A new approach to image retrieval using multi-level generalized finite automata

2003 ◽  
Author(s):  
A. Soliman
Keyword(s):  
Image Retrieval ◽  
Finite Automata ◽  
New Approach ◽  
Multi Level

Efficient hybrid multi-level matching with diverse set of features for image retrieval

2020 ◽  
Vol 24 (16) ◽  
pp. 12267-12288
Author(s):  
V. Geetha ◽  
V. Anbumani ◽  
S. Sasikala ◽  
L. Murali
Keyword(s):  
Image Retrieval ◽  
Multi Level

NEW APPROACH TO MULTI-LEVEL PROCESSOR SCHEDULING

2010 ◽  
Vol 19 (03) ◽  
pp. 335-346 ◽  
Author(s):  
SAMANEH HOSSEINI SEMNANI ◽  
KAMRAN ZAMANIFAR
Keyword(s):  
Fitness Function ◽  
Systems Design ◽  
Heuristic Function ◽  
Problem Space ◽  
New Approach ◽  
Processor Scheduling ◽  
Quantum Time ◽  
Multi Level ◽  
Evaluation Parameters

The problem of finding the best quantum time in multi-level processor scheduling is addressed in this paper. Processor scheduling is one of the most important issues in operating systems design. Different schedulers are introduced to solve this problem. In one scheduling approach, processes are placed in different queues according to their properties, and the processor allocates time to each queue iteratively. One of the most important parameters of a processor's efficiency in this approach is the amount of time slices associated to each processor queue. In this paper, an ant colony optimization (ACO) algorithm is presented to solve the problem of finding appropriate time slices to assign to each processor queue. In this technique, each ant tries to find an appropriate scheduling. Ant algorithm searches the problem space to find the best scheduling. The quality of each ant's solution is evaluated using a new fitness function. This fitness function is designed according to the evaluation parameters of each processor queue and also according to the queue theory's relations. Also a heuristic function is presented which prompts ant to select better solutions. Computational tests are presented and the comparisons made with genetic algorithm (GA) and particle swarm optimization (PSO) algorithms which try to solve same problem. The results show the efficiency of this algorithm.

On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search

2010 ◽  
Vol 4 (4) ◽  
pp. 18-36 ◽  
Author(s):  
Jane You ◽  
Qin Li ◽  
Jinghua Wang
Keyword(s):  
Image Retrieval ◽  
Data Warehouse ◽  
Image Data ◽  
Image Feature ◽  
Image Indexing ◽  
Content Based Image Retrieval ◽  
New Approach ◽  
Guided Search ◽  
Dynamic Image ◽  
Face Features

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing. It also provides an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features. Experimental results confirm that the new approach is feasible for content-based image retrieval.

scholarly journals Automated Development of Individual Learning Paths on the Competency Approach

2013 ◽  
Vol 3 (4) ◽  
pp. 27
Author(s):  
Alexander Evgenevich Shukhman ◽  
Maria Vladimirovna Motyleva ◽  
Isabella Davidovna Belonovskaya
Keyword(s):  
Information System ◽  
Petri Net ◽  
Individual Learning ◽  
Educational Institutions ◽  
New Approach ◽  
Learning Path ◽  
Multi Level ◽  
Learning Paths ◽  
The Individual ◽  
Competency Level

This article proposes new approach to the development of student�??s individual learning path (ILP). The result of student�??s learning is defined as a set of competencies levels. The use of the multi-level system of generalized professional competencies allows to model ILP effectively and to automate the development of an optimal ILP. Each competency level in our model introduces some substantial units called subunits. The process of learning is a sequence of transitions from one student�??s state to another through a study of one or more courses. It is possible to present the relation of competency levels and courses in our model as Petri net. Using the Petri net model it is possible to automatically generate the individual learning path including the courses that the student needs to attend according to the set of certain competency levels. For searching of an optimum individual learning path we use algorithm based on tree of reachability for Petri net. The developed model and algorithm were used in the prototype of the information system for automating the construction of individual learning paths. The developed information system may be used for the construction of individual student's learning paths in educational institutions of higher education.

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