A hybrid genetic algorithm based approximate cash crop model with support vector machine classifier framework for predicting economic viability of underutilised crop in rural area

The research deals with developing a hybrid Genetic Algorithm based on approximate cash crop model framework to predict economic viability of underutilised crop in rural area using Support Vector Machine. Machine learning techniques are dependent on large amount of data in order to predict outcomes. However, underutilised crops are, by definition, not cultivated on a large commercial scale causes the scarcity of training data for this application. In this proposed framework, SVM is implemented in conjunction with a Genetic Algorithm (GA) and associated fitness functions to generate training data for the SVM from approximate models developed for normal cash crops. Approximate models are used in Genetic Algorithm as fitness function to generate synthetic data. Synthetic data generated is used to train Support Vector Machine. Experiments are designed to compare synthetic data from actual models and data generated from approximate models. Model and real data from World Bank is also used to validate the proposed framework. Finally, synthetic data generated from approximate farm income models for crop are tested against the real village data obtained from Crops For the Future Research Centre. The result shows that good classification is attainable in spite of the inaccuracy that existed in the training data from an approximate model and artificially generated through Genetic Algorithm which includes constraints that reflect physical conditions found in rural villages. This framework is able to identify village which is able to achieve potential success economically planting underutilised crops before cultivating the crop itself based on the approximate model from village who has successfully commercialised normal crops

Methylenetetrahydrofolate reductase C677T polymorphism in patients with lung cancer in a Korean population

<p>Abstract</p> <p>Background</p> <p>This study was designed to investigate an association between methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism and the risk of lung cancer in a Korean population.</p> <p>Methods</p> <p>We conducted a large-scale, case-control study involving 3938 patients with newly diagnosed lung cancer and 1700 healthy controls. Genotyping was performed with peripheral blood DNA for MTHFR C677T polymorphisms. Statistical significance was estimated by logistic regression analysis.</p> <p>Results</p> <p>The MTHFR C677T frequencies of CC, CT, and TT genotypes were 34.5%, 48.5%, and 17% among lung cancer patients, and 31.8%, 50.7%, and 17.5% in the controls, respectively. The MTHFR 677CT and TT genotype showed a weak protection against lung cancer compared with the homozygous CC genotype, although the results did not reach statistical significance. The age- and gender-adjusted odds ratio (OR) of overall lung cancer was 0.90 (95% confidence interval (CI), 0.77-1.04) for MTHFR 677 CT and 0.88 (95% CI, 0.71-1.07) for MTHFR 677TT. However, after stratification analysis by histological type, the MTHFR 677CT genotype showed a significantly decreased risk for squamous cell carcinoma (age- and gender-adjusted OR, 0.78; 95% CI, 0.64-0.96). The combination of 677 TT homozygous with 677 CT heterozygous also appeared to have a protection effect on the risk of squamous cell carcinoma. We observed no significant interaction between the MTHFR C677T polymorphism and age and gender or smoking habit.</p> <p>Conclusions</p> <p>This is the first reported study focusing on the association between MTHFR C677T polymorphisms and the risk of lung cancer in a Korean population. The T allele was found to provide a weak protective association with lung squamous cell carcinoma.</p