Abhay Bansal

Quality metrics a quanta for retrieving learning object by clustering techniques

E-learning today has shown exponential growth as it provides the potential to provide right information to the right people at right time and place, using the right medium. The atomic unit of any e-learning environment is a Learning object, a digital entity which can be used in electronic learning environment. These learning objects are stored in repositories and are managed by Learning Management Systems. However, the exponential availability of information leads to a difficult scenario like finding a particular educational resource for a learner, based on the context or based on his/her preferences. The searching through keywords or metadata will result in display of huge quantity of information. Thus there is an earnest need to identify the techniques that can provide more efficient mechanism for information retrieval. In this paper a model is being proposed that can enhance the search and delivery of a relevant learning object to a learner using quality metrics & clustering of learning objects through Self Organising Maps.

Empirical investigation of agile software development: cloud perspective

Technological advancements have contributed to more complex software demands. The Agile approach to software development is widely practiced by the software development industry as it offers faster production with a promise of better software quality. It also provides a flexible process to accommodate changes during the software development life cycle, as per dynamic user requirements. In this paper we characterize the significance of software development models and the role of Agile methodologies in todays dynamic world of technologies. The purpose of this paper is to empirically investigate the choice between two of the Agile methodologies, Scrum and Extreme Programming. The outcome of the investigation based on secondary sources and limited primary sources, reveals that current software industry practices tend to opt for Scrum-based development. This work highlights the benefits of bringing the cloud and Agile methods of software development together, to fully realize the potential of the distributed paradigm.

Delivery of learning knowledge objects using fuzzy clustering

Abstracte-Learning industry is rapidly changing and the current learning trends are based on personalized, social and mobile learning, content reusability, cloud-based and talent management. The learning systems have attained a significant growth catering to the needs of a wide range of learners, having different approaches and styles of learning. Objects delivered by these systems should provide a variety of learning content to satisfy different learners and should also have a pedagogical value than simple course content to empower learning. The Knowledge Objects of Knowledge Management Systems can be combined and delivered with existing Learning Objects of Learning Management System to provide better and more holistic user experience. Choosing a suitable object in accordance with learner category is a complex task. The paper encompasses data mining approach, fuzzy clustering technique to combine Learning and Knowledge objects based on attributes of metadata. These objects are further mapped with various learning styles and an appropriate set of objects are delivered to the learners. Thus, a personalized and more authentic learning experience is achieved emphasizing the content reusability.

PREDICTION & WARNING: a method to improve student's performance

Educational data mining is a new discipline, which aims at extracting useful information and thus knowledge from huge data sets present at Educational Institutions. The main aim for such a discipline is to improve the quality of education by analyzing every parameter that is related to it. This is a Non-Linear Problem. Machine Learning provides various algorithms and approaches to deal with problems related to determining education quality. For the present study, a prediction model based on the Radial Basis Function (RBF) is proposed and its aim is to predict marks obtained by students in a subject that is related to subjects taken during previous semesters. Based on the results of predicted performance thus obtained, students are categorized into groups and the students likely to fail are warned beforehand for improvement.

Implementing Edge Detection for Detecting Neurons from Brain to Identify Emotions

Edges of an image are considered a type of crucial information that can be extracted by applying detectors with different methodology. Edge detection is a basic and important subject in computer vision and image processing In this Paper we discuss several Digital Image Processing Techniques applied in edge feature extraction. Firstly, Linear filtering of Image is done is used to remove noises from the image collected. Secondly, some edge detection operators such as Sobel, Log edge detection, canny edge detection are analyzed and then according to the simulation results, the advantages and disadvantages of these edge detection operators are compared. It is shown that the canny operator can obtain better edge feature. Finally, Edge detection is applied to identify neurons in Brain. After this the Neurons are classified and feature vector will be calculated. KeywordsFilters, Sobel, Canny, Log, Distortion, Edge Detection Introduction (Heading 1)

Framework to leverage cloud for the modernization of the Indian agriculture system

In India, Information and Communication technology (ICT) is being used vastly as a tool in almost every sector like education, health, media, etc. But if we talk about Indian agriculture, ICT is still to be exploited for its benefits. Many initiatives have been taken by Government of India to promote and introduce ICT in agricultural field. On comparing to its counter developing countries like China, Japan, etc. where advanced ICT technologies like IOT are being implemented in agriculture sector, we find that for India still there is a long road ahead to travel. This paper proposes a cloud deployment model “Agri-assistant”, which provides agriculture related information assistance to Indian farmers living in rural areas, facing financial and connectivity constraints. The model leverages the existing Government services and mobile service to provide a solution to existing scenario with minimum burden on farmers pocket.

A Data Mining Approach to Improve Re-Accessibility and Delivery of Learning Knowledge Objects.

Today Learning Management Systems (LMS) have become an integral part of learning mechanism of both learning institutes and industry. A Learning Object (LO) can be one of the atomic components of LMS. A large amount of research is conducted into identifying benchmarks for creating Learning Objects. Some of the major concerns associated with LO are size, learning outcomes, pedagogical relevance, and amount of information it delivers to learners. With the advent of knowledge enriched learning, there is a need to create Knowledge Objects (KO) as well and combine these with LOs to create Learning Knowledge Objects (LKO), which can be delivered through an LMS, so that a more holistic knowledge bank is provided to the learners. For an effective LMS, creating a high quality LKO using an algorithm that ensures the delivery of appropriate learning material to the learners is the key issue. Smaller and relevant objects can be delivered to the student using data mining approaches, thereby helping advanced learners to improve their higher order thinking skills. Use of hierarchical clustering techniques for identifying LOs based on user needs is already established. In this paper the Shared Density Approach (SDA) is used to get cohesive clusters and handle cluster of different densities. Finding similar learning objects through clustering technique reduces the domain of search. SDA not only helps with delivery of Learning Objects from a relevant cluster, but also helps in finding objects that are closer to one another but belong to a different class. Objects can be delivered based on user learning approaches, thereby have a wider usage and thus improve re-accessibility.

A critical review of various testing techniques in aspect-oriented software systems

Software testing is a very crucial phase of the software development life cycle. In order to develop quality software using any approach such as module-oriented, object-oriented, aspect-oriented and componentbased, testing plays a crucial role. Aspect-Oriented Programming (AOP) is a relatively new software development approach and testing of software developed using this approach has not matured enough. Researchers have proposed various testing techniques for AOP. It is important to analyze existing Aspect-Oriented testing techniques in order to develop new, better, more efficient techniques. This paper presents a critical review of various existing testing techniques for AOP.

Chronic Kidney Disease analysis using data mining classification techniques

Data mining has been a current trend for attaining diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data. There are many data mining techniques like clustering, classification, association analysis, regression etc. The objective of our paper is to predict Chronic Kidney Disease(CKD) using classification techniques like Naive Bayes and Artificial Neural Network(ANN). The experimental results implemented in Rapidminer tool show that Naive Bayes produce more accurate results than Artificial Neural Network.

Investigating the inclinations of research and practices in Hadoop: A systematic review

Hadoop Technology is commonly being used to manage Big Data projects. The research done in this domain has increased over the past few years. In order to investigate whether Hadoop Technology is the next big thing we have conducted systematic literature review on the usage of Hadoop technology in Big Data projects. This work outlines the usage of Hadoop technology from year 2010 to 2013 using publications of conference proceedings, journals and magazines of IEEEXplore as primary studies. With the help of search strategy followed, we identified 690 research papers out of which 296 were identified as relevant papers. We observed that 166 studies by different authors were utilizing Hadoop Technology as tool in Big Data projects whereas only 130 studies were purely on Hadoop. This review will assist researchers to understand the present scenario of research in Big Data projects using Hadoop technology.