@conference {981,
	title = {Traditional Bengali Food Classification Using Convolutional Neural Network},
	booktitle = {2021 IEEE Region 10 Symposium (TENSYMP)},
	year = {2021},
	pages = {1-8},
	keywords = {Computational modeling, Convolutional Neural Network, Convolutional neural networks, Data models, fine tuning, image classification, Image recognition, Neural networks, traditional Bengali foods, Transfer learning, Turning, VGG16},
	doi = {10.1109/TENSYMP52854.2021.9550874},
	author = {Uddin, Asif Mahbub and Al Miraj, Abdullah and Sen Sarma, Moumita and Das, Avishek and Gani, Md. Manjurul}
}
@conference {747,
	title = {A Method for Automatically Generating Schema Diagrams for OWL Ontologies},
	booktitle = {1st Iberoamerican Knowledge Graph and Semantic Web Conference (KGSWC)},
	year = {2019},
	month = {06/2019},
	publisher = {Springer},
	organization = {Springer},
	chapter = {149-161},
	address = {Villa Clara, Cuba},
	abstract = {<p>Interest in Semantic Web technologies, including knowledge graphs and ontologies, is increasing rapidly in industry and academics. In order to support ontology engineers and domain experts, it is necessary to provide them with robust tools that facilitate the ontology engineering process. Often, the schema diagram of an ontology is the most important tool for quickly conveying the overall purpose of an ontology. In this paper, we present a method for programmatically generating a schema diagram from an OWL file. We evaluate its ability to generate schema diagrams similar to manually drawn schema diagrams and show that it outperforms VOWL and OWLGrEd. In addition, we provide a prototype implementation of this tool.</p>
},
	keywords = {design patterns, evaluation, implementation, ontology, schema diagrams, visualization},
	author = {Cogan Shimizu and Aaron Eberhart and Nazifa Karima and Quinn Hirt and Adila Krisnadhi and Pascal Hitzler}
}
