Pemilihan Jenis Asuransi Berdasarkan Demografi Calon Pemegang Polis dengan Metode Naïve Bayes Classifier

Asuransi merupakan salah satu cara untuk memproteksi diri di masa depan. Saat ini, Perusahaann asuransi berlomba-lomba untuk menawarkan produk asuransi yang menjanjikan. Dalam rangka bersaing dengan kompetitor lainnya dan demi memenuhi kebutuhan nasabahnya, Perusahaan asuransi memerlukan startegi bisnis yang bijak dan tepat agar produknya mendapat respon positif dari calon nasabah. Salah satu permasalahan dalam bidang asuransi adalah bagaimana menentukan jenis asuransi yang tepat untuk calon nasabah. Pada paper ini, dibahas tentang bagaimana menetukan jenis asuransi yang tepat menggunakan task dalam data mining untuk menggali informasi yang berkaitan dengan kebutuhan produk asuransi bagi calon nasabah. Metode yang digunakan untuk klasifikasi adalah Naïve Bayes Classifier. Hasil uji coba menunjukkan bahwa metode NBC mampu mengklasifikasi record dengan tingkat kinerja tertinggi sebesar 94.12% ketika proporsi pembagian data latih 90% dan data uji sebesar 10%. Karena kinerja sistem yang dihasilkan dapat dikatakan baik, sistem dianggap kredibel untuk merekomendasikan produk asuransi kepada calon nasaba

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

Dietary fiber from Tunisian common date cultivars (Phoenix dactylifera L.): Chemical composition, functional properties, and antioxidant capacity

30 Páginas.-- 2 Figuras.-- 4 TablasThe dietary fibers (DF) of 10 date varieties from Tunisian oases have been investigated. Further knowledge on the content, composition, and technological applications of those fibers could support their genetic variability and promote the socioeconomical development of growing areas. The composition, water- and oil-holding capacities, solubility, and antiradical activity have been determined. The DF content ranged from 4.7% (Matteta, Rochdi) to >7% (Deglé Nour, Garen Gaze, Smeti). Composition varied significantly among cultivars, and the results evidenced that uronic acids and lignin determine to a great extent the organoleptic quality of dates. Many of the varieties that have been studied (Garen Gaze, Matteta, Kenta, Rochdi, Mermella, Korkobbi, Eguwa) were selected because of great interest from technological and functional points of view. Among their physicochemical characteristics, these samples presented water- and oil-holding capacities of higher than 17 and 4 mL/g fiber, respectively, which make them suitable for use as additives in fiber-enriched foods. Also, DF of Garen Gaze, Smeti, Mermella, and Eguwa had a high antiradical capacity (>230 Trolox equiv/kg fiber). It was concluded that some of these varieties could be grown as potential sources of DF, which could be included in the formulation of fiber- and antioxidant-enriched foods. © 2012 American Chemical Society.We are grateful to the Institut des Regions Arides (Medenine, Tunisia) for the financial support of travel expenses of A.M. for his stay with the Instituto de la Grasa (Sevilla, Spain)

Fast Segmentation of Foreign Fiber Image

Part 1: Simulation, Optimization, Monitoring and Control TechnologyInternational audienceIn the textile industry, different types of foreign fibers may be mixed in cotton, and the foreign fibers seriously affect the quality of cotton products. The step of image segmentation is of vital importance in the process of the foreign fibers identification, which is, in the same way, the foundation for cotton foreign fiber automated inspection. This paper presents a new approach for fast segmentation of foreign fiber images. This approach includes four main steps, i.e., image transformation, image block, image background extraction, image enhancement and segmentation. In the first step, we transform the captured color images into gray-scale images, and invert the color of the transformed images. In the second step, the proportion relationship between target image and background was analyzed, and then the whole foreign fibers image was divided into several blocks based on the analysis results. In the third step, the background of foreign fiber image was extracted by image corrosion and gray-level correction. In the final step, the histogram of the gray-scale image was analyzed, and a piecewise linear transform model was proposed to enhance the image blocks based on the analysis results, and then the image blocks were segmented by Otsu’s method. The experiment results indicate that the proposed method can segment the foreign fiber image directly and precisely, and the speed of image processing is much faster than that of the conventional methods