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Volume-1 Issue-2: Published on January 15, 2013
Volume-1 Issue-2: Published on January 15, 2013

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Volume-1 Issue-2, January 2013, ISSN: 2319–6386 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Moinuddin Sarker, Mohammad Mamunor Rashid

Paper Title:

Waste Tyre and Polypropylene Mixture into Petroleum Fuel using ZnO

Abstract: Waste tyre and polypropylene waste plastic into petroleum fuel production process with laboratory batch process in present of ZnO catalyst at temperature range 250-430 ºC. In this experiment Pyrex glass reactor was use and sample was using total 75 gm.  Waste tyre was 25 gm by weight and polypropylene was 50 gm by weight. 5% Zinc Oxide catalyst was use in this experiment to accelerate the reaction.  Product fuel density is 0.75gm/ml and fuel color is light yellow. Waste tyre and polypropylene waste plastic to fuel conversion percentage was 63.47 %, light gas percentage was 12.27 %, and solid black residue percentage was 24.26%. Product fuel was analysis by using Perkin Elmer GC/MS and GC/MS chromatogram showed carbon chain range C3 to C21. GC/MS analysis result indicate that product fuel has aliphatic hydrocarbon including alkane, alkene and alkyl group, aromatic group, halogenated group, alcoholic group, nitrogen content and oxygen content compounds. Aromatic group compounds are Toluene,  1-ethyl-3-methyl-Benzene,  propyl-Benzene,  1,3,5-trimethyl-Benzene, Limonene and so on.  Product fuel can use internal combustion engine and feed for refinery process.    

Scrap tire, polypropylene, waste plastic, synthetic fuel, conversion.


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31.     Miriam Arabiourrutia, Martin Olazar, Roberto Aguado, Gartzen Lopez, Astrid Barona, and Javier Bilbao, HZSM-5 and HY Zeolite Catalyst Performance in the Pyrolysis of Tires in a Conical Spouted Bed Reactor, Ind. Eng. Chem. Res. 2008, 47, 7600–7609




Utpal Jyoti Bora, Majidul Ahmed

Paper Title:

E-Learning using Cloud Computing

Abstract: Cloud computing is becoming an adoptable technology for many of the organizations with its dynamic scalability and usage of virtualized resources as a service through the Internet. Cloud computing is growing rapidly, with applications in almost any area, including education. Now a day, e-learning is also becoming very popular and powerful trend, which is also broad. E-learning systems usually require many hardware and software resources. This paper presents the benefits of using cloud computing for e-learning. There are many educational institutions that cannot afford such investments, and cloud computing is the best solution,  especially in the universities where the use of computers are more intensive and what can be done to increase the benefits of common applications for students and teachers.

Cloud Computing, E-learning, ICT, SaaS, PaaS, IaaS.


1.        “A NEW TREND FOR E-LEARNING IN KSA USING EDUCATIONAL CLOUDS”, Abdullah Alshwaier, Ahmed Youssef and Ahmed Emam, Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.1, January 2012
2.        “Effective use of cloud computing in educational institutions”, Tuncay Ercana, WCES-2010

3.        “THE UTILITY OF CLOUD COMPUTING AS A NEW PRICING – AND CONSUMPTION - MODEL FOR INFORMATION TECHNOLOGY”, David C. Wyld, Department of Management, Southeastern Louisiana University, Hammond, LA USA, International Journal of Database Management Systems (IJDMS), Vol.1, No.1, November 2009

4.        “Cloud Computing-Future Framework for e-management of NGO's”, 1. Harjit Singh Lamba, 2.Gurdev Singh, International Journal of Advancements in Technology ISSN 0976-4860, Vol 2, No 3 (July 2011)

5.        “E-learning based on Cloud Computing”, Deepanshu Madan, Scholar’s; Computer science & Engg. Deptt. Dehradun institute of technology Dehradun, Ashish Pant, Assistant Professor; Computer Sc. & Engg dept. Dehradun Institute of Technology Dehradun Suneet Kumar, Assistant Professor; Computer Sc. & Engg dept. Dehradun Institute of technology Dehradun, India, Arjun Arora, Assistant Professor; Computer Sc. & Engg dept. Dehradun Institute of Technology, Dehradun, India. , International Journal of Advanced Research in Computer Science and Software Engineering

6.        “Using Cloud Computing for E-learning Systems”, PAUL POCATILU, FELICIAN ALECU, MARIUS VETRICI, Economic Informatics Department, Academy of Economic Studies Piata Romana, Secot 1, Bucharest, ROMANIA

7.        “E-Learning on the Cloud “, Mohammed Al-Zoube, Princess Sumaya University for Technology, Jordan.

8.        “APPLIANCE OF CLOUD COMPUTING ON E-LEARNING”, Bhruthari G. Pund, Prajakta P. Deshmukh, Prof. Ram Meghe Institute Of Technology, Badnera, Amravati, Maharashtra.

9.        “Cloud Computing Benefits  for E-learning Solutions”, Paul POCATILU, PhD, Associate Professor, Department of Economic Informatics, Academy of Economic Studies, Bucharest.

10.     “An E-learning System Architecture based on Cloud Computing”, Md. Anwar Hossain Masud, Xiaodi Huang, World Academy of Science, Engineering and Technology 62 2012

11.     Cloud Computing Issues and Benefits Modern Education, By D.Kasi Viswanath, S.Kusuma & Saroj Kumar Gupta, Madanapalle Institute of Technology and Science Madanapalle, Chittoor




Mohammadreza Ghorbaniparvar, Fatemeh Ghorbaniparvar

Paper Title:

Portfolio Optimization Applied For Wholesale Electricity Spot Market (WESM) Based On Markowitz Theory

Abstract: With the introduction of deregulation, the electricity market has turned from a monopoly market to a free market, while electric power distributor companies are facing a problem of designing the optimal portfolio in a competitive electricity market. Notionally, the portfolio selection problem can be solved by assigning requirement capacities to the spot market and bilateral contracts. This paper objective is to introduce a novel approach in order to address the electric power distributor companies’ portfolio selection problem. Since electricity pricing is volatile and there is no ways to store electricity, this portfolio varies from a financial portfolio. The mathematical formulations and forecasted price of different asset returns for both the long term and the spot market portfolio selection have been derived according to Markowitz Modern Portfolio Theory. Moreover, we applied the data which comes from Manila Electric Railroad And Light Company (MERALCO) for different assets in this paper. Multiple Linear Regression Considering Explanatory Variables is employed to forecast the price of the spot market which is Wholesale Electricity Spot Market (WESM) in this paper. The portfolio selection problem for MERALCO is finally formulated as optimization problem, which can be solved by Genetic Algorithm (GA) in MATLAB and Microsoft Office Excel.

Portfolio Selection, Spot Electricity Market, Forward Contract, Futures Contract, GA.


1.        M. Shahidehpour, H. Yamin, and Z. Li, Market operations in electric power systems : forecasting, scheduling, and risk management. NewYork: IEEE : Wiley-Interscience, 2002.
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7.        I. Vehvilainen and J. Keppo, “Managing electricity market price risk”,Eur. J. Oper. Res., vol. 145, no.1, pp. 136-147, Feb 2003.

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9.        M. Liu, F.F. Wu, and Y. Ni, “Market allocation between bilateral contracts and spot market without financial transmission rights”, in Proc. IEEE Power Eng. Soc. Summer Meeting, 2003, vol.2, pp.13-17.

10.     D. Feng, D. Gan, J. Zhong, and Y. Ni, “Supplier asset allocation in a pool-based electricity market”, IEEE Trans. Power Syst., vol.22, no.3, Aug 2007.

11.     Z. Bodie, A. Kane, A.J. Marcus, Investments, fourth ed., Irwin/McGraw-Hill, Chicago, 1999.

12.     H.M. Markowitz, Portfolio selection, J. Finance 7 (1952) 77–91.

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14.     Holland, J. H., Adaptation in Natural and Artificial System. 1975, Ann Arbor: The University of Michigan Press.

15.     Yao, X., “Evolving Artificial Neural Networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423-1447, 1999.

16.     Edward Olmedo .Price forecasting for WESM using Multiple Linear Regression Considering Explanatory Variables. Master thesis, Mapua Institute of technology. (2011)

17.     [Online] Available: Check for Manila Electric Railroad And Light Company

18.     Z. Bodie, A. Kane, A.J. Marcus, Investments, Boston: Irwin/McGraw-Hill, 1999

19.     [Online] Available: Check for Wholesale Electricity Spot Market.




K. Sahitya Yadav, K. Sumanth

Paper Title:

Analyzing Transformer Core Faults by Using Real-Rational Polynomial Function Model From FRA Data

Abstract: The paper presents the results of   the experimental investigation carried out on a transformer to obtain frequency response data under core faults.  These core faults were physically simulated to study and identify the various parameters that influence the frequency responses. Transfer Function using real-rational polynomial function model was computed from the frequency response data. Various transfer function parameters were computed for reference and simulated faulty frequency response data. These parameters are then analyzed to relate changes to characterize the defects. The analysis presented based on the transfer function characteristic parameter changes will help in diagnosing transformer core faults.

Frequency Response Analysis; Real-rational polynomial; Transfer Function; core faults.


1.        J. Lapworth and T.McGrail, “Transformer winding movement detection by frequency response analysis,” Proc. 66th Annual International Conference of Doble Clients- April, 1999, Boston, USA.
2.        J.W. Kim, B. Park, S.C. Jeong, S.W. Kim, and P. Park, “Fault diagnosis of a power transformer using an improved frequency response analysis,” IEEE Trans. on Power Delivery, 20, (21), pp.169-178, Jan. 2005

3.        Leibfred,T., and Feser, K.: “Monitoring of power transformers using the transfer function method”, IEEE Trans. on Power Delivery, Vol.14, No.4, pp. 1333-1341, October 1999.

4.        Dick, E.P., and Erven, C.C.: ‘Transformer diagnostic testing by frequency response analysis’, IEEE Trans. on Power App. and Syst., Nov.-Dec.1978, PAS-97, (6), pp. 2144-2153

5.        CIGRE SC12 Transformer Colloquium, Summary on behalf of Study Committee 12, Budapest, 14 -16 June 1999.

6.        D.M. Sofian, Z.D. Wang, J.A.S.B. Jayasinghe, P.N.Jarman and S.A.Ryder, “Analysis and interpretation of Transformer FRA measurement Results using Transfer Function Estimation,” Proc. XIV ISH, Tsinghua University, Beijing, China, August 25-29, 2005.

7.        CIGRE Working Group-A2.26 document on  “Mechanical-Condition Assessment Of Transformer Windings Using Frequency Response Analysis (FRA)”, 2008

8.        Users Guide with MATLAB, The Mathworks, Inc, 2006.




Gopal Pandey, Swati Patel, Vidhu Singhal, Akshay Kansara

Paper Title:

A Process Oriented Perception of Personalization Techniques in Web Mining

Abstract: Web personalization is an approach, a marketing tool and a fine art. With the rapid development of Deep Web, a large number of web information often lead to "information overload" and "information disorientated ", yet, personalized techniques can solve this problem. Personalized techniques are one such software tool used to help users obtain recommendations for unseen items based on their preferences. The commonly used personalized techniques are content based filtering, collaborative filtering and rule based filtering. In this paper, we present a survey on a personalized collaborative filtering method combining the association rule mining focusing on the problems that have been identifying and the solution that have been proposed.    

Association rule mining, collaborative filtering, personalization, web mining, web usage mining.


1.        Jiawei Han, Micheline Kamber, “Data mining concepts and techniques”,    Elsevier Inc., Second Edition, San Francisco, 2006
2.        Charalampos Vassiliou, Dimitrios Stamoulis, Anastasios, “Creating     Adaptive Web Sites Using Personalization Techniques: A Unified, Integrated Approach and the Role of Evaluation”, Greece, Idea Group Publishing, 2003, pp. 261-285,ch 12

3.        Jaideep Srivastava, Robert Cooleyz, Mukund Deshpande, Pang-Ning Tan proposed “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data”, 2000.

4.        Yogita S. Pagar, Vishakha. R. Mote, Rahul S. Bramhane, “Web Personalization using Web Mining Techniques”, Emerging Trends in Computer Science and Information Technol2012 (ETCSIT2012)

5.        Liana Razmerita, Thierry Nabeth, Kathrin Kirchner, ”User Modeling and Attention Support: Towards a Framework of Personalization Techniques”, The Fifth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services, 2012

6.        Elnaz Davoodi, Keivan Kianmehr, Mohsen Afsharchi, “A semantic social network-based expert recommender system”, Springer Science Business Media, LLC 2012

7.        Ms.Kavita D.Satokar, Mr.S.Z.Gawali, “Web Personalization Using Web Mining”, International Journal of Engineering Science and Technology Vol. 2(3), 2010, 307-311.

8.        Xiaoyuan Su and Taghi M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques”, Hindawi Publishing Corporation Advances in Artificial Intelligence Volume 2009, Article ID 421425, 19 pages

9.        Hongwu Ye, “A Personalized Collaborative Filtering Recommendation Using Association Rule Mining and Self-Organizing Map”, JOURNAL OF SOFTWARE, VOL. 6, NO. 4, APRIL 2011

10.     Rahul Mishra, Abha Choubey, “Comparative Analysis of Apriori Algorithm and Frequent Pattern Algorithm for Frequent Pattern Mining in Web Log Data”, International Journal of Computer Science and Information Technologies, Vol. 3 (4) , 2012,4662 – 4665

11.     Sanjeev Rao, Priyanka Gupta, “Implementing Improved Algorithm over APRIORI Data Mining Association Rule Algorithm”, IJCST Vol. 3, Issue 1, Jan. - March 2012

12.     B.Santhosh Kumar, K.V.Rukmani, “Implementation of Web Usage Mining Using APRIORI and FP Growth Algorithms”, Int. J. of Advanced Networking and Applications 400 Volume:01, Issue:06, Pages: 400-404 (2010)

13.     [Online]Available:

14.     [Online]Available:




Utpal Bhattacharjee

Paper Title:

Environment and Sensor Robustness in Automatic Speech Recognition

Abstract: Most of the presently available speech recognition systems work efficiently only in some ideal conditions. This is due to the fact that these systems are based on some assumptions related to the operating conditions. The system works efficiently if the actual working environment is identical with the environment for which the system is built. Performance of the speech recognition system considerably degrades if mismatch between the training and the testing environment occurs. In the present study, mismatch due to sensor variability and environment has been considered and Cepstral Mean Normalization (CMN) and Spectral subtraction methods have been investigated as front-end methods for the reduction of noise. A Hidden Markov Model (HMM) based speech recognition system has been built with Mel-Frequency Cepstral Coefficient (MFCC) as feature vector. It has been observed that there is a 15% enhancement of system performance in channel and environment mismatched condition compared to baseline performance when CMN and spectral subtraction methods have been applied for noise reduction.

Robust Speech Recognition, MFCC, CMN, Spectral Subtraction.


1.        Z. Junhui, X. Xiang and K. Jingming, Noise Suppression Based on Auditory-Like Filters for Robust Speech Recognition, Proc. ICSP’02, 560-563, 2000.
2.        Steven F. Boll, Suppression of Acoustic Noise in Speech using Spectral Subtraction, IEEE Transaction on ASSP, 27(2), 113-120, 1979.

3.        Hossan, M.A.; Memon, S.; Gregory, M.A.; , "A novel approach for MFCC feature extraction," Signal Processing and Communication Systems (ICSPCS), 2010 4th International Conference on , vol., no., pp.1-5, 13-15 Dec. 2010

4.        Patel, I.; Rao, Y.S.; , "Speech Recognition Using Hidden Markov Model with MFCC-Subband Technique," Recent Trends in Information, Telecommunication and Computing (ITC), 2010 International Conference on , vol., no., pp.168-172, 12-13 March 2010.

5.        L.R. Rabiner, A Tutorial on Hidden Markov Model and Selected Application in Speech Recgnition, Proc. of IEEE, Vol. 77, No. 2, PP. 257-285, 1989.

6.        Ashraf, J.; Iqbal, N.; Khattak, N.S.; Zaidi, A.M.; , "Speaker Independent Urdu speech recognition using HMM," Informatics and Systems (INFOS), 2010 The 7th International Conference on , vol., no., pp.1-5, 28-30 March 2010

7.        D. Van Compernolle, Noise Adaptation in a Hidden Markov Model Speech Recognition System, Computer Speech and Language, 152-167, (1989).

8.        Nehe, N.S.; Holambe, R.S.; , "Isolated Word Recognition Using Normalized Teager Energy Cepstral Features," Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT '09. International Conference on , vol., no., pp.106-110, 28-29 Dec. 2009.

9.        Longbiao Wang; Kitaoka, N.; Nakagawa, S.; , "Robust Distant Speech Recognition by Combining Position-Dependent CMN with Conventional CMN," Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on , vol.4, no., pp.IV-817-IV-820, 15-20 April 2007

10.     Molau, S.; Hilger, F.; Ney, H.; , "Feature space normalization in adverse acoustic conditions," Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on , vol.1, no., pp. I-656- I-659 vol.1, 6-10 April 2003




Miteshkumar Shaileshbhai Parmar, Arvind D. Meniya

Paper Title:

Imperatives and Issues of IPSEC Based VPN

Abstract: VPN is Virtually connected networks. It is widely accepted technology for corporate world for enhancing their business. IPSEC  is standard for securing packet transmission over public networks. IPSEC private network layer security and more suitable for VPN technology. In VPN network which are mainly using public network(internet) required more secure mechanism for data transmission between to node or host(Gatewayes). This article extensively and exclusively studies the issues involved in IPSEC base VPN network. and possible solution for application base protocol implementation which can be exploded as further research purpose.

Authentication  Header  (AH),  Encapsulating  Security  Payload(ESP),  IP  Security  (IPSec),  Tunnel,  Transport, Virtual  PrivateNetworks (VPN), Quality of Service (QoS).


1.        Mr. Hitesh dhall, Ms. Dolly Dhall, Ms. Sonia Batra, Ms. Pooja Rani IMPLEMENTATION OF IPSEC PROTOCOL 2012 Second International Conference on Advanced Computing & Communication Technologies978-0-7695-4640-7/12
2.        RFC 2401, Security Architecture for the Internet Protocol, provides an overview of IPsec. The RFC is available for download at
3.        AH is IP protocol number 51. The AH version 2 standard is defined in RFC 2402, IP Authentication Header, available at 
4.        Olalekan Adeyinka Analysis of problems associated with IPSec VPN Technology 2008 978-1-4244-1643-1/08
5.        ESP is IP protocol number 50. The ESP version 2 standard is defined in RFC 2406, IP Encapsulating Security Payload (ESP), available at 
6.        D. Harkins and D. Carrel, “The Internet Key Exchange (IKE),” RFC 2409 (Proposed Standard), Internet Engineering Task Force, Nov. 1998.
7.        Ankur  Lal,  Dr.Sipi  Dubey,Mr.Bharat  Pesswani  "Reliability  of  MANET  through  the  Performance Evaluation  of  AODV,  DSDV,  DSR  "International        Journal of Advanced Research  in Computer Science and Software         Engineering Vol. 2, No.  5,May  2012,pp. 213-216.
8.        D. Harkins and D. Carrel, “The Internet Key  Exchange (IKE),” RFC 2409 (Proposed Standard), Internet Engineering Task Force, Nov. 1998.
9.        The Network Simulator-ns-2,  http:// www. Isi. Edu / nsnam/ns/index.html.
10.     Muhammad Awais Azam,  Zaka-Ul-Mustafa, Usman Tahir, S. M. Ahsan, Muhammad Adnan Naseem, Imran Rashid, Muhammad Adeel” Overhead Analysis of Security Implementation Using IPSec “
11.     S. P. Meenakshi S. V. Raghavan “Impact of IPSec Overhead on Web Application Servers”
12.     Ritu Malik Rupali Syal “Performance Analysis of IP Security VPN “International Journal of Computer Applications (0975 – 8887)  Volume 8– No.4, October 2010




Hemanshu A. Patel, Arvind D. Meniya

Paper Title:

A Survey on Commercial and Open Source Cloud Monitoring

Abstract: Cloud Monitoring plays a crucial role in providing application guarantees like performance, availability, and security. We can understand cloud computing as technologies that rely on the Internet to satisfy the computing needs of users, who do not generally own the physical infrastructure. All services are often provided by a third party with several common business applications online. Users can choose the services they want and access them from a web browser, while the software and data are stored on third party's company. Cloud Monitoring is an Integral part of maintenance. Requirement for a monitoring solution for cloud are totally different from legacy and virtualized Monitoring Environment .There are many third party solutions are available for cloud monitoring. But there is lack of standard model which covers all required parameter needed to be covered in solution so that an exhaustive report can be produced for service provider. This paper is intend to provide a brief introduction of cloud computing with cloud monitoring fundamental and its requirement including EUCALYPTUS an open source  software framework and other related framework needed to implement cloud solution.

AmzoneEC2, Eucalyptus, GoGrid, Microsoft Azure, Monitoring,OpenNebula, RackSpace.


1.        A Survey on Open-source Cloud Computing Solutions Patrícia Takako Endo , Glauco Estácio Gonçalves, Judith Kelner, Djamel Sadok.Universidade Federal de Pernambuco – UFPE
2.        OpenNebula Tutorial, Constantino Vázquez Blanco Borja Sotomayor , Distributed Systems Architecture Research Group ,Universidad Complutense de Madrid

3.        Cloud Computing with Nimbus. XtreemOS Summer School 2009. Oxford, September 2009. Kate Keahey,

4.        The Eucalyptus Open-source Cloud-computing System Daniel Nurmi, Rich Wolski, Chris Grzegorczyk Graziano Obertelli, Sunil Soman, Lamia Youseff, Dmitrii Zagorodnov Computer Science Department University of California, Santa Barbara Santa Barbara, California93106

5.        A Performance Guarantee Approach for Cloud Applications Based on Monitoring Jin Shao, Qianxiang Wang School of Electronics Engineering and Computer Science Peking University Beijing, China.

6.        Amazon Elastic Compute Cloud: User Guide Amazon Web Services Copyright © 2012 Amazon Web Services, Inc. and/or its affiliates

7.        Windows Azure™ Security Overview By Charlie Kaufman and Ramanathan Venkatapathy

8.        Cloud Infrastructure Service Management – A Review Department of Computer Science and Engineering, SCT Institute of Technology, Visvesvaraya Technological University  Bangalore, Karnataka, India





Shervan Fekri-Ershad, Hadi Tajalizadeh, Shahram Jafari

Paper Title:

Design and Development of an Expert System to Help Head of University Departments

Abstract: One of the basic tasks which is responded for head of each university department, is employing lecturers based on some default factors such as experience, evidences, qualifies and etc. In this respect, to help the heads, some automatic systems have been proposed until now using machine learning methods, decision support systems (DSS) and etc. According to advantages and disadvantages of the previous methods, a full automatic system is designed in this paper using expert systems. The proposed system is included two main steps. In the first one, the human expert’s knowledge is designed as decision trees. The second step is included an expert system which is evaluated using extracted rules of these decision trees. Also, to improve the quality of the proposed system, a majority voting algorithm is proposed as post processing step to choose the best lecturer which satisfied more experts’ decision trees for each course. The results are shown that the designed system average accuracy is 78.88. Low computational complexity, simplicity to program and are some of other advantages of the proposed system.

Keywords:  Expert system, Rule based system, Decision tree, Head of University Department.


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