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Uma Maheswari, V.
- Task Scheduling Model
Authors
1 S. A. Engineering College, Chennai, IN
2 Department of Information Science and Technology, Anna University, Chennai, IN
3 Department of Computer Science and Engineering, Anna University, Chennai, IN
Source
Indian Journal of Science and Technology, Vol 8, No S7 (2015), Pagination: 33-42Abstract
To design and implement a task scheduling model which predicts a schedule for a new task set without actually running a task scheduling algorithm. Generating an optimal schedule of tasks for an application is critical for obtaining high performance in a heterogeneous computing environment and it is a hard problem. This work attempts to optimize on the scheduling time by designing a task scheduling model. The task scheduling algorithm used in this work is based on ACO, a swarm intelligence model. The prediction is done after the training phase of the model. The model is validated by comparing the predicted schedule with the actual schedule obtained by running the ACO scheduling algorithm on the new task set. The parameters used for comparison are waiting time of tasks, average processor utilization and the scheduling time. The predicted schedule is comparable to the actual schedule with respect to waiting time of tasks and average processor utilization. The scheduling time is significantly reduced and the reduction in the scheduling time increases with the increase in the task set size.Keywords
ACO (Ant Colony Optimization), Ant Systems, Clustering, Heterogeneous Multiprocessors, Optimization Techniques, Task Scheduling.Full Text
- Anaemia and Work Output of Farm Women
Authors
1 Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore-641 043, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 24, No 9 (1987), Pagination: 253-259Abstract
By the end of the decade the developing countries population will increase from roughly three quarters to four fifths of the world's total population according to the estimates of Hulse.
- A Survey on E-Learning Personalization Techniques Using Data Mining
Authors
1 Department of Computer Applications (UG), Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 8, No 9 (2016), Pagination: 274-278Abstract
Web based education System (E-learning) has the tremendous growth in current learning scenario. Such systems utilize several data mining techniques and tools to evaluate the knowledge level of every user. This survey explores the impact of data mining techniques in adaptive e-learning environment. Implementation adaptive E-learning environment with personalized knowledge evaluation is a challenging task. In this paper, we explore different techniques and methods, which used in such environment and modern e-learning environment. Finally, we list the comparisons of these schemes by some criteria for Web based education system. By applying the most appropriate Data mining techniques on personalized e-learning environment will bring better solution, so based on the comparison, our system gives optimum way to achieve high accuracy in e-learning recommendation.