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 4 Issue 2 July - December 2017

Article

Cloud Computing Security Threats and Solutions

Stefan Henry* , Md Liakat Ali**

* UG Scholar, Caldwell University, New Jersey, United States.

** Assistant Professor of Computer Science, United States.

Henry, S., Ali, M. L. (2017). Cloud Computing Security Threats and Solutions. i-manager's Journal on Cloud Computing, 4(2), 1-8. https://doi.org/10.26634/jcc.4.2.14249

Abstract

The continued evolution of technology has led to a vast virtualized network of unlimited possibilities; cloud computing. Cloud computing is an internet based service that offers cloud storage, online storage, to clients. With the use of software applications, providers create an end user virtual application that provides the user with software resources along with the necessary hardware components needed to keep all data secured. Clients are given the choice of three service models when purchasing cloud service; Software as a Service, Platform as a Service, and Infrastructure as a Service. In all cloud servers, there are four different deployment models of cloud systems; the public cloud, private cloud, the hybrid cloud and the community cloud. When it comes to the use of cloud systems, even though there are large amount of advantages there are equally a large amount of serious risks that if left unchecked, could lead to serious implications to the user’s data and the provider’s cloud service. Because of the complexity of cloud computing, developing quality security measures is highly challenging. This paper provides a background on what cloud computing is; it dives into details of the service models, the deployment models, what the threats and risks are. This paper also offers notable solutions to combat the increasing threats against cloud systems.

References

[1]. Ali, M. L., Monaco, J. V., & Tappert, C. C. (2017). Keystroke Biometric studies with Hidden Markov Model and Its Extension on Short Fixed-Text Input. In Ubiquitous Computing, Electronics and Mobile Communication t h Conference (UEMCON), 2017 IEEE 8 Annual International Conference on (pp. 258-264). IEEE

[2]. Almorsy, M., Grundy, J., & Müller, I. (2016). An analysis of the cloud computing security problem. arXiv preprint arXiv:1609.01107.

[3]. Garg, S. (2017). Survey on Cloud Computing and Data Masking Techniques. Accessed December 15, 2017. Retrieved from http://academicscience.co.in/ admin/resources/project/paper/f201704041491311813. pdf

[4]. Iovan, Ş., & Iovan, A. A. (2016). Cloud Computing Security. Fiability & Durability/Fiabilitatesi Durabilitate, (1).

[5]. Krishna, B. H., Kiran, S., Murali, G., & Reddy, R. P. K. (2016). Security Issues In Service Model Of Cloud Computing Environment. Procedia Computer Science, 87, 246-251.

[6]. Mushtaq, M. O., Shahzad, F., Tariq, M. O., Riaz, M., & Majeed, B. (2017). An Efficient Framework for Information Security in Cloud Computing Using Auditing Algorithm Shell (AAS). arXiv preprint arXiv:1702.07140.

[7]. Sabahi, F. (2011, May).Cloud computing security threats and responses. In Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on (pp. 245-249). IEEE

[8]. Singh, A., & Chatterjee, K. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79, 88-115.

[9]. Thakur, K., Ali, M. L., Kopecky, S., Kamruzzaman, A., & Tao, L. (2016, November). Connectivity, Traffic Flow and Applied Statistics in Cyber Security. In Smart Cloud (SmartCloud), IEEE International Conference on (pp. 295- 300). IEEE

[10] . Thakur, K., Tao, L., Wang, T., & Ali, M. L. (2017). Cloud Computing and its Security Issues. Application and Theory of Computer Technology, 2(1), 1-10.

[11]. Zissis, D., & Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), 583-592.

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Article

An Introduction to World Leading CRM: SalesForce.com

Harshi Garg* , Uma kumari**

* M.Tech Scholar, College of Engineering and Technology, Mody University of Science and Technology(MUST), Rajasthan, India.

** Assistant Professor, College of Engineering and Technology, Mody University of Science and Technology (MUST), Rajasthan, India

Garg, H., and Kumari, U. (2017). An Introduction to World Leading CRM: SalesForce.com. i-manager's Journal on Cloud Computing, 4(2), 9-14. https://doi.org/10.26634/jcc.4.2.14579

Abstract

In the today’s world where every single person wants to keep their data secure, everyone is switching from their IT setups to cloud. Salesforce.com is the platform which is based on cloud computing which enables us in replacing the existing traditional system where software applications are installed on computer hardware. This paper proposed how the Force.com platform has been changed the traditional system into a world leading CRM. There are numerous reasons which makes IT giants that they should move towards Force.com platform. Salesforce.com is the world leading cloud provider having number of different features one can learn. This paper mainly focuses on the important aspects of Force.com platform. Some of the features are still new to the developer. The proposed work is done to make developer familiar with the editions and versions of this platform. With each release some new features are added to enhance the functionality of the platform.

References

[1]. Baek, S., Kim, K., & Altmann, J. (2014, July). Role of platform providers in service networks: The case of salesforce. com app exchange. In Business Informatics th (CBI), 2014 IEEE 16 Conference on (Vol. 1, pp. 39-45). IEEE.

[2]. Buttle, F., Ang, L., & Iriana, R. (2006). Sales force automation: review, critique, research agenda. International Journal of Management Reviews, 8(4), 213- 231.

[3]. Jagani, S. (2016). Lightning Experience vs. Salesforce Classic. Alliance Tek Empowering IT solutions. Retrieved from https://www.alliancetek.com/blog/post/2016/10/18/ lightning-experience-vs-salesforce-classic.aspx

[4]. Lyon, L. A., & Jameson, K. (2015, October). From clicks to code: Resources women use to learn to code in apex. In Visual Languages and Human-Centric Computing (VL/HCC), 2015 IEEE Symposium on (pp. 303- 304). IEEE.

[5]. Malthouse, E. C., Haenlein, M., Skiera, B., Wege, E., & Zhang, M. (2013). Managing customer relationships in the social media era: Introducing the social CRM house. Journal of Interactive Marketing, 27(4), 270-280.

[6]. Manchar, A., & Chouhan, A. (2017, February). Salesforce CRM: A new way of managing customer relationship in cloud environment. In Electrical, Computer and Communication Technologies (ICECCT), 2017 Second International Conference on (pp. 1-4). IEEE.

[7]. Parthasarathy, M. & Sohi, R. (1997). Sales-force automation and the adoption of technology innovations by sales people: theory and implications. Journal of Business and Industrial Marketing, 12(3/4), 196-208.

[8]. Parvatiyar, A., & Sheth, J. N. (2001). Customer relationship management: Emerging practice, process, and discipline. Journal of Economic & Social Research, 3(2), 1-34.

[9]. Patel, J., & Chouhan, A. (2016, October). An approach to introduce basics of Salesforce. com: A cloud service provider. In Communication and Electronics Systems (ICCES), International Conference on (pp. 1-8). IEEE.

[10]. Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167-176.

[11]. Pullarao, K., & Thirupathirao, K. (2013). A new way of developing applications in cloud environment using force. com (salesforce. com). International Journal of Computer Application, 1(3).

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Research Paper

LiDAR Point Cloud 3D Attribute Extraction and Development of PCL Based Visualization Interface

Teh Peh Chiong* , Lai Koon Chun , XAVIER, Ng Kok Leong , Ong Chu En

* Assistant Professor, Department of Electronic Engineering, Universiti Tunku Abdul Rahman, Malaysia.

Assistant Professor in the Department of PetroChemical Engineering, Universiti Tunku Abdul Rahman, Malaysia.

* Engineer, Universiti Tunku Abdul Rahman, Malaysia

_* Research Assistant, Universiti Tunku Abdul Rahman, Malaysia.

Chiong, T. P., Leong, N. K., En, O. C., Chun, L. K., and Hong, A. T. W. (2017). LiDAR Point Cloud 3D attribute extraction and development of PCL based visualization interface. i-manager's Journal on Cloud Computing, 4(2), 15-22. https://doi.org/10.26634/jcc.4.2.14250

Abstract

Handling LiDAR data have been growing ever since the high demand in automotive vehicle and surveillance system application. This paper introduces a conversion tool which allows the extraction of point cloud data while rearranging the data to fit Point Cloud Library (PCL) input format which is known as point cloud data (PCD). Scanse Sweep LiDAR is used as an initial guidance in LiDAR data acquisition where it is able to export .csv and .xyz data. Conversion such as cartesian to polar have also been included due to Scanse Sweep hardware .csv format is arranged in polar form. Moreover, converted point cloud is processed using PCL greedy fast triangulation method for 3D modelling and random sample consensus (RANSAC) algorithm used for plane segmentation which each features are combined in a single application constructed using LabVIEW.

References

[1]. Forsyth, D. A., & Ponce, J. (2002). Computer vision: A modern approach. Prentice Hall Professional Technical Reference.

[2]. Goyer, G. G., & Watson, R. (1963). The laser and its application to meteorology. Bulletin of the American Meteorological Society, 44(9), 564-570.

[3]. Kovacs, L., Zimmermann, A., Brockmann, G., Gühring, M., Baurecht, H., Papadopulos, N. A. et al. (2006). Three-dimensional recording of the human face with a 3D laser scanner. Journal of Plastic, Reconstructive & Aesthetic Surgery, 59(11), 1193-1202.

[4]. LiDAR Tutorial (2017). 6 Advantages of LiDAR technology. Retrieved from http://home.iitk.ac.in/ ~blohani/LiDAR_Tutorial/AdvantagesofLiDARtechnology. htm

[5]. Narkhede, A., & Manocha, D. (1995). Fast polygon triangulation based on seidel's algorithm. In Graphics Gems V, 394-397.

[6]. Pastra, K., Saggion, H., & Wilks, Y. (2003). Extracting relational facts for indexing and retrieval of crime-scene photographs. Knowledge-Based Systems, 16(5-6), 313- 320.

[7]. Point Cloud Library (2017). The pcd (point cloud data) file format. Retrieved from http://pointclouds.org/ documentation/tutorials/pcd%20file%20format.php

[8]. Portland State University (2017). Light detection and ranging (LiDAR). Retrieved from http://web.pdx.edu/ ~jduh/courses/geog493f12/Week04.pdf

[9]. Rusu, R. B., & Cousins, S. (2011, May). 3d is here: point cloud library (pcl). In Robotics and automation (ICRA), 2011 IEEE International Conference on (pp. 1-4). IEEE.

[10]. Sansoni, G., Trebeschi, M., & Docchio, F. (2009). State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors, 9(1), 568-601.

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Research Paper

Improving the Performance of KNN Classification Algorithms by Using Apache Spark

B. Rajesh* , Asadi Srinivasulu**

* M.Tech Scholar, Department of Software Engineering, Jawaharlal Nehru Technological University Ananthapur, Andhra Pradesh, India.

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

Rajesh, B.., and Srinivasulu, A. (2017). Improving the Performance of KNN Classification Algorithms by Using Apache Spark. i-manager's Journal on Cloud Computing, 4(2), 23-32. https://doi.org/10.26634/jcc.4.2.14382

Abstract

Data mining and machine learning are the most interesting research areas which find meaningful information from the large amount of data available, and converts into understandable form for further use. Diabetes is one of the growing diseases all over the world. Health trade professionals desire a reliable prediction system to diagnose polygenic disease. Tools and techniques available will be used to find the appropriate approaches and methods for classification of diabetes and in extracting valuable pattern. The Spark software was employed as a mining tool for diagnosing diabetes. Thus, using the spark, the performance of KNN Classification can be improved.

References

[1]. Aggarwal, C. C., Han, J., Wang, J., & Philip, S. Y. (2003). A Framework for Clustering Evolving Data Streams. In Proceedings 2003 VLDB Conference (pp. 81-92).

[2]. Bradley, P. S., Fayyad, U., & Reina, C. (1998, August). Scaling Clustering Algorithms to Large Databases. In KDD (pp. 9-15).

[3]. Burkardt, J. (2009). K-means clustering. Virginia Tech, Advanced Research Computing, Interdisciplinary Center for Applied Mathematics.

[4]. Celebi, M. E., Kingravi, H. A., & Vela, P. A. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications,40(1), 200-210.

[5]. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-54.

[6]. Gupta, H., & Srivastava, R. (2014). K-means based document clustering with automatic “K” selection and cluster refinement. International Journal of Computer Science and Mobile Applications, 2(5), 7-13.

[7]. Haraty, R. A., Dimishkieh, M., & Masud, M. (2015). An enhanced k-means clustering algorithm for pattern discovery in healthcare data. International Journal of Distributed Sensor Networks, 11(6).

[8]. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100- 108.

[9]. Helma, C., Cramer, T., Kramer, S., & De Raedt, L. (2004). Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. Journal of Chemical Information and Computer Sciences, 44(4), 1402-1411.

[10]. Jen, C., Wang, C., Jiang, B. C., Chu, Y., & Chen, M. (2012). Application of classification techniques on development an early-warning system for chronic illnesses. Expert Systems with Applications, 39(10), 8852- 8858.

[11]. Jothi, N., Rashid, N.A.A., & Husain, W. (2015). Data mining in healthcare–a review. Procedia Computer Science, 72, 306-313.

[12]. Kang, S., Kang, P., Ko, T., Cho, S., Rhee, S., & Yu, K. S. (2015). An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction. Expert Systems with Applications, 42(9), 4265-4273.

[13]. Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis (Vol. 344). John Wiley & Sons.

[14]. Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of Healthcare Information Management,19(2), 64-72.

[15]. Kohonen, T. (2001). Self-Organizing Maps (Vol. 30). Springer.

[16]. Kulis, B., & Jordan, M. I. (2011). Revisiting k-means: New algorithms via Bayesian nonparametrics. arXiv preprint arXiv:1111.0352.

[17]. MacQueen, J. (1967, June). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297).

[18]. Morr, C. E., & Subercaze, J. (2010). Knowledge management in healthcare. In Handbook of research on developments in e-health and telemedicine: Technological and social perspectives (pp. 490-510). IGI Global.

[19]. Obenshain, M. K. (2004). Application of data mining techniques to healthcare data. Infection Control & Hospital Epidemiology, 25(8), 690-695.

[20]. Soliman, T. H. A., Sewissy, A. A., & AbdelLatif, H. (2010, November). A gene selection approach for classifying diseases based on microarray datasets. In Computer Technology and Development (ICCTD), 2010 2nd International Conference on (pp. 626-631). IEEE.

[21]. Su, C. T., Wang, P. C., Chen, Y. C., & Chen, L. F. (2012). Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients. Journal of Medical Systems, 36(4), 2387-2399.

[22]. Tapia, J. J., Morett, E., & Vallejo, E. E. (2009). A clustering genetic algorithm for genomic data mining. In Foundations of Computational Intelligence Volume 4 (pp. 249-275). Springer, Berlin, Heidelberg.

[23]. Tomar, D., & Agarwal, S. (2013). A survey on Data Mining approaches for Healthcare. International Journal of Bio-Science and Bio-Technology, 5(5), 241-266.

[24]. Veloso, R., Portela, F., Santos, M. F., Silva, A., Rua, F., Abelha, A., & Machado, J. (2014). A clustering approach for predicting readmissions in intensive medicine. Procedia Technology, 16, 1307-1316.

[25]. Yang, J., Li, J., Mulder, J., Wang, Y., Chen, S., Wu, H., ... & Pan, H. (2015). Emerging information technologies for enhanced healthcare. Computers in Industry, 69, 3- 11.

[26]. Zheng, B., Yoon, S. W., & Lam, S. S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41(4), 1476- 1482.

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

Current Issue Vol. 11 Issue 1

Most Read

Most Cited

Volume 4 Issue 2 July - December 2017

Cloud Computing Security Threats and Solutions

Stefan Henry* , Md Liakat Ali**

* UG Scholar, Caldwell University, New Jersey, United States. ** Assistant Professor of Computer Science, United States.

Henry, S., Ali, M. L. (2017). Cloud Computing Security Threats and Solutions. i-manager's Journal on Cloud Computing, 4(2), 1-8. https://doi.org/10.26634/jcc.4.2.14249

Abstract

References

References

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An Introduction to World Leading CRM: SalesForce.com

Harshi Garg* , Uma kumari**

* M.Tech Scholar, College of Engineering and Technology, Mody University of Science and Technology(MUST), Rajasthan, India. ** Assistant Professor, College of Engineering and Technology, Mody University of Science and Technology (MUST), Rajasthan, India

Garg, H., and Kumari, U. (2017). An Introduction to World Leading CRM: SalesForce.com. i-manager's Journal on Cloud Computing, 4(2), 9-14. https://doi.org/10.26634/jcc.4.2.14579

Abstract

References

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LiDAR Point Cloud 3D Attribute Extraction and Development of PCL Based Visualization Interface

Teh Peh Chiong* , Lai Koon Chun **, XAVIER***, Ng Kok Leong ****, Ong Chu En *****

Chiong, T. P., Leong, N. K., En, O. C., Chun, L. K., and Hong, A. T. W. (2017). LiDAR Point Cloud 3D attribute extraction and development of PCL based visualization interface. i-manager's Journal on Cloud Computing, 4(2), 15-22. https://doi.org/10.26634/jcc.4.2.14250

Abstract

References

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Improving the Performance of KNN Classification Algorithms by Using Apache Spark

B. Rajesh* , Asadi Srinivasulu**

* M.Tech Scholar, Department of Software Engineering, Jawaharlal Nehru Technological University Ananthapur, Andhra Pradesh, India. **Associate Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Andhra Pradesh, India.

Rajesh, B.., and Srinivasulu, A. (2017). Improving the Performance of KNN Classification Algorithms by Using Apache Spark. i-manager's Journal on Cloud Computing, 4(2), 23-32. https://doi.org/10.26634/jcc.4.2.14382

Abstract

References

Full Article (HTML)

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* UG Scholar, Caldwell University, New Jersey, United States.

** Assistant Professor of Computer Science, United States.

* M.Tech Scholar, College of Engineering and Technology, Mody University of Science and Technology(MUST), Rajasthan, India.

** Assistant Professor, College of Engineering and Technology, Mody University of Science and Technology (MUST), Rajasthan, India

Teh Peh Chiong* , Lai Koon Chun , XAVIER, Ng Kok Leong , Ong Chu En

* M.Tech Scholar, Department of Software Engineering, Jawaharlal Nehru Technological University Ananthapur, Andhra Pradesh, India.

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