Ernest Mwebaze

Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis

In this paper, provide an application of Learning Vector Quantization (LVQ)-based techniques for solving a real-world problem. We apply LVQ for automated diagnosis of crop disease in cassava plants using features extracted from images of plants’ leaves. The problem reduces to a five class problem in which we attempt to distinguish between a leaf from a health plant and leaves representing four different viral and bacterial diseases in cassava. We discuss the problem under additional constraints that the solution must easily be deployable on a mobile device with limited processing power. In this study we explore the right configuration of type of algorithm and type of features extracted from the leaves that optimally solves the problem. We apply different variations of LVQ and compare them with standard classification techniques (Naive Bayes, SVM and KNN). Results point to a preference of color feature representations and LVQ-based algorithms.

Evaluating Subcriticality during the Ebola Epidemic in West Africa

The 2014–2015 Ebola outbreak is the largest and most widespread to date. In order to estimate ongoing transmission in the affected countries, we estimated the weekly average number of secondary cases caused by one individual infected with Ebola throughout the infectious period for each affected West African country using a stochastic hidden Markov model fitted to case data from the World Health Organization. If the average number of infections caused by one Ebola infection is less than 1.0, the epidemic is subcritical and cannot sustain itself. The epidemics in Liberia and Sierra Leone have approached subcriticality at some point during the epidemic; the epidemic in Guinea is ongoing with no evidence that it is subcritical. Response efforts to control the epidemic should continue in order to eliminate Ebola cases in West Africa.

Design Choices for Automated Disease Surveillance in the Social Web

The social web has emerged as a dominant information architecture accelerating technology innovation on an unprecedented scale. The utility of these developments to public health use cases like disease surveillance, information dissemination, outbreak prediction and so forth has been widely investigated and variously demonstrated in work spanning several published experimental studies and deployed systems. In this paper we provide an overview of automated disease surveillance efforts based on the social web characterized by their different high level design choices regarding functional aspects like user participation and language parsing approaches. We briefly discuss the technical rationale and practical implications of these different choices in addition to the key limitations associated with these systems within the context of operable disease surveillance. We hope this can offer some technical guidance to multi-disciplinary teams on how best to implement, interpret and evaluate disease surveillance programs based on the social web.

Ontology boosted deep learning for disease name extraction from Twitter messages

This paper presents an ontology based deep learning approach for extracting disease names from Twitter messages. The approach relies on simple features obtained via conceptual representations of messages to obtain results that out-perform those from word level models. The significance of this development is that it can potentially reduce the cost of generating named entity recognition models by reducing the cost of annotating training data since ontology creation is a one-time cost as the conceptual level the ontology is meant to be fairly static and reusable. This is of great importance when it comes to social media text like Twitter messages where you have a large, unbounded lexicon with spatial and temporal variations and other inherent biases that make it logistically untenable to annotate a representative amount of text for general purpose models for live applications.

Correction: Evaluating Subcriticality during the Ebola Epidemic in West Africa.

[This corrects the article DOI: 10.1371/journal.pone.0140651.].