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Published in Eric W. Harmsen, Megh R. Goyal, Flood Assessment, 2017
For lead-times of 10, 20 and 30 min, the storms provide an average hit rate (HR) of 0.90, 0.86 and 0.84, respectively. The hit rate score is the fraction of observed events that is forecast correctly. It ranges from zero (0) at the poor end to one (1) at the good end. The probability of detection (POD) of storms varies from 0.61, 0.50 and 0.41. While the false alarm rates (FAR) is 0.27, 0.38 and 0.46 for lead-time of 10, 20 and 30 min, respectively. Figure 17.2 shows POD values and FAR values for the complete set of storms. In the ideal situation POD should approach to one (1), while the FAR results should approach to zero (0). The performance index was 0.74, 0.66 and 0.60 for 10 min, 20 min and 30 min, respectively for the model, (Figure 17.3).
Indexing on Biometric Databases
Published in Gaurav Jaswal, Vivek Kanhangad, Raghavendra Ramachandra, AI and Deep Learning in Biometric Security, 2021
Geetika Arora, Jagdiah C. Joshi, Karunesh K. Gupta, Kamlesh Tiwari
Evaluation of an indexing scheme is done based on some parameters such as hit rate, penetration rate, and bin miss rate. Hit rate is the percentage of genuine matches that are successful at top t matches from the total number of queries made. Penetration rate refers to the percentage of database that must be returned as the candidate list for a successful retrieval. Bin miss rate is another parameter that represents fraction of genuine biometric templates misplaced in a wrong class.
A B2B flexible pricing decision support system for managing the request for quotation process under e-commerce business environment
Published in International Journal of Production Research, 2019
K.H. Leung, C.C. Luk, K.L. Choy, H.Y. Lam, Carman K.M. Lee
In this stage, parameters are identified through a series of discussions and consultations with the industry experts, followed by defining the fuzzy characteristics of each parameter. In this case study, a total of 13 parameters are identified, as shown in Table 1. Data are retrieved from various sources, such as from the internal CRM system and the back-end cloud database of the B2B e-commerce online retail site. These parameters can be categorised into three types of data: (i) Customer profile and purchasing behaviour, (ii) Product information, and (iii) Customer transaction records. Customer profile and purchasing behaviour – Customer profiles, with basic information, such as the customer ID, company name, in charge staff name, contact number, and foundation year, are retrieved and stored in the centralised cloud database in the Data storage and retrieval module of the Smart-Quo. Parameters under this category, i.e. parameters A, C, D, E, F, G, J, and L, are also gathered and stored for further data mining purpose.Product information – The details of all listed products in the B2B e-commerce retail sites are retrieved and stored in the centralised cloud database in the Data storage and retrieval module of the Smart-Quo. In particular, the historical sales performance of each product (Parameter B), and the average monthly hit rate of each product (Parameter K), that is, the number of sales of a product compared to the number of people who visit the specific product webpage to look at that product, are identified as parameters that are taken into consideration for the extraction of the relationship with other parameters.Transaction records – Historical transactions are stored in the cloud database of the Smart-Quo. Relevant data, which includes the transaction ID, customer ID, order date, delivery date, payment date, product ID, unit of products sold, order’s total price, average number of SKUs in an order, and the total value of transported goods, are retrieved along with parameters H, I, and M, which are, respectively, the number of SKUs ordered along with this product for a particular customer, the expected ordering quantity of this product for a particular customer, and the discount of this product for a particular customer.With the parameters identified, the fuzzy characteristics of each parameter, i.e. the fuzzy terms and fuzzy membership functions, are defined. While there is no clear indication of the range and type of the membership function of each parameter, users of the proposed system are recommended to evaluate and define the fuzzy characteristics based on the operating scale of the company and the opinions of the decision-makers who are used to regularly prepare quotations and in determining the quotation prices. With respect to the operating scale of the case company, the membership functions of each parameter are defined and illustrated in Figure 4(b) and Table 1.