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Mobile cloud learning, a combination of mobile learning and cloud computing, is a relatively new concept that holds considerable promise for future development and delivery in the education sectors. With the mass popularity of 3G, WIFI wireless network and intelligent mobile terminal equipment (intelligent mobile phone, tablet computer, etc.), mobile learning has become one of the important ways of learning. But the traditional mobile learning mode has many disadvantages, and the mobile learning based on cloud computing is a good way to overcome the disadvantages. This paper first introduces the concept and cloud computing, then designs a structure of mobile learning system based on cloud computing and mobile learning architecture and how mobile learning differentiate from e-learning.

Cloud Computing; Mobile Learning; E-Learning; Mobile Technologies

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S. Dhanalakshmi, S. Suganya and K. Kokilavani, "Mobile Learning Using Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.102-108, 2014.

Cloud computing is based on several other computing research areas such as HPC, virtualization, utility computing and grid computing. The service oriented, loose coupling, strong fault tolerant, business model and ease use are main characteristics of cloud computing. Grid computing in the simplest case refers to cooperation of multiple processors on multiple machines and its objective is to boost the computational power in the fields which require high capacity of the CPU. In grid computing multiple servers which use common operating systems and software have interactions with each other. Grid computing is hardware and software infrastructure which offer a cheap, distributable, coordinated and reliable access to powerful computational capabilities. This paper strives to compare and contrast Cloud Computing with Grid Computing from various angles and give insights into the essential characteristics of both.

Cloud Computing, Grid Computing, Cloud services

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S. Dhanalakshmi and G. Thenmozhi, "Assessment of Grid and Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.109-113, 2014.

Cloud computing is growing rapidly, with applications almost in any area, including education. E-Learning system requires many hardware and software resources. Therefore, there is a need to redesign the educational system to meet the needs better. The advent of computers with sophisticated software has made it possible to solve many complex problems very fast and at a lower cost. This paper introduces the characteristics of the current E-Learning and then analyses the concept of cloud computing and describes the architecture of cloud computing platform by combining the features of E-Learning. This paper describes the following aspects: architecture, construction method and external interface with the model and tried introduce cloud computing to E-Learning.

Architecture, Cloud computing, E-learning, Information Technology

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Muruganandam. S, and .Sruthi .K. L.V, "Cloud Computing For E-Learning System Based On System Architecture," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.114-119, 2014.

Cloud computing is one of today's most exciting technologies due to its ability to reduce costs associated with computing while increasing flexibility and scalability for computer processes. During the past few years, cloud computing has grown from being a promising business idea to one of the fastest growing parts of the IT industry. IT organizations have expresses concern about critical issues (such as security) that exist with the widespread implementation of cloud computing. The security for Cloud Computing is emerging area for study and this paper provide security topic in terms of cloud computing based on analysis of Cloud Security threats and Technical Components of Cloud Computing.

Cloud Computing, Security, Threats, attacks, Cloud Service Provider

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V.P.Muthukumar and R.Saranya, "A Survey on Security Threats and Attacks in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.120-125, 2014.

This In today’s world, the amount of data is increasing tremendously. In order to analyze data and make decisions, data residing at different sources are integrated. Data integration is an approach to integrate data from different data sources. Data federation is a data integration strategy used to create integrated virtual view. This paper deals with various approaches of data integration to resolve semantic heterogeneity using ontology. Various ontology based data integration techniques are reviewed and issues are summarized. Different metrics and approaches are also discussed to improve the quality of the data.

Data Integration, Ontology, Semantic heterogeneity, Data quality

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Sowmya Devi L, Jai Barathi B, Hema M.S. and S. Chandramathi , "A Survey: Different Approaches to Integrate Data Using Ontology and Methodologies to Improve the Quality of Data," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.126-131, 2014.

This paper discusses the use of pervasive computing in health monitoring using computer devices with enriched database distribution to virtually bring the experts to an emergency site. It examines the vision of new field pervasive healthcare and identifies new research thrusts: Convenient communication and access to PHR (Patient Health Record), Helping experts, patients and others to navigate and locate nearest health care, convenient traffic monitoring for health experts. Some of hypothetical pervasive computing scenarios are considered and uses them to find out key capabilities that are missing from today’s systems. The paper ends with a discussion of the research necessary to develop these capabilities using embedded sensors.

Smart Ambulance, Health, Health Care Hospitals, Doctors, Mobile Ambulance, Patient Monitoring, Medical Services,Computerized Monitoring, Distributed Database, Pervasive Computing

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Jasleen Kaur and Neera Batra, "A Traffic Aware Health Monitoring Application Embedded in Smart Ambulance (THESA)," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.132-137, 2014.

[1] J. Han and M.Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, 2001. [2] S.Lloyd, “Least squares quantization in PCM”, IEEE Transactions on Information Theory, 1982, pp.129-136. [3] A. Campan and G. Serban, “Adaptive Clustering algorithms”, Advances in Artificial Intelligence, Springer, 2006. [4] G.Serban and A.Campan, “Adaptive Clustering using a Core-based Approach”, Informatica, Volume L, Number 2, 2005. [5] Charu C. Aggarwal, Philip S. Yu, “A Framework for Clustering Massive Text and Categorical Data Streams”, ACM SIAM Data Mining Conference, 2006 [6] Angie King, “Online k-Means Clustering of Non-stationary Data”, Prediction Project Report, 2012 [7] Seokkyung Chung and Dennis McLeod, “Dynamic Pattern Mining: An Incremental Data Clustering Approach”, Journal on Data Semantics, Volume 2, 2005 [8] A.M.Rajee and F.Sagayaraj Francis, “Inter Cluster Movement Estimation model based on cluster parameters”, in Proc. IEEE International Conference on Computational Intelligence and Computing Research”, 2013, pp.369-372. [9] Jain A. K, “Data Clustering: 50 Years Beyond K-means”, Pattern Recognition Letters 31(8), 2010, pp.651–666. [10] Jain A. K, Murty M. N and Flynn, P. J, “Data Clustering: A Review. ACM Computing Surveys”, 31(3), 1999, pp. 264–323.

Data Clustering; Inter Cluster Data Movement; Probabilistic Model; Un-Clustered Information

[1] J. Han and M.Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, 2001.
[2] S.Lloyd, “Least squares quantization in PCM”, IEEE Transactions on Information Theory, 1982, pp.129-136.
[3] A. Campan and G. Serban, “Adaptive Clustering algorithms”, Advances in Artificial Intelligence, Springer, 2006.
[4] G.Serban and A.Campan, “Adaptive Clustering using a Core-based Approach”, Informatica, Volume L, Number 2, 2005.
[5] Charu C. Aggarwal, Philip S. Yu, “A Framework for Clustering Massive Text and Categorical Data Streams”, ACM SIAM Data Mining Conference, 2006
[6] Angie King, “Online k-Means Clustering of Non-stationary Data”, Prediction Project Report, 2012
[7] Seokkyung Chung and Dennis McLeod, “Dynamic Pattern Mining: An Incremental Data Clustering Approach”, Journal on Data Semantics, Volume 2, 2005
[8] A.M.Rajee and F.Sagayaraj Francis, “Inter Cluster Movement Estimation model based on cluster parameters”, in Proc. IEEE International Conference on Computational Intelligence and Computing Research”, 2013, pp.369-372.
[9] Jain A. K, “Data Clustering: 50 Years Beyond K-means”, Pattern Recognition Letters 31(8), 2010, pp.651–666.
[10] Jain A. K, Murty M. N and Flynn, P. J, “Data Clustering: A Review. ACM Computing Surveys”, 31(3), 1999, pp. 264–323.

A. M. Rajee and F. Sagayaraj Francis, "A Probabilistic Estimation of Cluster Region Prone to Inter Cluster Data Movement," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.138-141, 2014.

In this paper introduced a common report of TCP congestion control and using the fast transmit over sending packets of Using AIMD Additive increase and multiplicative decrease. The host receives implicit packet loss or explicit packet mark indicate internal congestion. Concept implements AIMD to prevent from the traffic over TCP network. The acknowledgment of PACKET SENDING implements faster over TCP network using AIMD. Recent the report using Active queue management AQM has the pathological packet-dropping pattern.

AIMD; AQM; Congestion Control; Pathological Packet

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S.Kalai Selvi and A.Meena, "RECENTING ADVANCE REPORT IN TCP COGESTION CONTROL USING AIMD," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.142-145, 2014.