Mukta Goyal

Intuitionistic Group Decision Making to Identify the Status of Student's Knowledge Acquisition in E-Learning Systems

Learners performance evaluation in an E-learning environment is a multi-criteria decision problem and important to personalize the sequence of learning concepts according to their knowledge level. Crisp responses, leads uncertainty in the evaluation process due to successful guesses or choosing a more probable answer. Analysis of learners response to a complex/subjective questions needs more effort. Moreover, due to uncertainty and imprecise nature of learner, traditional methods are inadequate to assess, how much time he has spent on studying the learning contents and also the number of backtracking he followed. This paper proposes an intuitionistic fuzzy multicriteria decision making that investigates the use of an intuitionistic fuzzy technique for order preference by similarity to ideal solution TOPSIS method for the evaluation of student in an E-learning environment. Synthetic data are created for the criterias that affect the student evaluation in E-learning domain and thereafter results are evaluated.

Emerging information technology and contemporary challenging R & D problems in the area of learning: An artificial intelligence approach

Access to information and electronic resources is a main hurdle faced by e-learners. In addition, it is difficult to find learning materials that best suit students needs while tutors need to find an automatic way for receiving feedback from students. Therefore, the development of efficient new procedures to better guide students for selecting suitable learning materials is becoming a challenging factor in the e-Learning environment.

Fuzzy logic approach for adaptive test sheet generation in e-learning

Good quality test refers to the ability of questions to test the students cognitive abilities in the most efficient manner. The task of designing and developing a question paper for an adaptive e-learning system depends on organization of questions, concept, activity and learner level. In order to perform a better adaptation or Personalization we propose an e-test classify based on Blooms classification, also known as taxonomy of learning objective for different type of learner model like rote learner, analytical learner, example base learner, analogy and deductive learner and commonsense based learner. In this paper we use fuzzy approach to evaluate student preferable test in e-learning environment.

Fuzzy approach to detect learning style using McCarthy model as a tool for e-learning system

This study suggests a soft computing approach to extract knowledge which can decide learners learning style in e-learning environment. The approach uses the rules of the learning style instrument(LSI) designed by Marlene Lefever which is based on the McCarthy Model. There are e-learning system which detect the learning style of a particular learner in a crisp manner. Due to the fuzzy nature of the learner a fuzzy approach is used to decide the learning style of the learner. The proposed method has used the McCarthy Model as a tool to detect the learning style of the student. This increase the efficiency of the learning because most of the systems are based on the notion that each student has only one learning style which is not true.

Intuitionistic fuzzy ant colony optimization for course sequencing in E-learning

In state-of-art E-learning scenarios, adaptive courses sequencing is important to save the learner loss in hyperspace. Learning material is a unstructured place in hyperspace and adaptive course sequencing light the path to learners for selecting appropriate courses with their knowledge levels. In this work, a method based on adaptive content sequencing using facultys personal strength is proposed for providing the most suitable content out of the overloaded course material. Further, learning path is optimized using ant colony optimization on Intuitionistic fuzzy data. Results show that objectives of better contents development, coherence among courses, better teaching and learning is achievable by selecting best path in DAG traversal using proposed framework.

Load Optimization in Femtocell Using Iterative Local Search Heuristics in an Enterprise Environment

Femtocells are required to connect the end users and also provide better services to access various applications such as web 2.0 services, mobile multimedia, etc. The major challenge for mobile network operators is, to provide such services with better coverage and capacity to its end users. This requires efficient load balancing of femtocells corresponding to number of users, such that, the cellular network can provide services to the end users in smooth manner. This paper presents a heuristic search technique to optimize the load on femtocells to provide better services to the end users.

Location-Based Services: Current State of The Art and Future Prospects

As technology evolves, the use of smartphones for personal work, shopping, learning, entertainment is growing day by day. One of the major applications of mobile devices is in the area called location-based services (LBS). LBS is a software-oriented service provided by smartphones that perform control operations using location-based data. The objective of this paper is twofold—first, to study deeper about existing LBS methodologies and next to analyze the applications of LBS in various fields. Further, this work focuses on comparative analysis of LBS based upon different attributes like interoperability, extendibility, scalability, and flexibility. For the purpose of comparison, many research papers are considered and studied analytically. Finally, in order to emphasis our analysis, for methods of location-based services and their applications, a novel application GoClues is developed for tourism industry.

A simulation annealing based anti-collision protocol for RFID tag identification

The advanced properties like low cost, increased communication range, own battery source etc. preferred Radio frequency identification (RFID) over bar codes for object identification and tracking. In RFID systems, reader-to-reader, tag-to-tag, reader-to-tag collisions are major challenges during object identification. The performance analysis of state-of-art protocols shows that the majority of probabilistic and deterministic algorithms are inefficient. In this work, the proposed protocol uses simulated annealing mechanism which concentrates on parameters of physical state of the RFID readers while selecting the reader for interrogation. If readers with best physical state are selected for interrogation then chances of better network performance also increases. The physical state parameters include energy efficiency, mobility, distance to tag etc. The simulation analysis of proposed scheme shows better performance with parameters like tag identification ratio, total successful interrogation cycles, total time cycles, total IDLE cycles, total number of collisions, delay, and total number of packets sent and received. It is also observed that a collision free RFID network is possible with integration of simulated annealing mechanism.

Cognitive Models and Its Current Challenges

Cognitive learning and learner’s mind processes play important roles in education. It is a process of acquiring new habits, knowledge and skills which together enable the students to do something they could not have done before. Some of the theories are bloom’s taxonomy, information processing theory and critical thinking. Students understand and remember better if they can fit their learning into a framework and are motivated to learn if that framework fits into what they understand as their ultimate goal. Here we reviewed some of the cognitive models which may support students to learn the learning content through the learning management system. In this context, the student performs the user role while using the cognitive model as a tool to learn and take part in the subject in a more effective way.