46 citations · 70 across the 13 of their papers we have counts for
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Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification
Jihen Amara, Birgitta König-Ries, Sheeba Samuel
While deep learning has significantly advanced automatic plant disease detection through image-based classification, improving model explainability remains crucial for reliable dis…
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publications
Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel
Deep Learning (DL) techniques are increasingly applied in scientific studies across various domains to address complex research questions. However, the methodological details of th…
Enhancing Explainability in Multimodal Large Language Models Using Ontological Context
Jihen Amara, Birgitta König-Ries, Sheeba Samuel
Recently, there has been a growing interest in Multimodal Large Language Models (MLLMs) due to their remarkable potential in various tasks integrating different modalities, such as…
Evaluating the method reproducibility of deep learning models in the biodiversity domain
Waqas Ahmed, Vamsi Krishna Kommineni, Birgitta König-Ries +3
Artificial Intelligence (AI) is revolutionizing biodiversity research by enabling advanced data analysis, species identification, and habitats monitoring, thereby enhancing conserv…
From human experts to machines: An LLM supported approach to ontology and knowledge graph construction
Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel
The conventional process of building Ontologies and Knowledge Graphs (KGs) heavily relies on human domain experts to define entities and relationship types, establish hierarchies,…