113 research outputs found
Controlling cassava mosaic virus and cassava mealybug in Sub-Saharan Africa:
No full textmillions fed, Cassava, mosaic virus, mealybug,
Recommended from our members
Attain, Retain & Train: The Godly Art That Produces Lasting Accomplishments
No full textCOMPARING THE PROFITABILITY OF CASSAVA-BASED PRODUCTION SYSTEMS IN THREE WEST AFRICAN COUNTRIES: COTE D'IVOIRE, GHANA AND NIGERIA
No full textSub-Saharan Africa (SSA) cassava-producing countries such as Nigeria, Ghana, and Côte d'Ivoire have developed, in recent years, a renewed interest in cassava as an alternative food crop. This has led to a major expansion in cassava-based production systems in Nigeria and Ghana, whereas there has been a slower growth in Côte d'Ivoire (Nweke et al., 1998). This paper is based on the argument that the difference in various factors such as agricultural policies (i.e., trade and price policies, domestic production taxes or subsidies), location and technologies (production and processing) between Nigeria, Ghana and Côte d'Ivoire the difference in the level of growth in cassava-based production systems. The paper examines, using the Policy Analysis Matrix (PAM), the magnitude of the impact of these factors on the private and social profitability of cassava production and post-production processing in Côte d'Ivoire, Ghana and Nigeria. The topic has not been examined in previous studies. The paper relies primarily on data for Côte d'Ivoire, Ghana and Nigeria from the Collaborative Study of Cassava in Africa (COSCA) survey. The baseline results demonstrate the similarity in efficiencies of production in these West African countries. The simulation findings indicated that, in Côte d'Ivoire, farmers benefited from the depreciation of the equilibrium exchange rate while farmers in Ghana and Nigeria suffered losses. Simulation results also indicated that Ivorian and Ghanaian cassava/maize farmers could benefit from growing IITA's improved variety and adopting mechanized processing methods.Crop Production/Industries,
Economic growth and distribution of income: A growth model to fit Ghanaian data
No full textIncome distribution, economic growth, Development strategies,
Sensitivity of welfare effects estimated by equilibrium displacement model: A biological productivity growth for semisubsistence crops in Sub-Sahara African market with high transaction costs
No full textequilibrium displacement model, pivotal shift, Cassava, semisubsistence, market margins, double buffering, Development strategies,
Trade liberalization, poverty, and food security in India:
No full textfood security, Nutrition, Computable general equilibrium (CGE), Globalization, Markets, trade,
Do external grants to district governments discourage own-revenue generation?: A look at local public finance dynamics in Ghana
No full textDecentralization, Inter-governmental transfers, Local government, Internally generated revenues, Development strategies,
Big Data Analysis for Unstructured Data in Nigeria Court System
No full text<p><strong>Abstract:</strong> Big Data Analysis for unstructured data involves analyzing and processing large amounts of unstructured data, such as text, images, and audio, to extract meaningful insights and knowledge. Techniques used in big data analysis for unstructured data include Natural Language Processing (NLP), Computer Vision, and Speech Recognition. Big data analysis can be useful in the Nigerian court system for analyzing unstructured data, such as legal documents, witness statements, and court transcripts. The goal would be to identify patterns and relationships within the data that can help make more informed decisions, improve processes, and increase the efficiency of the court system. This paper presents an improved Hybrid model for legal case document classification. The system starts by collecting legal case documents from an online domain. The collected documents are in pdf format. The collected pdf files were converted to texts using a pdf miner library in python. The converted texts were used in creating tables using the pandas library. After the creation of the dataset table, the dataset was pre-processed by removing Nan values, and non-alphanumeric values, and also performing tokenization. The tokenized data was then passed into principal component analysis for the selection of important features. The selected features were then used in training an LSTM model for the classification of the legal case documents. The result of the LSTM is outstanding, having an accuracy of 98% for training. The model was deployed to the web, for easy execution, testing, and assessment.</p><p><strong>Keywords:</strong><i> </i>Big Data, Legal Case, Principal Component Analysis, Long Short-Term Memory, Python flask.</p><p><strong>Title:</strong> Big Data Analysis for Unstructured Data in Nigeria Court System</p><p><strong>Author:</strong> C. Aloy-Okwelle, J. Palimote, O.P Nweke</p><p><strong>International Journal of Novel Research in Computer Science and Software Engineering</strong></p><p><strong>ISSN 2394-7314</strong></p><p><strong>Vol. 10, Issue 3, September 2023 - December 2023</strong></p><p><strong>Page No: 44-50</strong></p><p><strong>Novelty Journals</strong></p><p><strong>Website: www.noveltyjournals.com</strong></p><p><strong>Published Date: 08-November-2023</strong></p><p><strong>DOI: </strong><a href="https://doi.org/10.5281/zenodo.10082510"><strong>https://doi.org/10.5281/zenodo.10082510</strong></a></p><p><strong>Paper Download Link (Source)</strong></p><p><a href="https://www.noveltyjournals.com/upload/paper/Big%20Data%20Analysis%20for%20Unstructured-08112023-1.pdf"><strong>https://www.noveltyjournals.com/upload/paper/Big%20Data%20Analysis%20for%20Unstructured-08112023-1.pdf</strong></a></p>
Recommended from our members
BUILDING ON SUCCESSES IN AFRICAN AGRICULTURE
No full textCONTENTS: 1. African Agriculture: Past Performance, Future Imperatives / Steven Haggblade, Peter Hazell, Ingrid Kirsten, and Richard Mkandawire; 2. Generalizing from Past Successes / Steven Haggblade; 3. Recent Growth in African Cassava / Felix Nweke, Steven Haggblade, and Ballard Zulu; 4. Maize Breeding in East and Southern Africa, 1900–2000 / Melinda Smale and T. S. Jayne; 5. Mali’s White Revolution: Smallholder Cotton from 1960 to 2003 / James Tefft; 6. Smallholder Dairy in Kenya / Margaret Ngigi; 7. Are Kenya’s Horticultural Exports a Replicable Success Story? / Nicholas Minot and Margaret Ngigi; 8. Strategies for Sustainable Natural Resource Management / Steven Franzel, Frank Place, Chris Reij, and Gelson Tembo; 9. The Changing Policy Environment Facing African Agriculture / Francis Chigunta, Ross Herbert, Michael Johnson, and Richard Mkandawire; 10. The Pretoria Statement on the Future of African Agricultur
Building on successes in African agriculture:
No full textCONTENTS: 1. African Agriculture: Past Performance, Future Imperatives / Steven Haggblade, Peter Hazell, Ingrid Kirsten, and Richard Mkandawire; 2. Generalizing from Past Successes / Steven Haggblade; 3. Recent Growth in African Cassava / Felix Nweke, Steven Haggblade, and Ballard Zulu; 4. Maize Breeding in East and Southern Africa, 1900–2000 / Melinda Smale and T. S. Jayne; 5. Mali's White Revolution: Smallholder Cotton from 1960 to 2003 / James Tefft; 6. Smallholder Dairy in Kenya / Margaret Ngigi; 7. Are Kenya's Horticultural Exports a Replicable Success Story? / Nicholas Minot and Margaret Ngigi; 8. Strategies for Sustainable Natural Resource Management / Steven Franzel, Frank Place, Chris Reij, and Gelson Tembo; 9. The Changing Policy Environment Facing African Agriculture / Francis Chigunta, Ross Herbert, Michael Johnson, and Richard Mkandawire; 10. The Pretoria Statement on the Future of African AgricultureCollective behavior, Property rights, Public goods, Agroforestry, Irrigation, Fisheries, Forest management, Rangelands, plant genetic resources, Pests Management, Watersheds, agribusiness, extension activities, extension-research linkages, Collective action,
- …
