Search results for: Merlyn Chapfunga

Authors: Sara Bashir, Arthur Pagiwa, Merlyn Chapfunga, Ali Ahmad Khan, Gugulethu Moyo, Osman Hassan

Abstract:

Micronutrient malnutrition (MNM) is a persistent health issue in Sudan, where food fortification (FF) has the potential to improve nutritional intake. However, community acceptance and understanding are critical to the success of fortification programs. This study aimed to explore community perspectives on food fortification in Sudan, assessing knowledge, acceptability, and misconceptions. Using a mixed-methods design, an online survey was conducted through social media, gathering responses from 1,118 participants from various demographic backgrounds. Approximately half of the respondents were not aware of what FF entails and there were prevalent misconceptions about FF, perceived health benefits, and acceptance influenced by individual beliefs and circumstances. The results highlight a considerable gap in understanding the purpose and benefits of FF, despite general awareness. This study underscores the need for targeted educational campaigns to address misconceptions and promote acceptance, with attention to gender and age-specific perspectives. Furthermore, the findings provide valuable insights for policymakers aiming to implement effective, culturally-sensitive FF initiatives and awareness campaigns in Sudan.

Keywords: food fortification, malnutrition, micronutrients, Sudan

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Authors: Anthony Proschka, Deepak Mishra, Merlyn Ramanan, Zurab Baratashvili

Abstract:

Small and medium-sized businesses receive over 160 billion invoices every year. Since these documents exhibit many subtle differences in layout and text, extracting structured fields such as sender name, amount, and VAT rate from them automatically is an open research question. In this paper, existing work in template-based document extraction is extended, and a system is devised that is able to reliably extract all required fields for up to 70% of all documents in the data set, more than any other previously reported method. The approaches are described for 1) detecting through visual features which template a given document belongs to, 2) automatically generating extraction rules for a given new template by composing regular expressions from multiple components, and 3) computing confidence scores that indicate the accuracy of the automatic extractions. The system can generate templates with as little as one training sample and only requires the ground truth field values instead of detailed annotations such as bounding boxes that are hard to obtain. The system is deployed and used inside a commercial accounting software.

Keywords: data mining, information retrieval, business, feature extraction, layout, business data processing, document handling, end-user trained information extraction, document archiving, scanned business documents, automated document processing, F1-measure, commercial accounting software

Procedia PDF Downloads 130