Search Results - "data mining"

Genebank accessions sub-setting tool: IPSR Innovation Profile by Kehel, Zakaria, Ramírez Villegas, Julián Armando, Obreza, Matija, Rabil, Christelle, Aouzal, Khadija, Mora, Brian, García, Juan Camilo, Sotelo, Humberto Steven

“…Identifying the correct set of accessions to respond to these requests adequately can be time-consuming.The subsetting tool allows to develop and implement data mining techniques for subsetting germplasm accessions, thus helping address germplasm requests more effectively and efficiently. …”
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Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi by Mukashov, Askar, Robinson, Sherman, Thurlow, James, Arndt, Channing, Thomas, Timothy S.

“…This paper uses machine learning, simulation, and data mining methods to develop Systematic Risk Profiles of three developing economies: Kenya, Rwanda, and Malawi. …”
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How did households in Mali cope with covariate shocks between 2018 and 2023? Exploration of a unique dataset by Marivoet, Wim, Hema, Aboubacar

“…Apart from a detailed profiling of both dimensions, this analysis relies on a data mining algorithm to uncover interesting associations between covariate shocks and coping strategies. …”
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How did households in Chad cope with covariate shocks between 2018 and 2023? Exploration of a unique dataset by Marivoet, Wim, Hema, Aboubacar

“…Apart from a detailed profiling of both dimensions, this analysis relies on a data mining algorithm to uncover interesting associations between covariate shocks and coping strategies. …”
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Zambia: Systematic analysis of domestic production and world market shocks by Mukashov, Askar, Diao, Xinshen, Jones, Eleanor, Thurlow, James

“…The Zambian Computable General Equilibrium (CGE) model was employed to simulate a range of potential economic outcomes under various sampled shock scenarios developed using historical data on domestic agricultural yield volatilities and world market prices for traded goods. Data mining and machine learning methods were applied to quantify the contribution of each shock to the uncertainty of these outcomes. …”
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Malawi: Systematic analysis of domestic production and world market shocks by Mukashov, Askar, Duchoslav, Jan, Kankwamba, Henry, Jones, Eleanor, Thurlow, James

“…The Malawian Computable General Equilibrium (CGE) model was employed to simulate a range of potential economic outcomes under various sampled shock scenarios developed using historical data to capture domestic agricultural yield volatilities and world market prices uncertainty for traded goods. Data mining and ma-chine learning methods were applied to quantify the contribution of each shock to the uncertainty of economic outcomes (gross domestic product [GDP], private consumption, poverty, and undernourishment). …”
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Kenya: Systematic analysis of domestic production and world market shocks by Mukashov, Askar, Mbuthia, Juneweenex, Omune, Lensa, Jones, Eleanor, Thurlow, James

“…The Kenyan Computable General Equilibrium (CGE) model was employed to simulate a range of po-tential economic outcomes under various sampled shock scenarios developed using historical data to capture do-mestic agricultural yield volatilities and world market prices uncertainty for traded goods. Data mining and machine learning methods were applied to quantify the contribution of each shock to the uncertainty of economic outcomes (gross domestic product [GDP], private consumption, poverty, and undernourishment). …”
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Papua New Guinea: Systematic analysis of domestic production and world market shocks by Mukashov, Askar, Dorosh, Paul A., Schmidt, Emily, Thurlow, James

“…The Computable General Equilibrium (CGE) model of PNG was used to simulate a range of potential economic outcomes under these scenarios. In addition, data mining and machine learning methods were applied to quantify the contribution of each shock to the uncertainty of economic outcomes. …”
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Uganda: Systematic analysis of world market and domestic production shocks by Mukashov, Askar, Jones, Eleanor, Thurlow, James

“…The significance of these risks is assessed based on the range of the shocks’ impacts on four main economic and development indicators: total GDP, private consumption, poverty rate, and prevalence of undernourishment. The analysis uses data mining methods to simultaneously sample many shocks from historical data, con structing a comprehensive set of realistic shock scenarios for Uganda. …”
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Advancing multivariate time series similarity assessment: an integrated computational approach by Tonle, Franck Bruno Noumbo, Tonnang, Henri E. Z., Ndadji, Milliam M. Z., Tchoupé Tchendji, Maurice, Nzeukou, Armand, Senagi, Kennedy, Niassy, Saliou

“…Data mining, particularly multivariate time series data analysis, is crucial in extracting insights from complex systems and supporting informed decision-making across diverse domains. …”
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Reducing vulnerability to hydro-meteorological extremes in Africa. A qualitative assessment of national climate disaster management policies: accounting for heterogeneity by Tall, Arame, Patt AG, Fritz, Steffen

“…This paper examines the heterogeneity that exists within Africa′s institutional arrangements for climate-related disaster risk management, and introduces a three-partite policy classification that ranks each country as one of three disaster management policy types: the ‘Unprepared Firefighters′ (whose response to disasters is late, delayed and ineffective), the ‘Prepared Firefighters′ (for the most part effective disaster responders) and the ‘Disaster Averters′ (who experienced a paradigm shift and moved focus away from the hazard itself towards a reduction of the underlying risk factors that cause disasters). Through extensive data mining, interviews and qualitative country assessments, we map where African countries lie on this spectrum of effective climate-related disaster risk management. …”
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Unlocking Big Data’s Potential to Strengthen Farmers’ Resilience: The Platform for Big Data in Agriculture by Jiménez, Daniel, Ramírez Villegas, Julián Armando

“…The availability of free and open access to large amounts of crop and weather data—combined with data mining approaches—enabled researchers to spot the main limiting factors of crop productivity at a site-specific scale, combine it with seasonal climate predictions, and get the information to farmers in time to inform their planting decisions. …”
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Digital solutions to transform agriculture: lessons and experiences in Ethiopia by Tamene, Lulseged D., Abera, Wuletawu, Erkossa, Teklu

“…The team explored different data mining techniques, exchanged experiences and developed frameworks that can facilitate further data analysis endeavors. …”
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A novel locus from the wild allotetraploid rice species Oryza latifolia Desv. confers bacterial blight (Xanthomonas oryzae pv. oryzae) resistance in rice (O. sativa) by Angeles-Shim, Rosalyn B., Shim, Junghyun, Vinarao, Ricky B., Lapis, Ruby S., Singleton, Joshua J.

“…Putative candidate genes that were identified by data mining and comparative sequence analysis can provide targets for further studies on mapping and cloning of the causal gene for PXO339 resistance in the MDILs. …”
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Bangladesh: Systematic analysis of domestic production and world market shocks by Mukashov, Askar, Jones, Eleanor, Thurlow, James

“…The Bangladesh Computable General Equilibrium (CGE) model was employed to simulate a range of potential economic outcomes under various shock scenarios sampled using historical data to capture domestic agricultural yield volatilities and world market price uncertainty for traded goods. Data mining and machine learning methods were applied to quantify the contribution of each shock to the uncertainty of economic outcomes (gross domestic product [GDP], private consumption, poverty, and undernourishment). …”
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Ghana: Systematic analysis of world market and domestic production shocks by Mukashov, Askar, Pauw, Karl, Jones, Eleanor, Thurlow, James

“…The significance of these risks is assessed based on the range of the shocks’ impacts on four main economic and development indicators: total GDP, private consumption, poverty rate, and prevalence of undernourishment. The analysis uses data mining methods to simultaneously sample many shocks from historical data, con structing a comprehensive set of realistic shock scenarios for Ghana. …”
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Biodereplication of antiplasmodial extracts: application of the amazonian medicinal plant piper coruscans kunth by Vásquez Ocmín, Pedro, Gallard, Jean François, Van Baelen, Anne Cécile, Leblane, Karine, Cojean, Sandrine, Mouray, Elisabeth, Grellier, Philippe, Amasifuen Guerra, Carlos Alberto, Beniddir, Mehdi A., Evanno, Laurent, Figadère, Bruno, Maciuk, Alexandre

“…Molecular networking and automated annotation of targeted mass through data mining were followed by mass-guided compound isolation by taking advantage of the versatility and finely tunable selectivity offered by centrifugal partition chromatography. …”
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Reference-Guided Draft Genome Assembly, Annotation and SSR Mining Data of the Peruvian Creole Cattle (Bos taurus) by Estrada Cañari, Richard, Corredor Arizapana, Flor Anita, Figueroa, Deyanira, Salazar Coronel, Wilian, Quilcate Pairazamán, Carlos Enrique, Vásquez Pérez, Héctor Vladimir, Maicelo Quintana, Jorge Luis, Gonzales, Jhony, Arbizu Berrocal, Carlos Irvin

“…A total of 19,803 protein-coding genes were annotated in the PCC genome. For SSR data mining, we detected similar statistics in comparison with other breeds. …”
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An early study on the potential of landscape and geographical variables to reduce bias in forest forecast planning in Ireland by Judd, Charles Socrates

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Genome-wide association study of growth performance and immune response to Newcastle disease virus of indigenous chicken in Rwanda by Habimana, R., Ngeno, K., Okeno, T.O., Hirwa, C.D., Keambou, T.C., Yao, Nasser

“…Multiple testing was corrected using chromosomal false detection rates of 5 and 10% for significant and suggestive thresholds, respectively. BioMart data mining and variant effect predictor tools were used to annotate SNPs and candidate genes, respectively. …”
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