i-manager Publications

i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 1

An OptiAssign-PSO Based Optimisation for Multi-Objective Multi-Level Multi-Task Scheduling in Cloud Computing Environment
Advanced Analytics for Disease Tracking & Remote Intravenous Injection Monitoring
DevKraft - Fueling Collaboration in Coding Challenges
Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions
Blockchain Healthcare Management using Patients

Most Cited

Volume 1 Issue 3 May - July 2014

Article

Introduction to Green Computing Model for Clouds

D. R. Robert Joan*

Assistant Professor of Mathematics, Christian College of Education, Marthandam.

Joan, D. R. R. (2014). Introduction to Green Computing Model for Clouds. i-manager’s Journal on Cloud Computing, 1(3), pp.1-7. https://doi.org/10.26634/jcc.1.3.3154

Abstract

In this article, the author collects some definitions and concepts related to green computing model for clouds. Greening your computing equipment is a low-risk way for your business to not only help the environment, but also to reduce costs. Reducing energy usage, which also reduces carbon-dioxide emissions and your energy bill, is the most effective thing you can do. Green computing can be defined as the efficient use of computing resources. Computing reduced power consumption is environmentally less expensive. It is also associated with the proper use of computing resources and plays a vital role in minimizing their dangerous impact on surroundings. The strength of public and community opinion shifting in favour of environmental responsibility is also growing rapidly. Computing is also an area of human activity in which there are real environmental savings to be made, some of which can be achieved by implementing relatively straightforward practical measures with existing hardware.

References

[1]. Agrawal, N. (2011). Green Computing. Retrieved from http://www.slideshare.net/Nikunj_Agrawal/greencomputing- 6434375?related=3.

[2]. Ameera, D. (2011). Green Computing. Retrieved from http://www.slideshare.net/ameerasjaffar/greencomputing- ameera.

[3]. Assadi, S. (2012). Green Computing. Retrieved from http://www.slideshare.net/ShabaParveenAssadi/ green computing- 15243391.

[4]. Barnatt, C. (2012). Green Computing. Retrieved from http://explainingcomputers.com/green.html.

[5]. Haritha, T. (2012). Green cloud. Retrieved from http://www.slideshare.net/akhilrocker143/green-cloud- 11174972?related=2.

[6]. Janssen, (2007). Green Computing. Retrieved from http://www.techopedia.com/definition/14753/greencomputing.

[7]. Kumar, S., & Buyya, R. (2011). Green Cloud computing and Environmental Sustainability. Retrieved from http://www.cloudbus.org/papers/Cloud- EnvSustainability2011.pdf.

[8]. Kumara, V., & Anuradha. (2011). Green Computing. Retrieved from http://www.slideshare.net/praftek/ greencomputing- 9347931.

[9]. Lahiri, M., & Moseley, J. L. (2013). “Migrating Educational Data and Services to Cloud Computing: Exploring Benefits and Challenges”. Educational Technology, Vol. 53(1), pp. 20-30.

[10]. Maheshwari, V. (2013). Green Computing. Retrieved from http://www.slideshare.net/vyommaheshwari9/ green-computing-26819161.

[11]. Panda, R. (2013). “E-waste Management: A Step towards Green Computing”, International Journal of Environmental Engineering and Management, Vol. 4(5), pp. 417-424, ISSN 2231-1319, Retrieved from http://www.ripublication.com/ ijeem.htm.

[12]. Paquet, K. G. (2013). “Consumer Security Perceptions and the Perceived Influence on Adopting Cloud Computing: A Quantitative Study Using the Technology Acceptance Model”. ProQuest LLC, Ph.D. Dissertation, Capella University, ISBN: 978-1-2678-8556-2.

[13]. Sahni, N. (2011). Green Computing. Retrieved from http://www.slideshare..net/neenasahni/green- computing-ppt.

[14]. Sharma, J. (2012). Green Computing. Retrieved from http://www.slideshare.net/subtlejaya/greencomputing- 12673497.

[15]. Ullman, D. F., & Haggerty, B. (2010). “Embracing the Cloud: Six Ways to Look at the Shift to Cloud Computing”, EDUCAUSE Quarterly, Vol. 33(2) ISSN-1528-5324.

[16]. Yedavalli, Y. (2014). Green Computing. Retrieved from http://www.slideshare.net/yasaswiniyedavalli/g reen-computing-31026264.

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Research Paper

Dynamic Resource Allocation for Green Clouds

M.S.Mumtaj Zareena* , M.Mahil, N.Rupavathy*

* P.G. Scholar, Department of Computer Science and Engineering, Govt College of Engineering, Tirunelveli, India.

Assistant Professor, Department of Computer Science and Engineering, Govt.College of Engineering, Tirunelveli, India.

* P.G. Scholar, Department of Computer Science and Engineering, Govt.College of Engineering, Tirunelveli, India.

Zareena, M. S. M., Mahil, M., and Rupavathy, N. (2014). Dynamic Resource Allocation for Green Clouds. i-manager’s Journal on Cloud Computing, 1(3), 8-15. https://doi.org/10.26634/jcc.1.3.3155

Abstract

Cloud computing is an on demand service as it offers dynamic, flexible and efficient resource allocation for reliable and guaranteed services in ‘pay-as-you-use’ manner to the customers. Such a process of allocation and de-allocation of resources is the key to accommodate unpredictable demands. However, despite the recent growth of the Cloud Computing market, several problems with the process of resource allocation remain unaddressed. A system that uses virtualization technology and skewness algorithm to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use had been presented. The result reveals the achievement of both overload avoidance and green computing for systems with multi resource constraints.

References

[1]. M. Nelson, B.-H. Lim, and G. Hutchins, (2005). “Fast transparent migration for virtual machines,” in Proc. of the USENIX Annual Technical Conference, pp. 391-394.

[2]. C. Tang, M. Steinder, M. Spreitzer, and G. Pacifici, (2007). “A scalable application placement controller for enterprise data centers,” in Proc. Of the International World Wide Web Conference (WWW'07), pp. 331-340.

[3]. G. Chen, H. Wenbo, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao, (2008). “Energy-aware server provisioning and load dispatching for connection-intensive internet services,” in Proc. of the USENIX Symposium on Networked Systems Design and Implementation (NSDI'08), pp. 337- 350.

[4]. M. Isard, V. Prabhakaran, J. Currey, U. Wieder, K. Talwar, and Goldberg, (2009). “Quincy: Fair scheduling for distributed computing clusters,” in Proc. of the ACM Symposium on Operating System Principles (SOSP'09), pp. 261-276.

[5]. M. Zaharia, D. Borthakur, J. Sen Sarma, K. Elmeleegy, S. Shenker, and I. Stoica, (2010). “Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling,” in Proc. of the European conference on Computer systems (EuroSys'10), pp. 265-278.

[6]. P. Padala, K.-Y. Hou, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, and A. Merchant, (2009). “Automated control of multiple virtualized resources,” in Proc. of the ACM European conference on Computer systems (EuroSys'09), pp. 13-26.

[7]. Prasad Saripalli, GVR Kiran, Ravi Shankar R, Harish Narware and Nitin Bindal, (2011). “Load Prediction and Hot Spot Detection Models for Autonomic Cloud Computing”, in the Proc. of Fourth IEEE International Conference, pp. 397-402.

[8]. J. S. Chase, D. C. Anderson, P. N. Thakar, A. M. Vahdat, and R. P. Doyle, (2001). “Managing energy and server resources in hosting centers,” in Proc. of the ACM Symposium on Operating System Principles (SOSP'01), pp. 103-116.

[9]. D. Meisner, B. T. Gold, and T. F. Wenisch, (2009). “Powernap: eliminating server idle power,” in Proc. of the international conference on Architectural support for programming languages and operating systems (ASPLOS'09), pp. 205-216.

[10]. N. Bobroff, A. Kochut, and K. Beaty, (2007). “Dynamic placement of virtual machines for managing sla violations,” in Proc. of the IFIP/IEEE International Symposium on Integrated Network Management (IM'07), pp. 119-128.

[11]. Yexi Jiang,Chand-shing Perng, Tao Li and Rong Chang, (2012). “Self Adaptive cloud Capacity Planning” in Proc. of the IEEE 9th international conference, pp. 73- 80.

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Research Paper

Privacy Preserving Using Multi Layer Encryption forDelegated Access Control in Public Clouds

K.Nethaji Sundar Sukumar* , L. Venkateswara Reddy**

* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.

** Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.

Sukumar, K. N. S. S., and Reddy, L. V. (2014). Privacy Preserving Using Multi Layer Encryption for Delegated Access Control in Public Clouds. i-manager’s Journal on Cloud Computing,1(3), 16-20. https://doi.org/10.26634/jcc.1.3.3157

Abstract

The two layer encryption is to enforce the fine-grained access control on confidential data hosted in the cloud, based on fine-grained encryption data. Under this scenario, data owners are encrypting the data before uploading them on the cloud and re-encrypting the data whenever users change to an alternate. Here the owner is communicating with clouds and users. The better approach used is to minimize the overhead work at the data owners. So, the authors have proposed an approach by using multi layer encryption, whereas the cloud performs a fine-grained encryption. A challenging issue is how to decompose Access Control Policies (ACPs) such that the multi layer encryption can be performed. In the present context, the authors have used novel optimization algorithm and KeyGen algorithm. Also, an efficient group key management scheme, that supports ACPs to work efficiently, is also used.

References

[1]. Mohamed Nabeel and Elisa Bertino, (2014). “Privacy Preserving Delegated Access Control in Public Clouds", IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 9, pp. 2268-2280.

[2]. M. Nabeel and E. Bertino, (2012). “Privacy Preserving Delegated Access Control in the Storage as a Service Model,” Proc. IEEE Int'l Conf. Information Reuse and Integration (IRI), pp. 645-652.

[3]. M. Nabeel, N. Shang, and E. Bertino, (2013). “Privacy Preserving Policy Based Content Sharing in Public Clouds,” IEEE Trans. Knowledge and Data Eng, Vol. 25, No. 11, pp. 2602-2614.

[4]. M. Nabeel and E. Bertino, (2011). “Towards Attribute Based Group Key Management,” Proc. 18th ACM Conf. Computer and Comm. Security, pp.821-824.

[5]. M. Nabeel and E. Bertino, (2010). “Attribute Based Group Key Management,” Centre for Education and Research, pp. 1-38.

[6]. S.D.C. di Vimercati, S. Foresti, S. Jajodia, S. Paraboschi, and P. Samarati, (2007). “Over-Encryption: Management of Access Control Evolution on Outsourced Data,” Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB '07), pp. 123-134.

[7]. A. Fiat and M. Naor, (1994). “Broadcast Encryption,” Proc. 13th Ann. Int'l Cryptology Conf. Advances in Cryptology (CRYPTO '93), pp. 480-491.

[8]. A. Shamir, (1979). “How to Share a Secret,” Comm. of the ACM, Vol. 22, pp. 612-613.

[9]. M. Pirretti, P. Traynor, P. McDaniel, and B. Waters, (2006). “Secure Attribute-Based Systems,” Proc. 13th ACM Conf. Computer and Comm. Security (CCS '06), pp. 99- 112.

[10]. V. Goyal, O. Pandey, A. Sahai, and B. Waters, (2006). “Attribute-Based Encryption for Fine-Grained Access Control of Encrypted Data,” Proc. 13th ACM Conf. Computer and Comm. Security (CCS '06), pp. 89-98.

[11]. X. Liang, Z. Cao, H. Lin, and J. Shao, (2009). “Attribute Based Proxy Re-Encryption with Delegating Capabilities,” Proc. Fourth Int'l Symp.Information, Computer, and Comm. Security (ASIACCS '09), pp. 276-286.

[12]. J.-M. Do, Y.-J. Song, and N. Park, (2011). “Attribute Based Proxy Re-Encryption for Data Confidentiality in Cloud Computing Environments,” Proc. First Int'l Conf. Computers, Networks, Systems and Industrial Eng., pp. 248-251.

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Research Paper

Mobile Cloud-Based Approach for Disease Diagnosis

Gandikota Ramu* , B.Eswara Reddy**

*-** Department of Computer Science & Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapuram, India.

Ramu, G., and Reddy, B. E. (2014). Mobile Cloud-Based Approach for Disease Diagnosis. i-manager’s Journal on Cloud Computing, 1(3), 21-28. https://doi.org/10.26634/jcc.1.3.3159

Abstract

Disease diagnosis is a major issue in healthcare system; day to day new diseases are being found with different symptoms. So, identifying the disease is a big challenge for common people .The rapid development of cloud computing and android technology makes healthcare system work simple. By using these technologies, we have developed this mobile cloud based approach for disease diagnosis system. Here, the authors have proposed a mobile cloud based architecture for enabling disease diagnosis. We developed an android application for the users; it is a very user friendly application to use without any prior knowledge and the cloud server will have the details of the diseases, precautions, hospital and medical volunteers that respond to the user request. This application can predict the disease based on the given symptoms’ combination using decision trees from the training data. The main aim of this research is to predict the disease name based on the patient’s selected symptoms and provide necessary remedies and hospital details. If any extra information is needed, it suggests the medical voluntaries.

References

[1]. Sang-Joong Jung, RistoMyllylä, and Wan-Young Chung (2013). “ Wireless Machine-to-Machine Healthcare Solution Using Android Mobile Devices in Global Networks”, IEEE Sensors Journal, Vol. 13, No. 5, pp. 1419-1424.

[2]. Jieyue He, Hae-Jin Hu, Robert Harrison, Phang C. Tai, and Yi Pan, (2006). “Rule Generation for Protein Secondary Structure Prediction With Support Vector Machines and Decision Tree”, IEEE Transactions On Nanobioscience, Vol. 5, No. 1, pp. 46-53.

[3]. Xiaoliang Wang, QiongGui, Bingwei Liu, Zhanpeng Jin, Member, IEEE, and Yu Chen, (2014). “Enabling Smart Personalized Healthcare: A Hybrid Mobile-Cloud Approach for ECG Telemonitoring”, IEEE Journal of Biomedical And Health Informatics, Vol. 18, No. 3.

[4]. Huang Lin, Jun Shao, Chi Zhang, and Yuguang Fang, (2013). “CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring”, IEEE Transactions On Information Forensics And Security, Vol. 8, No. 6, pp. 985-997.

[5]. Yue Tong, Student Member, IEEE, Jinyuan Sun, Member, IEEE, Sherman S. M. Chow, and Pan Li, (2014). “Cloud-Assisted Mobile-Access of Health Data With Privacy and Auditability”, IEEE Journal Of Biomedical And Health Informatics, Vol. 18, No. 2, pp. 419-429.

[6]. AbdelghaniBenharref and Mohamed Adel Serhani, (2014). “Novel Cloud and SOA-Based Framework for EHealth Monitoring Using Wireless Biosensors”, IEEE Journal of Biomedical and Health Informatics, Vol. 18, No. 1, pp. 46-55.

[7]. Smith Tsang, Ben Kao, Kevin Y. Yip, Wai-Shing Ho, and Sau Dan Lee, (2011). “Decision Trees for Uncertain Data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 1, pp. 64-78.

[8].http://manjrasoft.com/ aneka_architecture.html

[9]. Mitchell, (1997). “Decision trees and ID3 algorithm”- T.M. Machine Learning, McGraw-Hill, Inc New York, USA

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[14].http://www.engineersgarage. com/articles/what-isandroid- introduction

[15]. Deloitte (2014). 2014 Global health care outlook : shared challenges, shared opportunities.

[16]. BoyiXu, Li Da Xu, Senior Member, IEEE, HongmingCai, Cheng Xie, Jingyuan Hu, and Fenglin Bu, (2014). “Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services”, IEEE Transactions on Industrial Informatics, Vol. 10, No. 2, pp. 1578-1586.

[17].http://en.wikipedia.org /wiki/Binary_heap

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Review Paper

Critical Review on the Challenges of Cloud ComputingImplementation in Indian Education Scenario

Sabarish.P* , Smitha.R**

* Assistant Professor in Physical Science Education, Arafa Institute For Teacher Education, Attur, Thrissur, India.

** Assistant Professor in Physical Science Education, NSS Training College, Ottapalam, India

Sabarish, P., and Smitha, R. (2014). Critical Review on the Challenges of Cloud Computing Implementation in Indian Education Scenario. i-manager’s Journal on Cloud Computing, 1(3), 29-35. https://doi.org/10.26634/jcc.1.3.3160

Abstract

Cloud computing refers to the web-based computing services that allows the users to access hardware and software applications operated and governed by an institution or organization in far-off areas. Cloud computing necessities the storing of data, user applications and sharing of information and computer resources. This technology is considered as boon, especially to the educational institutions as these institutions can streamline their administrative functions, mend the communication process among their staff members, afford global partnership and also reduce the IT infrastructure cost. Various scholarly articles are available regarding the adoption and benefits of cloud computing in educational institutions the major focus being laid on the deployment models, service providers, the educational platforms and also the efficiency of cloud computing in e-learning process. Some studies also give an insight about the implementation of these services in Indian Universities. However, there are very few studies which discuss the barriers in implementing cloud computing in educational institutions and hence the scope for further empirical study is needed in this perspective. This paper critically reviews the key benefits of deploying cloud computing in education and the major challenges in implementation and the possible measures to overcome the identified barriers.

References

[1]. Akin, C.O., Matthew, F.T., Daramola Comfort Y. (2014). “The Impact and Challenges of Cloud Computing Adoption on Public Universities in Southwestern Nigeria”. International Journal of Advanced Computer Science and Applications. Vol. 5(8), pp.13-19.

[2]. Bansal. S, Singh.S & Kumar, A. (2012). “Use of Cloud Computing in Academic Institutions”. International Journal of Computer Science and Telecommunication, Vol. 3(1), pp. 427-429.

[3]. Behrend, T., Wiebe, E., London, J. & Johnson, E. (2011). “Cloud Computing adoption & usage in community colleges”. Behaviour & Information Technology, Vol. 30(2), pp. 231-240.

[4]. Bora, U. J & Ahmed, M. (2013). “E-Learning using Cloud Computing”, International Journal of Science and Mod-em Engineering, Vol.1(2), pp. 9-13.

[5]. Chandra, D.G., Borah, M.D. (2012). “Cost benefit analysis of cloud computing in education”. International Conference on Computing, Communication and Applications, pp. 1-6.

[6]. Dan Morill. (2011). “Cloud Computing in Education”, Available at:

[7]. Dogo, Eustace M., Salami, Abdulazeez., Salman, Salim I. (2013). “Feasibility Analysis of Critical Factors Affecting Cloud Computing in Nigeria”. International Journal of Cloud Computing and Services Science, Vol. 2(4), pp. 276-287.

[8]. Erkoç, M. F. & Kert, S. B. (2011). “Cloud Computing For Distributed University Campus: A Prototype Suggestion”, In International Conference on Future of Education, Firenze.

[9]. Gital, A. Ya'u & Zambuk, F. U. (2011). “Cloud Computing: Solution to ICT in Higher Education in Nigeria”, Advances in Applied Science Research, Vol. 2(6), pp. 364-369.

[10]. Hamidon, Z. (2011). “Application of Cloud Computing in Teaching and Learning in a Post Graduate program at Open University Malaysia”, pp. 370-383.

[11]. Hashim, A. S & Othman, M. (2014). “Cloud Computing Adoption by Universities: Concepts and Review”, International Journal of Science and Research. Vol. 3(2), pp. 348-353.

[12]. Huth, A., &Cebula, J. (2011). “The Basics of Cloud Computing”, Carnegie Mellon University, pp.1-4.

[13]. Jalgaonkar, M., Kanojia, A. (2013). “Adoption of Cloud Computing in Distance Learning”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 2(1), pp. 17-20.

[14]. Jayasena, K.P.N. (2012). “How Can Cloud Computing Bridge the Digital Divide in Srilankan Education?”, International Journal of Scientific Knowledge, Vol. 1(4), pp.16-24.

[15]. Khmelevsky, Y., & Voytenko, V. (2010). “Cloud computing infrastructure prototype for university education and research”, In Proceedings of the 15th Western Canadian Conference on Computing Education.

[16]. Lakshminarayanan, R., Kumar, B. Raju, M. (2013). “Cloud Computing Benefits for Educational Institutions”, Available at:

[17]. Maaita, A.A, Muhsen, Z. F, & Nsour, A. J. (2013). “ Cloud Computing Educational Environment ”. European, Mediterranean & Middle Eastern Conference on Information Systems (EMC), pp. 1-6.

[18]. Mathew, S. (2012). “Implementation of Cloud Computing in Education-A Revolution”, International Journal of Computer Theory and Engineering, Vol. 4(3), pp. 473-475.

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i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 1

Most Read

Most Cited

Volume 1 Issue 3 May - July 2014

Introduction to Green Computing Model for Clouds

D. R. Robert Joan*

Assistant Professor of Mathematics, Christian College of Education, Marthandam.

Joan, D. R. R. (2014). Introduction to Green Computing Model for Clouds. i-manager’s Journal on Cloud Computing, 1(3), pp.1-7. https://doi.org/10.26634/jcc.1.3.3154

Abstract

References

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Dynamic Resource Allocation for Green Clouds

M.S.Mumtaj Zareena* , M.Mahil**, N.Rupavathy***

Zareena, M. S. M., Mahil, M., and Rupavathy, N. (2014). Dynamic Resource Allocation for Green Clouds. i-manager’s Journal on Cloud Computing, 1(3), 8-15. https://doi.org/10.26634/jcc.1.3.3155

Abstract

References

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Privacy Preserving Using Multi Layer Encryption forDelegated Access Control in Public Clouds

K.Nethaji Sundar Sukumar* , L. Venkateswara Reddy**

* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India. ** Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.

Sukumar, K. N. S. S., and Reddy, L. V. (2014). Privacy Preserving Using Multi Layer Encryption for Delegated Access Control in Public Clouds. i-manager’s Journal on Cloud Computing,1(3), 16-20. https://doi.org/10.26634/jcc.1.3.3157

Abstract

References

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Mobile Cloud-Based Approach for Disease Diagnosis

Gandikota Ramu* , B.Eswara Reddy**

*-** Department of Computer Science & Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapuram, India.

Ramu, G., and Reddy, B. E. (2014). Mobile Cloud-Based Approach for Disease Diagnosis. i-manager’s Journal on Cloud Computing, 1(3), 21-28. https://doi.org/10.26634/jcc.1.3.3159

Abstract

References

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Critical Review on the Challenges of Cloud ComputingImplementation in Indian Education Scenario

Sabarish.P* , Smitha.R**

* Assistant Professor in Physical Science Education, Arafa Institute For Teacher Education, Attur, Thrissur, India. ** Assistant Professor in Physical Science Education, NSS Training College, Ottapalam, India

Sabarish, P., and Smitha, R. (2014). Critical Review on the Challenges of Cloud Computing Implementation in Indian Education Scenario. i-manager’s Journal on Cloud Computing, 1(3), 29-35. https://doi.org/10.26634/jcc.1.3.3160

Abstract

References

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M.S.Mumtaj Zareena* , M.Mahil, N.Rupavathy*

* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.

** Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.

* Assistant Professor in Physical Science Education, Arafa Institute For Teacher Education, Attur, Thrissur, India.

** Assistant Professor in Physical Science Education, NSS Training College, Ottapalam, India