TY - THES A1 - Tanzin, Tamanna T1 - Assessment of growth and yield potential of different vegetable amaranth type in Kleve, Germany N2 - Amaranthus belongs to the Amaranthaceae family which has approximately 70 species. It is considered as an underutilized plant but some research in the recent years showed that it is a promising plant for future nutrition supply. Amaranths are widely known for its nutritional value and their easy growing habit. This underutilized plant can become a good option for the commercial growers because it is easy to grow, it has high nutritional value and low maintenance cost. Many species of the Amaranthus genus are known as annual weeds, some are used as a vegetable and ornamental plants, some are known as pseudocereals which are mainly used for their grain. The core objective of this study was to assess the growth and yield potential of different vegetable amaranths to select a suitable variety for Kleve, Germany. Eight vegetable amaranths variety (Leafy variety and Stem variety) were grown in the experimental garden of Hochschule Rhein-Waal University. A seed germination test was also conducted at thelife science lab to observe the best seed germination rate at different temperatures. Plant height, number of leaves per plant were measured continuously after two weeks of sowing till the harvest week. The fresh and dry weight of leaf, stem and root were measured after final harvest. The statistical analysis of growth and yield performance of stem and leafy varieties were done separately. Leafy varieties showed a high variation in yield and germination rate than stem varieties. Leafy variety 'Surinamese Spinach'(L7) and 'Marog Green' (L8) excelled in all yield parameters whereas 'Chinese Spinach' (L6) was consistent in low growth and yield. Stem variety 'Danta Bhutan' (S2) and 'Danta Lusina' (S3) showed low yield performance. All the varieties showed a high germination rate above 20°C in seed germination test. Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-2711 ER - TY - THES A1 - Nawaz, Muhammad Ahsan T1 - Automatic Classification of a Customer’s Vertical Market by Using Text Mining N2 - The German logistics company Schenker AG categorizes its customers into so-called vertical markets. For instance; category “Automotive” is assigned to car manufacturer BMW AG. The classification allows the company to evaluate its revenue and profits on different customer segments which, in turn, has an impact on Schenker’s strategic planning. Until now the assignment is carried out manually which means that someone from sales department should perform some research on a customer’s public profile whenever a new customer is registered in the database. With the rapid growth of global trade in recent years and Schenker’s expansion to the Asian market with thousands of new customers the manual approach is no longer sustainable in a global market. This thesis provides an alternative solution based on scraping customer data available in the web and classification of the extracted content. We deal with three significant difficulties: find web data related to a given customer name (we do have a company name but no homepage URL in Schenker database), extract a predictive portion of the data without introducing too much noise and, finally, set up a classification algorithm. Most importantly, the whole process needs to be implemented automatically. For the classification task, we have identified two tree-based classification algorithms random forest and extreme gradient boosting (xgboost). Random forest performs better by using package ranger with an overall 52% accuracy and 87.8% multiclass area under the curve. On the other hand, xgboost takes less time to compute, but the accuracy is poor as compared to random forest. KW - Text classification KW - Natural language processing Y1 - 2018 ER -