Learning is based on collaborative learning theory. Collaborative learning theory has interaction, individual accountability, teamwork and personalized guidance. All these aspects can be performed in web 2.0 using social networking sites. So e-learning 2.0 on web 2.0 is not a new class of learning management system or a pedagogy but which promotes the user to collaborate and build the information and not just a mere spectator/consumer of information. In this paper the researchers have made an assessment of 23 e-learning systems, a survey on some of the popular tools/sites which will be useful and augment e-learning 2.0 and have discussed the features of an experimental web solution to have a look and feel of these tools.

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed for homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of real and benchmark data sets of recognizing totally unconstrained handwritten numerals. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase and combining phase. A wide range of comparative experiments are conducted for real and benchmark data sets of recognizing totally unconstrained handwritten numerals. The accuracy of base classifiers is compared with homogeneous and heterogeneous models for data mining problem. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and also heterogeneous models exhibit better results than homogeneous models for real and benchmark data sets of recognizing totally unconstrained handwritten numerals.

With the rapid development of E-Learning, collaborative learning is important for teaching, learning methods and strategies. Studies over the years shown that students had actively and interactively involved in a classroom discussion to gain their knowledge. Collaborative learning is able to accommodate the situation, where student can exploit and share their resources and skills by asking for information, evaluating, monitoring one another’s information and idea. Therein, the activity allowing one question has many answer or information that should be selected. Every answer has a weighting and very subjective to select. In this paper, we introduce question answering for collaborative learning with domain knowledge and answer quality predictor. By using answer quality predictor, the quality of answers could be determined. On the other side, domain knowledge could be used as knowledge about the environment in which the target information operates as a reference. Through the process of collaborative learning, the usage knowledge base will be enriched for future question answering. Further, not only the student could get answers form others but also provided by the system.

In today's IT world combination of AD (Agile Development) and CC (Cloud Computing) is a good recipe for the user needs fulfillment in efficient manners. This combination brings superiority for both worlds, Agile and Cloud. CC opportunities are optimized by AD processes for iterative software releases and getting more frequent user feedback while reducing cost. This paper analyzes the AM (Agile Methodology) processes and its benefits, issues with CC. ACD (Agile Cloud Development) approach helps a lot in overwhelming the challenges of both practices, encourages higher degree of innovation, and allows finding discovery and validation in requirements.

This paper introduces an improved ear recognition approach based on 3 dimensional keypoint matching and combining local and holistic features. At first, the 3D keypoints are detected using the shape index image. The system consists of four primary steps: i) ear image segmentation; ii) local feature extraction and matching; iii) holistic feature extraction and matching; and iv) combination of local and holistic features at the match score level. For the segmentation purpose, we use an efficient skin segmentation algorithm, to localize a rectangular region containing the ear. For the local feature extraction and representation purpose, we use the Sparse Representation based Localized Feature Extraction. For the holistic matching component, we introduce a voxelization scheme for holistic ear representation. The match scores obtained from both the local and holistic matching components are combined to generate the final match scores.

Observed images with bare eyes are always different than the acquired ones using an imaging system since the captured images are considered as the degraded versions of the original scene. These degradations may vary between image noise, lighting defects and blur. Therefore, this article addresses the field of computer forensics with image deblurring as the latent details that are indeed present in the captured images are concealed due to the blurring artifact. Moreover, the constant types of blur that are being dealt with in forensics are the motion and the out-of-focus blur. The motion blur occurs due to the motion of the recorded objects or the camera during the capturing process. The out-of-focus blur occurs due to lens defocusing errors. Different examples are provided to focus on the importance of deblurring forensic images. In addition, concise commentaries on deblurring methods, applications and blur types are deliberated for additional knowledge.

Data mining methodology can analyze relevant information results and produce different perspectives to understand more about the students’ activities. When designing an educational environment, applying data mining techniques discovers useful information that can be used in formative evaluation to assist educators establish a pedagogical basis for taking important decisions. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used for decision making in educational system.
In this study, we collected the student’s data that have different information about their previous and current academics records and then apply different classification algorithm using Data Mining tools (WEKA) for analysis the student’s academics performance for Training and placement.
This study presents a proposed model based on classification approach to find an enhanced evaluation method for predicting the placement for students. This model can determine the relations between academic achievement of students and their placement in campus selection.

This paper explores the various code smells or the so called bad code symptoms present in procedural C software. The code smells are analyzed in the light of aspect oriented programming. The intention is to handle the code smells with aspect oriented constructs as it offers more versatile decomposition techniques than the traditional modularization techniques, for software evolution and understandability. The code smells are described at the function and program level. The code smells are followed by the aspect oriented transformations that may be required in order to improve the code quality.