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Customer Relationship Management is important in analyzing business performance. Predicting customer buying behavior enables the business to better address their customers and enhance service level and overall profit. This paper focuses on proposing a model that predicts future period sales in a real retail department store with low prediction error rate, and it also discovers the main sales trends over time. A model based on the Prophet algorithm is implemented and modified according to different parameters in order to lower the prediction error. The modifications consisted of the insertion of a new seasonality pattern, changes in the Fourier order of the existing and the new seasonality pattern, inclusion of the holiday data, and parameterizing its impact. The performance of the standard and modified model is evaluated in terms of the MAE (mean absolute error) and MAPE (mean absolute percentage error). The standard and the modified model were tested on a real dataset consisting of the sales between 2011-2019 in a department store of a shopping center in Albania. Implementation results show that the MAE in sales prediction for the modified model is reduced, while the MAPE in sales prediction for the modified model was measured for prediction periods. The implementation results indicate a comparable or evenbetter performance than the standard model. different

Biometric devices identify persons based on the minutiae extracted from fingerprint images. Image quality is very important in this process. Usually, fingerprint images have low quality and in many cases they are obtained in various positions. The paper focuses on increasing minutiae detected number by fusing two fingerprint images obtained in various positions. Biorthogonal wavelets have advantages compared to orthogonal wavelets. Fusion is performed in wavelet domain by implementing biorthogonal wavelet. Terminations and bifurcations are extracted from the original and fused images using licensed software Papillon 9.02 and manually extraction by an expert. Biorthogonal Wavelet transform is implemented in the image fusion process, yielding in the increased number of the minutiae compared to the original one. Different biorthogonal wavelets are experimented and various results are obtained. Finding the appropriate wavelet is important in the fusion process since it has a direct impact in the number of minutiae extracted. Based on the number of minutiae and MSE results, the appropriate wavelet to be used in the fusion process is defined.

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