Search Results - "Simumba, Naomi"

Multiple objective metaheuristics for feature selection based on stakeholder requirements in credit scoring by Simumba, Naomi, Okami, Suguru, Kodaka, Akira, Kohtake, Naohiko

“…Alternative data is increasingly utilized for credit evaluation of financially excluded persons. However, requirements, such as reliability, which gain new…”
Get full text

Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring by Simumba, Naomi, Okami, Suguru, Kodaka, Akira, Kohtake, Naohiko

“…Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional…”
Get full text

Spatiotemporal Integration of Mobile, Satellite, and Public Geospatial Data for Enhanced Credit Scoring by Simumba, Naomi, Okami, Suguru, Kodaka, Akira, Kohtake, Naohiko

“…Credit scoring of financially excluded persons is challenging for financial institutions because of a lack of financial data and long physical distances, which…”
Get full text

Alternative Scoring Factors using Non-Financial Data for Credit Decisions in Agricultural Microfinance by Simumba, Naomi, Okami, Suguru, Kodaka, Akira, Kohtake, Naohiko

“…Financial exclusion has a major socio-economic impact on the poor and unbanked. Financially excluded smallholder farmers face challenges accessing credit…”
Get full text

Sandwiched Lo-Res Simulation for Scalable Flood Modeling by Putra, Refaldi I. D., Ishikawa, Tatsuya, Simumba, Naomi, Tatsubori, Michiaki

“…High-resolution flood modeling is enabled by utilizing high-resolution input derived by remote sensing technologies such as Light Detection and Ranging (LiDAR)…”
Get full text

SAR2NDVI: Pre-Training for SAR-to-NDVI Image Translation by Kimura, Daiki, Ishikawa, Tatsuya, Mitsugi, Masanori, Kitakoshi, Yasunori, Tanaka, Takahiro, Simumba, Naomi, Tanaka, Kentaro, Wakabayashi, Hiroaki, Sampei, Masato, Tatsubori, Michiaki

“…Geospatial machine learning is of growing importance in various global remote-sensing applications, particularly in the realm of vegetation monitoring…”
Get full text

Towards Efficient Satellite Data Representation Learning with Consistency Loss by Simumba, Naomi, Kimura, Daiki, Tatsubori, Michiaki

“…Foundation models are often pretrained on large datasets and have been valuable in improving efficiency of model training for language and visual processing…”
Get full text

Geospatial Sampling by Maximizing Information Entropy by Kimura, Daiki, Simumba, Naomi, Freitag, Marcus, Schmude, Johannes, Tatsubori, Michiaki

“…To improve the unsupervised training of geospatial foundation models, we propose a novel approach that prepares diverse and unbiased datasets by maximizing an…”
Get full text

TensorBank: Tensor Lakehouse for Foundation Model Training by Kienzler, Romeo, Tizzei, Leonardo Pondian, Blumenstiel, Benedikt, Nagy, Zoltan Arnold, Mukkavilli, S. Karthik, Schmude, Johannes, Freitag, Marcus, Behrendt, Michael, Civitarese, Daniel Salles, Simumba, Naomi, Kimura, Daiki, Hamann, Hendrik

“…Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural…”
Get full text

Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation by Muszynski, Michal, Klein, Levente, da Silva, Ademir Ferreira, Atluri, Anjani Prasad, Gomes, Carlos, Szwarcman, Daniela, Singh, Gurkanwar, Gu, Kewen, Zortea, Maciel, Simumba, Naomi, Fraccaro, Paolo, Singh, Shraddha, Meliksetian, Steve, Watson, Campbell, Kimura, Daiki, Srinivasan, Harini

“…Global vegetation structure mapping is critical for understanding the global carbon cycle and maximizing the efficacy of nature-based carbon sequestration…”
Get full text

Credit decision tool using mobile application data for microfinance in agriculture by Simumba, Naomi, Okami, Suguru, Kohtake, Naohiko

“…Smallholder agriculture is vital for food security and poverty reduction. Poor, unbanked smallholder farmers face challenges in obtaining loans for agriculture…”
Get full text

Foundation Models for Generalist Geospatial Artificial Intelligence by Jakubik, Johannes, Roy, Sujit, Phillips, C. E, Fraccaro, Paolo, Godwin, Denys, Zadrozny, Bianca, Szwarcman, Daniela, Gomes, Carlos, Nyirjesy, Gabby, Edwards, Blair, Kimura, Daiki, Simumba, Naomi, Chu, Linsong, Mukkavilli, S. Karthik, Lambhate, Devyani, Das, Kamal, Bangalore, Ranjini, Oliveira, Dario, Muszynski, Michal, Ankur, Kumar, Ramasubramanian, Muthukumaran, Gurung, Iksha, Khallaghi, Sam, Hanxi, Li, Cecil, Michael, Ahmadi, Maryam, Kordi, Fatemeh, Alemohammad, Hamed, Maskey, Manil, Ganti, Raghu, Weldemariam, Kommy, Ramachandran, Rahul

“…Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth…”
Get full text