By Himansu Sekhar Behera, Durga Prasad Mohapatra

The publication is a suite of top quality peer-reviewed examine papers offered within the moment overseas convention on Computational Intelligence in info Mining (ICCIDM 2015) held at Bhubaneswar, Odisha, India in the course of five – 6 December 2015. The two-volume complaints tackle the problems and demanding situations for the seamless integration of 2 center disciplines of computing device technology, i.e., computational intelligence and knowledge mining. The e-book addresses assorted tools and methods of integration for boosting the general objective of information mining. The publication is helping to disseminate the information approximately a few leading edge, energetic learn instructions within the box of information mining, computer and computational intelligence, besides a few present matters and functions of comparable topics.

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Additional resources for Computational Intelligence in Data Mining—Volume 1: Proceedings of the International Conference on CIDM, 5-6 December 2015

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J. Autom. Control Eng. (JOACE) 3(2) (2015) 2. : Ear recognition using kernel based algorithm. Int. J. Sci. Eng. Technol. Res. (IJSETR) 3(4) (2014) 3. : Person recognition using multimodal biometrics. Int. J. Emerg. Technol. Adv. Eng. ISSN:2250–2459; ISO 9001:2008 Certified Journal, 4(4) (2014) 4. : 2D-3D face recognition method based on a modified CCA-PCA Algorithms (2014). 5772/58251 A Novel Approach for Biometric Authentication System … 11 5. , Rajni: Face recognition based on PCA algorithm using simulink in matlab.

Doc files and work with JSON like Objects called BSON [7]. MongoDB is a Full-Featured database because it supports rich querying, Implementation of Data Analytics for MongoDB … 41 real time aggregation, traditionally consistent. Others features are fully-consistent reads, secondary indexes, atomic write and query language [8]. MongoDB offers very flexible and dynamic schema to store documents in JSON format [9]. Arrays within other documents and embedding of documents are supported by MongoDB BSON [10–12].

Oper. Res. Soc. 40, 75–81 (1989) 2. : An EOQ model for deteriorating items with time varying demand and partial backlogging. J. Oper. Res. Soc. 50, 1176–1182 (1999) 3. : (T, Si) policy inventory model for deteriorating items with time proportional demand. J. Oper. Res. Soc. 32, 137–142 (1981) 4. : Recent trends in modeling of deteriorating inventory. Eur. J. Oper. Res. 134, 1–16 (2001) 5. : Inventory replenishment policies for deteriorating items in a declining market. Int. J. Prod. Res. 21, 813–826 (1983) 6.

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