Finding Resilience-Friendly Compiler Optimizations Using Meta-Heuristic Search Techniques

Scheduling lecture is scheduled number of components consisting of courses, lecturer, students, classrooms, and time with a number of restrictions and requirements (constraints) certain to get optimal results and the best. In this paper will be discussed and created scheduling lecture with a problem-solving approach to the science of Artificial Intelligence (Artificial Intelligence), by using an approximation of the mathematical problem that is aiming to find a situation or object that meets a number of requirements or specific criteria (Constraint Satisfaction Problem) to get the optimal scheduling and the best. To solve these problems the solution search techniques used by an algorithm that will result in optimal scheduling and the best (heuristic search) techniques combined with Smart Backtracking and Look Ahead called Intelligent Search to find and resolve problems when encountered a condition where no there is a solution in due course scheduling constraints and requirements are not met (deadlock). The application of these methods and techniques in the course scheduling information system is built, using the PHP programming language and MySQL database to solve the problem of scheduling to get optimal results and the best.

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Heuristic search techniques for cell to switch assignment in location area planning for cellular networks

2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577) ◽

10.1109/icc.2004.1313360 ◽

2004 ◽

Cited By ~ 10

Author(s):

S. Mandal ◽

D. Saha ◽

A. Mahanti

Keyword(s):

Cellular Networks ◽

Heuristic Search ◽

Search Techniques ◽

Location Area ◽

Area Planning

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Heterogeneous transfer learning for activity recognition using heuristic search techniques

International Journal of Pervasive Computing and Communications ◽

10.1108/ijpcc-03-2014-0020 ◽

2014 ◽

Vol 10 (4) ◽

pp. 393-418 ◽

Cited By ~ 11

Author(s):

Kyle Dillon Feuz ◽

Diane J. Cook

Keyword(s):

Transfer Learning ◽

Activity Recognition ◽

Heuristic Search ◽

Care Facility ◽

Feature Space ◽

Training Data ◽

Transfer Model ◽

Content Type ◽

Search Techniques ◽

Learning Techniques

Purpose – The purpose of this paper is to study heterogeneous transfer learning for activity recognition using heuristic search techniques. Many pervasive computing applications require information about the activities currently being performed, but activity recognition algorithms typically require substantial amounts of labeled training data for each setting. One solution to this problem is to leverage transfer learning techniques to reuse available labeled data in new situations. Design/methodology/approach – This paper introduces three novel heterogeneous transfer learning techniques that reverse the typical transfer model and map the target feature space to the source feature space and apply them to activity recognition in a smart apartment. This paper evaluates the techniques on data from 18 different smart apartments located in an assisted-care facility and compares the results against several baselines. Findings – The three transfer learning techniques are all able to outperform the baseline comparisons in several situations. Furthermore, the techniques are successfully used in an ensemble approach to achieve even higher levels of accuracy. Originality/value – The techniques in this paper represent a considerable step forward in heterogeneous transfer learning by removing the need to rely on instance – instance or feature – feature co-occurrence data.

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