@article {466,
	title = {Research on flexible customization of business function for intelligent traffic cloud platform},
	journal = {Journal of Digital Information Management},
	volume = {15},
	year = {2017},
	note = {Export Date: 22 February 2018},
	month = {2017},
	pages = {312 - 319},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041029410\&partnerID=40\&md5=4bd92bd9fccdbba84441a3fd6d5cfca8},
	author = {Pan, H. and Huang, Z. and He, Z.}
}
@article {441,
	title = {Resolving rule conflicts based on Na{\"\i}ve Bayesian model for associative classification},
	journal = {Journal of Digital Information Management},
	volume = {12},
	year = {2014},
	note = {Export Date: 10 July 2014},
	month = {2014},
	pages = {36 - 43},
	abstract = {The rule conflict is an important issue for associative classification due to a large set of rules. In this paper, a new approach called Associative Classification with Bayes (AC-Bayes) is proposed. To address rule conflicts, AC-Bayes has two distinguished features: (1) Associative classification is improved. (2) Na{\"\i}ve Bayesian model is applied in process of classification. A small set of high quality rules is generated by discovering not only frequent but mutual associated itemsets. Therefore, it will reduce the opportunities of rule conflicts. AC-Bayes also selects the best n rules to predict the class value of new instances. When rule conflicts are occurred, we collect those instances covered by these matched rules to form a new training set. Then, it scans new training set to compute the posterior probabilities of each class conditioned on the test instance. The class value that maximizes the posterior probability is assigned to the test instance. The experiments{\textquoteright} results show that the improved associative classification decreases significantly the number of rules and AC-Bayes has better average classification accuracy in comparison with associative classification and Na{\"\i}ve Bayesian classification.},
	keywords = {Associative classification, Bayes, Data mining, Rule conflicts},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84900460217\&partnerID=40\&md5=bec565675923a67f04838dadf519210d},
	author = {Huang, Z. and Zhou, Z. and He, T.}
}
@article {394,
	title = {Design of a teaching robot based on fischertechnik model},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {190 - 193},
	abstract = {This paper designed and developed a teaching robot - three degrees of freedom manipulator based on Fischer creative combination model platform. Firstly, the overall structure of the robot was analyzed to determine the drive programs, and then the manipulator structure was assembled and established in accordance with the modular design approach. Finally, the robot control system was studied. This paper focuses on the hardware design and control system of the robot. Practice has proved that the teaching robot we designed has the advantages of low cost, easy to regulation, good repeatability and it has good promotion and application prospects in the teaching field.},
	keywords = {Fischechnik model, PLC control system, Teaching robot},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84880818201\&partnerID=40\&md5=ee65643ea046ef1e8cf9d62466a1f2b1},
	author = {Li, Z. and Huang, Z.}
}
