In recent years, the advancement in internet technologies has greatly altered the learning landscape, thus, a shift from traditional methods of learning to internet based learning platforms. E-learning, m-learning and cloud are some of the most powerful responses to these growing technological shift by the education sectors. Their impact and benefits cannot be over-emphasized with regard to making learning accessible, affordable, available and convenient. In addition, the use of cloud technology has made the world of education more integrated, networked and composite. This makes e-learning and m-learning as highly effective as the conventional method of learning delivery. However, despite these advantages, the security and the protection of learners’ data on this cloud platform have been some of the major challenges to m-learning effective implementation and use.

This paper discusses the various benefits of the using m-learning platform and cloud infrastructure in higher education. It also examines the vulnerabilities of the platform as well as other security and privacy challenges regarding the effective implementation of m-learning in cloud infrastructure environment. Finally, it proposes a detailed data protection and security framework that is needed for addressing these issues. It is expected that the proposed framework when fully implemented, will bring about necessary solution to issues relating to the security and data protection of m-learners in cloud computing environment, increase trust in the use of the system as well as enhance the m-learning platforms.

Generally, measuring the Information Security maturity(ISM) is the first step to build a new knowledge information security management system in an organization. Knowing the ISM level helps organizations decide the type of protection strategies and policies will be taken and their priorities to strengthen their competitive ability. One of the possible ways to solve the problem is a using multiple criteria decision-making (MCDM) methodology. Analytic hierarchy process (AHP) is one of the most commonly used MCDM methods, which combines subjective and personal preferences in the information security assessment process. However, the AHP involves human subjectivity, which introduces vagueness type of uncertainty and requires the use of decision-making under those uncertainties. In this paper, the IS maturity is based on hierarchical multilevel information security gap analysis model for ISO 27001:2013 security standard. The concept of fuzzy set is applied to Analytic Hierarchical Process (AHP) to propose a model for measuring organizations IS maturity under uncertain environment. Using fuzzy AHP approach helps determine more efficiently importance weights of factors and indicators, especially deal with imprecise and uncertain expert comparison judgments. A case study is used to illustrate the better new method for IS evaluation.

This computing which runs on a web browser. It provides access to a number of web applications through internet without booting the whole OS. The purpose behind Cloud Operating System is that the full system is running on the Web browser and lives on it. Cloud OS is thought of as a new era of an Operating System in which everything inside an Operating System can be accessed from everywhere inside a specific network. The user just need to login onto the web browser and thus can have access to his personalized web-tops where all the applications and data is stored.

In this paper it will discuss what is the difference between the Cloud OS and a simple Operating System and how the Cloud OS is developed defining all the requirements and functionalities of a cloud OS. We will also discuss in detail about the load balancing, Geo-replication Data Storage and Virtualization in Cloud OS.

Wireless sensor networks (WSNs) have become a popular research area that is widely gaining the attraction from both the researchers and the practitioner communities due to their wide area of applications. These include real time sensing for audio delivery, imaging, video streaming, environmental monitoring, industrial applications and remote monitoring. WSNs are constrained with limited energy due to their physical size. In order to maximize network lifetime, efficient use of limited sensor nodes energy resources is important. Energy efficient routing protocol for maximum lifetime in wireless sensor networks (EERPM) is proposed. Sensor nodes lifetime optimization models are formulated subject to energy consumption constraint, data flow conservation constraint, maximum data rate constraint and link capacity constraint. The models are used to solve mathematical models for the maximum lifetime routing problems. Sensor nodes transmit their data packets based on the link capacity that is inference free among the sets of links. Moreover, algorithms are developed for coverage of sensor nodes and maximization of lifetime for sensor nodes. Simulation results show that EERPM performs better than MLCS, MLCAL and AEEC protocols. It can reduce data gathering latency and achieve load balancing. Finally, the proposed method extends network lifetime compared to the related selected protocols.

The main task of any clustering algorithm is to produce compact and well-separated clusters. Well separated and compact type of clusters cannot be achieved in practice. Different types of clustering validation are used to evaluate the quality of the clusters generated by clustering. These measures are elements in the success of clustering. Different clustering requires different types of validity measures. For example, unsupervised algorithms require different evaluation measures than supervised algorithms. The clustering validity measures are categorized into two categories. These categories include external and internal validation. The main difference between external and internal measures is that external validity uses the external information and internal validity measures use internal information of the datasets. A well-known example of the external validation measure is Entropy. Entropy is used to measure the purity of the clusters using the given class labels. Internal measures validate the quality of the clustering without using any external information. External measures require the accurate value of the number of clusters in advance. Therefore, these measures are used mainly for selecting optimal clustering algorithms which work on a specific type of dataset. Internal validation measures are not only used to select the best clustering algorithm but also used to select the optimal value of the number of clusters. It is difficult for external validity measures to have predefined class labels because these labels are not available often in many of the applications. For these reasons, internal validation measures are the only solution where no external information is available in the applications. 

All these clustering validity measures used currently are time-consuming and especially take additional time for calculations. There are no clustering validity measures which can be used while the clustering process is going on.

This paper has surveyed the existing and improved cluster validity measures. It then proposes time efficient and optimized cluster validity measures. These measures use the concept of cluster representatives and random sampling. The work proposes optimized measures for cluster compactness, separation and cluster validity. These three measures are simple and more time efficient than the existing clusters validity measures and are used to monitor the working of the clustering algorithms on large data while the clustering process is going on.

Requirements prioritization is an essential component of software release planning and requirement engineering. In requirement engineering the requirements are arranged as per their priority using prioritization techniques to develop high-quality software’s. It also helps to the decision makers for making good decisions about, which set of requirements should be executed first. In any software development industry a ‘software project’ may have a larger number of requirements and then it is very difficult to prioritize such type of larger number of requirements as per their priority when stakeholder’s priorities are in the form of linguistic variables. This paper presents a comparative analysis of existing seven techniques based on various aspects like: scale of prioritization, scalability, time complexity, easy to use, accuracy, and decision making, etc. It was found from literature survey none of the techniques can be considered as the best one. These techniques undergo from a number of drawbacks like: time complexity, lack of scalability, Negative degree of membership function, inconsistency ratio, rank updates during requirement development, and conflicts among stakeholders. This paper proposed a model called ‘ANN Fuzzy AHP model’ for requirements prioritization that will overcome these limitations and drawbacks. In the investigation of this proposed model, a case study is implemented. Ozcan et al [31] using a FAHP (Fuzzy AHP) with ANN based technique to choose the best supplier based on the multiple criteria. The examination on ANN with FAHP is performed on MATLAB software and outcome evaluated by fuzzy pair-wise comparison matrix with three supplier selection criteria states that the requirements prioritization outcome is better from existing techniques.with higher priority.

Incorporating information and communication technology (ICT), especially computer/mobile games into teaching and learning has been identified as a proven method of increasing primary grade students’ intrinsic motivation towards learning. However, in countries like Sri Lanka with teacher centric education cultures, the teacher still plays a significant role in the child’s education process. Therefore, it is imperative to look at the teachers’ willingness and inclination to integrate technology enhanced games in their classrooms. The purpose of this study is to investigate the teachers’ preparedness, attitude towards integrating mobile games in teaching and the issues faced by the teachers when trying to use technology in the Sri Lankan primary classroom. A questionnaire for assessing mobile game based learning readiness was designed and used as the research instrument to assess the inclination of teachers to incorporate mobile-based games for learning in their classroom. The survey was conducted involving primary school teachers in four Type 3 schools of Gampaha district in Sri Lanka. Type 3 schools have classes only up to grade 8. It was identified that the teachers in Type 3 schools of Gampaha district are moderately inclined towards incorporating mobile games into their day-to-day teaching activities.

At a recent time, digital data increases very speedily from small business to large business. In this span of internet explosion, choices are also increases and it makes the selection of products very difficult for users so it demands some recommendation system which provides good and meaningful suggestions to users to help them to purchase or select products of their own choice and get benefited. Collaborative filtering technique works very productive to provide personalized suggestions. It works based on the past given ratings, behavior and choices of users to provide recommendations. To boost its performance many other algorithms and techniques can be combined with it. This paper describes the method to boost the performance of collaborative filtering algorithm by taking multiple attributes in consideration where each attribute has some weight.