Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/

Occurrence of Verocytotoxin-Producing Escherichia coli O157 on Dutch Dairy Farms

During the period from September 1996 through November 1996, 10 Dutch dairy farms were visited to collect fecal samples from all cattle present. The samples were examined for the presence of verocytotoxin (VT)-producing Escherichia coli (VTEC) of serogroup O157 (O157 VTEC) by immunomagnetic separation following selective enrichment. Cattle on 7 of the 10 dairy farms tested positive for O157 VTEC, with the proportion of cattle infected varying from 0.8 to 22.4%. On the seven farms positive for O157 VTEC, the excretion rate was highest in calves ages 4 to 12 months (21.2%). In a follow-up study, two O157 VTEC-positive farms and two O157 VTEC-negative farms identified in the prevalence study were revisited five times at intervals of approximately 3 months. Cattle on each farm tested positive at least once. The proportion of cattle infected varied from 0 to 61.0%. Excretion rates peaked in summer and were lowest in winter. Again, the highest prevalence was observed in calves ages 4 to 12 months (11.8%). O157 VTEC strains were also isolated from fecal samples from horses, ponies, and sheep and from milk filters and stable flies. O157 VTEC isolates were characterized by VT production and type, the presence of the E. coli attaching-and-effacing gene, phage type, and pulsed-field gel electrophoretic genotype. No overlapping strain types were identified among isolates from different farms except one. The predominance of a single type at each sampling suggests that horizontal transmission is an important factor in dissemination of O157 VTEC within a farm. The presence of more than one strain type, both simultaneously and over time, suggests that there was more than one source of O157 VTEC on the farms. Furthermore, this study demonstrated that the O157 VTEC status of a farm cannot be ascertained from a single visit testing a small number of cattle