Preeti Gupta

Role of decision tree in supplementing tacit knowledge for Hypothetico-Deduction in higher education

With a notion to create a knowledge centric environment, this paper substantiates the inclusion of data mining technique of decision tree for supplementing Hypothetico-Deductive methodology. Presently tacit knowledge plays an important role in the formulation of testable hypothesis from a theoretical framework of dependent and independent variables, identified for the system. The introduction of decision tree in Hypothetico-Deductive methodology concretizes a path towards knowledge creation. The case of a higher education institution is considered in particular.

Opposition learning based phases in artificial bee colony

Artificial bee colony (ABC) is a recently introduced swarm intelligence algorithm (SIA). Initially only unconstrained problems were handled by ABC, which was later modified by embedding one more parameter called modified rate to handle constrained problems. Since then, ABC and its variants have shown a remarkable success in the domain of swarm intelligence optimization algorithms. The exploration capability of ABC is comparatively better than exploitation which sometimes limits the convergence rate of ABC while handling multimodal optimization problems. In this study the foraging process of two phases has been enhanced by embedding opposition based learning concept. This modification is introduced to enhance the acceleration and exploitation capability of ABC. The variant is named as O-ABC (Opposition based ABC). The efficiency of O-ABC is initially evaluated on 12 benchmark functions consulted from literature. Later O-ABC is applied for intrusion detection. The simulated comparative results have shown the competitiveness of the proposal.

Knowledge building through optimized classification rule set generation using genetic based elitist multi objective approach

Knowledge building is an important activity taken up by various organizations. The paper exemplifies the creation of a knowledge-centric environment for a non-profit sector such as a higher education. Building knowledge and thereafter using it are important aspects of knowledge-centric environment; this further helps the organization to gain competitive advantage. With the increase in popularity of genetic algorithm (GA), the technique has been used in building efficient classifiers for creating effective rule sets. The paper makes use of multi-objective genetic algorithm for building GA-based efficient classifier because classification rule mining is itself, a multi-objective problem. Knowledge expressed through classification rules help in establishing relationships between attributes that are not visible openly. The study assumes importance as curriculum planning is an important aspect of any academic institution, the knowledge derived in the form of rules residing in the knowledge base help to substantiate proper curriculum development, making a sizeable contribution toward professional growth and advancement of the students. On implementation of the findings, educational organizations will be able to institute themselves as knowledge centric.

Label Powerset Based Multi-label Classification for Mobile Applications

Nowadays, we witness plethora of mobile applications running on smartphones. These mobile applications, whether native/inbuilt or web applications, face battery and processing power bottleneck. Thus, analyzing the energy consumption and RAM usage of these mobile applications become imperative, for making these applications work in longer run. The paper adopts a multi-label classification approach to study the effect of various contributory factors on energy consumption and RAM usage of mobile applications.

Relevance Index for Inferred Knowledge in Higher Education Domain Using Data Mining

Optimizing the real-life scenarios facilitate knowledge building. Developing a knowledge model for optimizing certain output criteria enhances the benefits by many folds. Even a non-profit sector like education needs to define knowledge models that optimize their functioning and eventually help in knowledge building. Quantifying the factors determining the academic well-being of the students in any educational organization is of prime importance. The paper exemplifies the implementation of Data Mining Technique to deduce knowledge through classification rules and further assign relevance index to inferred knowledge.

Genetic-Based Weighted Aggregation Model for Optimization of Student’s Performance in Higher Education

Most of the real-life problems are optimization problems, where the aim is to develop a model that optimizes certain output criteria. Education domain though a nonprofit sector intends to optimize its functioning by adopting procedures that tend toward knowledge building. Increasing the student’s performance has always been an area of interest among these education organizations. The paper exemplifies the usage of Binary encoded genetic algorithm to model student’s performance in a course pertaining to higher education. It gives significance to the variables identified responsible, for affecting the performance of the students in the course under study. Adopting such knowledge-based activities may help the organizations to eventually establish themselves as a Knowledge Centric Higher Education Organization.