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314 tulosta hakusanalla Shaimaa Saad

Let's swim in the deep ocean

Let's swim in the deep ocean

Shaymaa Elkeey

LAP Lambert Academic Publishing
2020
pokkari
This book supports national science standards related to life science. Also this book describes the animals and illustrates activities about the ocean . The images support early readers in understanding the text . The repetition of words and phrases help early readers learn new words . And so introduces early readers to subject -specific vocabulary words, which are defined in the glossary section and to use the table of contents as a whole .
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Magic will tell children to do science

Magic will tell children to do science

Shaymaa Elkeey

LAP Lambert Academic Publishing
2020
pokkari
This book presents lessons to help veteran, experienced, and novice teachers develop a sound, realistic, fun and exciting science curriculum in magic within the guidelines of the Next Generation Science Standards and the Common Core Standards. It is designed as a practical handbook for busy classroom teachers;organized to include best practices for teaching science; structured with easily implemented ideas that embrace and address the needs of all learners; The lessons are designed with inquiry learning, thus engaging all children in the learning process through hands-on activities in cooperative learning groups. I wrote this book using everyday language and with simple descriptive examples that are thought-provoking and supported by leading researchers and verified by my extensive experience.
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Learning the Graph Edit Distance by Embedding the Graph Matching

Learning the Graph Edit Distance by Embedding the Graph Matching

Shaima Algabli

Lap Lambert Academic Publishing
2021
pokkari
This method is to automatically deduce the insertion, deletion and substitution costs of the Graph edit distance. The method is based on embedding the ground-truth node-to-node mappings into a Euclidean space and learning the edit costs through the hyperplane that splits the nodes into mapped ones and non-mapped ones in this new space. In this way, the algorithm does not need to compute any graph matching process, which is the main drawback of other methods due to its intrin- sic exponential computational complexity. Nevertheless, our learning method has two main restrictions: 1) the insertion and deletion edit costs have to be constants; 2) the substitution edit costs have to be represented as inner products of two vectors. One vector represents certain weights and the other vector represents the distances between attributes. Experimental validation shows that the matching accuracy of this method outperforms the current methods. Furthermore, there is a significant reduction in the runtime in the learning process.This book has main contributions in learning cost based on embedding.
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