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- Ahmed, Bestoun S., 1982-, et al.
(författare)
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A new approach to speed up combinatorial search strategies using stack and hash table
- 2016
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Ingår i: Proceedings of 2016 SAI Computing Conference, SAI 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467384605 ; , s. 1217-1222
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Konferensbidrag (refereegranskat)abstract
- Owing to the significance of combinatorial search strategies both for academia and industry, the introduction of new techniques is a fast growing research field these days. These strategies have really taken different forms ranging from simple to complex strategies in order to solve all forms of combinatorial problems. Nonetheless, despite the kind of problem these approaches solve, they are prone to heavy computation with the number of combinations and growing search space dimensions. This paper presents a new approach to speed up the generation and search processes using a combination of stack and hash table data structures. This approach could be put to practice for the combinatorial approaches to speed up the generation of combinations and search process in the search space. Furthermore, this new approach proved its performance in diverse stages better than other known strategies. © 2016 IEEE.
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| 2. |
- Kusetogullari, Huseyin, et al.
(författare)
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Self-Adaptive Hybrid PSO-GA Method for Change Detection Under Varying Contrast Conditions in Satellite Images
- 2016
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Ingår i: Proceedings of the 2016 SAI Computing Conference (SAI). - : IEEE. - 9781467384605 ; , s. 361-368
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a new unsupervised satellite change detection method, which is robust to illumination changes. To achieve this, firstly, a preprocessing strategy is used to remove illumination artifacts and results in less false detection than traditional threshold-based algorithms. Then, we use the corrected input data to define a new fitness function based on the difference image. The purpose of using Self-Adaptive Hybrid Particle Swarm Optimization-Genetic Algorithm (SAPSOGA) is to combine two meta-heuristic optimization algorithms to search and find the feasible solution in the NP-hard change detection problem rapidly and efficiently. The hybrid algorithm is employed by letting the GA and PSO run simultaneously and similarities of GA and PSO have been considered to implement the algorithm, i.e. the population. In the SAPSOGA employed, in each iteration/generation the two population based algorithms share different amount of information or individual(s) between themselves. Thus, each algorithm informs each other about their best optimum results (fitness values and solution representations) which are obtained in their own population. The fitness function is minimized by using binary based SAPSOGA approach to produce binary change detection masks in each iteration to obtain the optimal change detection mask between two multi temporal multi spectral landsat images. The proposed approach effectively optimizes the change detection problem and finds the final change detection mask.
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| 3. |
- Ruffaldi, Emanuele, et al.
(författare)
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Data collection and processing for a multimodal learning analytic system
- 2016
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Ingår i: Proceedings of 2016 SAI Computing Conference (SAI). - : IEEE. - 9781467384605 - 9781467384612 ; , s. 858-863
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Konferensbidrag (refereegranskat)abstract
- Learning Analytic (LA) systems are aimed at supporting teachers in understanding the learning process by analyzing the information and the interaction of students with computer systems. In the case of a project-based learning process there is a need of introducing measure the student’ activity as acquired via multiple modalities and then processed. The acquisition and processing needs to take into account the specificities of the learning context and deployment at schools, in particular in terms of system architecture. The paper proposes an architecture for the acquisition and processing of data for project-based LA designed to be interoperable and scalable. System design, details of the solutions and brief examples of acquired data are presented.
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