain and Durga Prasad Mohapatra “Test Case Generation from Behavioral UML Models”, International Journal of Computer Applications,  vol. 6, no. 8, September, pp. 5-11, 2010.

28.     Sangeeta Sabharwal, Ritu Sibal and Chayanika Sharma,“Applying Genetic Algorithm for Prioritization of Test Cases Scenarios Derived from UML Diagrams”, International Journal of Computer Science Issue,Vol. 8, issue 3, No. 2, , pp. 433-444, 2011.

29.     Srinivas N, Deb K, “Multiobjective optimization using nondominated sorting in genetic algorithms”, Evolutionary Computation, vol. 2, pp. 221-248, 1994.

30.     V. Chankong and Y. Haimes, “Multi-objective decision making theory and methodology”. New York: North-Holland, 1983.

31.     Vikas Panthi, Durga Prasad Mahopatra,” Automatic Test Case Generation using Sequence Diagram”, Proceeding of ICADC, AISC 174, pp.277-284, 2013.

32.     V. Mary Sumalatha, G.S.V. P.Raju,” Object Oriented Test Case Generation Technique Using Genetic Using Genetic Algorithms”, International Journal of Computer Application(0975-8887), Volume61-No.20,January 2013.


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5.

Authors:

Ribata Najoua, Aknin Noura, El Kadiri Kamal Eddine

Paper Title:

Raise Performance in Mobile Cloud-Based Learning

Abstract: This paper will describe the research method used for the design of the Collaborative as a Service (CaaS). Which is to provide a novel approach to raise performance in mobile Cloud-Based Learning, by a constructive approach of task allocation in mobile cloud-based learning, using Kolb’s Learning Style (KLS) to accurately allocate responsible tasks to each learner in order to raise collaborative learning performance? We employ a Genetic Algorithm (GA) to facilitate the task allocation.

Keywords:
Collaborative as a Service, Genetic Algorithm, Kolb’s Learning Style, Mobile Cloud-Based Learning, Task allocation problem.


References:

1.        Mell, P & Grance, T 2009, ‘Draft NIST Working Definition of Cloud Computing. National Institute of Standards and Technology’.
2.        Vouk, M. A 2008, ‘Cloud computing — issues, research and implementations’, Journal of Computing and Information Technology, vol. 16, no. 4, pp. 235-246.

3.        Rimal, B. P, Choi, E & Lumb, I 2009, ‘A taxonomy and survey of cloud computing systems’, 5th International Joint Conference on INC, IMS and IDC, Seoul, Korea, August 2009.

4.        Dagon, D, Martin, T & Starner, T 2004, ‘Mobile phones as computing devices: the viruses are coming’, IEEE Transaction on Pervasive Computing, vol. 3, no. 4, pp. 11-15.

5.        Rao, N. M 2010, ‘Cloud Computing Through Mobile-Learning’, International Journal of Advanced Computer Science and Applications, vol. 1, no. 6, pp. 42-47.

6.        Ribata, N, Arif, A, ; Aknin, N, El Kadiri, K. E 2013, ‘Mobile Cloud-Based Learning: A Comprehensive Survey, Research Issues and Future Directions’, MAROC’2013 : Workshop on Models and Algorithms for Reliable and Open Computing, Tetouan/Morocco, December 16, 2013.

7.        Sharples, M, Arnedillo-Sanchez, I, Milrad, M & Vavoula, G 2009, ‘Mobile learning: small devices, big issues’, Technology-Enhanced Learning, Springer, pp. 233-249.

8.        Sharma, S. K & Kitchens, F. L 2004, ‘Web Services Architecture for M- Learning’, Electronic Journal on e-Learning, vol. 2, no. 1, pp. 203-216.

9.        Kim, S.H, Mims, C & Holmes, K.P 2006, ‘An introduction to current trends and benefits of mobile wireless technology use in higher education’, Association for the Advancement of Computing in Education Journal, vol.14, no.1, pp. 77-100.

10.     Vanska, R 2004, ‘Mobile learning in Europe: A multidisciplinary approach’, Proceedings of the 12th European Conference and Specialist Trade Fair for Educational and Information Technology, Karlsruhe, Germany, 2004.

11.     Koole, M, McQuilkin, J. L & Ally, M 2010, ‘Mobile Learning in Distance Education: Utility or Futility’, The Journal of Distance Education, vol. 24, no. 2, pp. 59-82.

12.     Ting, R. Y. L 2005, ‘Mobile learning: current trend and future challenges’, 5th IEEE International Conference on Advanced Learning Technologies (ICALT), Kaohsiung, Taiwan, July 2005.

13.     Zawacki-Richter, O, Brown, T & Delport, R 2009, ‘Mobile learning: from single project status into the mainstream’, European Journal of Open, Distance and E- Learning, vol. 2009, no. 22.

14.     Sultan, N 2010, ‘Cloud computing for education: A new dawn?’ International Journal of Information Management, vol. 30, no. 2, pp. 109-116.

15.     Liao, J & Wang, M 2011, ‘A collaborative learning system based on cloud and e-commerce’, 8th IEEE International Conference on e-Business Engineering, Beijing, China, October 2011.

16.     Chua, F. F & Tay, E. S 2012, ‘Developing virtual learning environment 2.0 using web services approach’, 12th IEEE International Conference on Advanced Learning Technologies, Rome, Italy, July 2012.

17.     Schwabe, G, Goth, C & Frohberg, D 2005, ‘Does team size matter in mobile learning’, International Conference on Mobile Business (ICMB), July 2005.

18.     Feldmann, B 2006 ‘Group Types in E-Learning Environments-Study Team, Working Team and Learning Team’, 7th International Conference on Information Technology Based Higher Education and Training (ITHET), Ultimo, Australia, July 2006.

19.     Wu, T, Liu, D & Bi, X 2010, ‘Team learning orientation and conflicts influence team performance in different task difficulties’, IEEE International Conference on Advanced Management Science (ICAMS), Chengdu, China, July 2010.

20.     Koh, E & Lim, J 2012, ‘Too Early, Too Bad: Uncovering and Understanding the Initial Participation Paradox in Technology-Mediated Learning Teams’, IEEE Transactions on Professional Communication, vol. 55, no. 1, pp. 55-84.

21.     Kolb, A. Y & Kolb, D, A 2005 ‘Learning styles and learning spaces: enhancing experiential learning in higher education’, Academy of Management Learning and Education, vol. 4, no. 2, pp.193-212.

22.     Kolb, A.Y & Kolb, D. A 2005, Bibliography of research on experiential learning theory and the Learning Style Inventory, Department of Organizational Behavior, Weatherhead School of Management, Case Western Reserve University, Cleveland, OH.

23.     Kolb, D. A 1984, Experiential learning: experience as a source of learning and development. Upper Saddle River, NJ:Prentice Hall.

24.     Kolb, D. A 1999, Learning Style Inventory, Hay/Mcber Training Resource Group, Boston, USA.

25.     Belbin, R 1993, Team Roles at Work, Butterworth Heinemann.

26.     Loo, R 2004, ‘Kolb’s learning styles and learning preferences: is there a linkage’, Educational Psychology, vol. 24, no. 1, pp. 99-108.

27.     Raschick, M, Maypole, D. E & Day, P. A 1998, ‘Improving field education through Kolb’s learning theory’, Journal of Social Work Education, vol. 34, no. 1 pp. 34-42.

28.     Jones, C, Reichard, C & Mokhtar, K 2003, ‘Are students’ learning styles discipline specific?’, Community College Journal of Research and Practice, vol. 27, no. 5, pp. 363-375.

29.     Thiele, J. E 2003, ‘Learning patterns of online students’, Journal of Nursing Education, vol. 42, no. 8, pp. 364-367.

30.     Terrell, S. R & Dringus, L 2000, ‘An investigation of the effecti of learning style on student success in an online learning environment’, Journal of Educational Technology Systems, vol. 28, no. 3 pp. 231-238.

31.     Richmond, A. S & Cummings, R 2005, ‘Implementing Kolb’s learning styles into online distance education’, International Journal of Technology in Teaching and Learning, vol. 1, no. 1, pp. 45-54.
32.     Vidyarthi, D. P, Tripathi, A. K, Sarker, B. K and Rani, K 2003 ‘Comparative study of two GA based task allocation models in distributed computing system’, The 4th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT).
33.     Page, A. J, Keane, T. M and Naughton, T. J 2010, ‘Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system’, Journal of Parallel Distributed Computing, vol. 70, no. 7, pp. 758-766.

34.     Fernandez, J and Basavaraju 2012 ‘Task allocation model in globally distributed software projects’, the 7th IEEE International Conference on Global Software Engineering, Rio Grande do Sul, Brazil, August 2012.
35.     Holland, J. H 1992 Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA, USA.

36.     Andrew, J. P, Thomas, M. K & Thomas, J. N 2010 ‘Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system’, Journal of Parallel Distributed Computing, vol. 70, pp. 758-766.


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