Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis

The impact of saving and credit cooperatives on food security in the West Amhara Region of Ethiopia

In rural Ethiopia, among other things, lack of adequate financial service is considered as the basic problem to alleviate rural poverty and to solve the problem of food insecurity. Commercial banks are restricted to urban centres. Providing rural financial service through RUSACCO to the poor has been proposed as a tool for economic development and for achieving food security. Evidence from research in this regard has been so far scanty, especially in rural Ethiopia. The aims of this study are to analyze the determinants of membership, to identify socioeconomic and demographic factors that influence members’ participation in RUSACCOs and to quantify the impact of RUSACCOs on member households’ food security. The study was conducted in two purposely selected woredas in the Amhara region one from food insecure (Lay Gayint woreda) and the other from food secure (Dejen woreda). Six RUSACCOs were selected randomly from these two woredas. Both qualitative and quantitative data were collected. Key informant interviews, focus group discussions and survey techniques were used to collect primary data. Collected data was then analyzed using mixed methods depending on the nature of data. For quantitative data analysis appropriate statistical models were used. The study result reveals that the number of members in each RUSACCO is very small. However, the majority of non-member respondents are willing to join RUSACCO. Lack of information about the benefits of RUSACCO membership is the main problem why many rural poor do not join RUSACCOs. Members participate in different aspects of the cooperatives, starting from attending general assembly up to board membership. They also participate actively in saving and borrowing activities of RUSACCO. The majority of the respondents believe the RUSACCO is a vital instrument in combating food insecurity. The empirical findings indicate that gender, marital status, occupation, educational level, participation in local leadership and participation in other income generation means determine the decision of rural poor to join a RUSACCO or not. The amount of saving is determined by household head occupation, farming experience and income level. While age of household head, primary occupation, farming experience, date of membership, annual total consumption expenditure, amount of saving and participation in other income generation activities influence members’ amount of borrowing by RUSACCO members. Finally, the study confirms that RUSACCO participation improves household food security. RUSACCO membership has made positive impact on household total consumption expenditure and food expenditure.