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Auflistung nach Autor:in "Algergawy, Alsayed"

1 - 4 von 4
  • Konferenzbeitrag
    A Core Ontology to Support Agricultural Data Interoperability
    (BTW 2023, 2023) Abdelmageed, Aly; Hatem, Shahenda; ael, Tasneem; Medhat, Walaa; König-Ries, Birgitta; Ellakwa, Susan; Elkafrawy, Passent; Algergawy, Alsayed
    The amount and variety of raw data generated in the agriculture sector from numeroussources, including soil sensors and local weather stations, are proliferating. However, these raw data in themselves are meaningless and isolated and, therefore, may offer little value to the farmer. Data usefulness is determined by its context and meaning and by how it is interoperable with data from other sources. Semantic web technology can provide context and meaning to data and its aggregation by providing standard data interchange formats and description languages. In this paper, we introduce the design and overall description of a core ontology that facilitates the process of data interoperability in the agricultural domain.
  • Konferenzbeitrag
    A Deep Learning-based Approach for Banana Leaf Diseases Classification
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Amara, Jihen; Bouaziz, Bassem; Algergawy, Alsayed
    Plant diseases are important factors as they result in serious reduction in quality and quantity of agriculture products. Therefore, early detection and diagnosis of these diseases are important. To this end, we propose a deep learning-based approach that automates the process of classifying ba- nana leaves diseases. In particular, we make use of the LeNet architecture as a convolutional neural network to classify image data sets. The preliminary results demonstrate the effectiveness of the proposed approach even under challenging conditions such as illumination, complex background, different resolution, size, pose, and orientation of real scene images.
  • Textdokument
    Entity Extraction in the Ecological Domain – A practical guide
    (BTW 2019 – Workshopband, 2019) Udovenko, Vladimir; Algergawy, Alsayed
    Scientific information comes in many shapes: As data in databases or spreadsheets, but also as textual information in papers and books. In order to exploit all this information and integrate all the knowledge that is available regarding a specific entity, it is necessary to identify entities and their relationships. In this paper, we provide a guideline to setting up a pipeline that supports entity and relationship extraction from scientific publications from the ecological domain.
  • Textdokument
    ISTMINER: Interactive Spatiotemporal Co-occurrence Pattern Extraction: A Biodiversity case study
    (INFORMATIK 2021, 2021) Sharafeldeen, Dina; Bakli, Mohamed; Algergawy, Alsayed; König-Ries, Birgitta
    In recent years, the exponential growth of spatiotemporal data has led to an increasing need for new interactive methods for accessing and analyzing this data. In the biodiversity domain, species co-occurrence models are critical to gain a mechanistic understanding of the processes underlying biodiversity and supporting its maintenance. This paper introduces a new framework that allows users to explore species occurrences datasets at different spatial and temporal periods to extract co-occurrence patterns. As a real-world case study, we conducted several experiments on a subset of the Global Biodiversity Information Facility (GBIF) occurrences dataset to extract species co-occurrence patterns interactively. For better understanding, these co-occurrence patterns are visualized in a map view and as a graph. Also, the user can export these patterns in CSV format for further use. For many queries, runtimes are in a range that allows for interaction already. Further optimizations are on our research agenda.