By Inti Y. González-Herrera, Carlos Pérez Risquet (auth.), Paulina Golinska, Marek Fertsch, Jorge Marx-Gómez (eds.)
This monograph comprises contemporary experiences in eco-informatics, promising principles
and new demanding situations in info administration for aiding sustainability in
companies and different association. The scope of this publication contains units of
solutions which exhibit varied stakeholders’ viewpoints on sustainability. In person chapters, authors talk about the function which Environmental
Information structures (EIS) play within the environmental wakeful functioning of
enterprise. New types, equipment and instruments helping sustainability are
presented. Emphasis is put on the leading edge method of eco-friendly
organization and coordination of shipping, logistics procedures and operations
management. the knowledge administration and choice making in
manufacturing and repair enterprises is highlighted.
The scope of this monograph additionally encompasses issues regarding the modeling
and tracking of weather change.
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Extra resources for Information Technologies in Environmental Engineering: New Trends and Challenges
Sample text
After the documents are preprocessed, the documents are gone through the data division process to two portions: training and testing documents for future uses. Then the classifying module is to classify the testing documents into different classes or labels after the system is trained by training documents. In the training phase, the training data are first processed by the SVM classifier (binary) and the k*(k-1)/2 decision functions are obtained. After the decision functions are found, the system goes to the testing phase.
Advances in Neural Information Processing Systems, 14, 681-687. [6] Egghe, L. & Michel, C. (2003). Construction of weak and strong similarity measures for ordered sets of documents using fuzzy set techniques. Information Processing and Management, 39, 771-807. [7] Friedman, J. (1996). Z. , 1999. Neural Networks. New Jersey: Practice-Hall Press. [9] Joachims, T. (1998). Text categorization with support machines: learning with many features. In proceedings 10th Europen Conference on machine learning (ECML) Chemnitz: Springer-Verlag (pp.
8min. 4min. System generation 42sec. 51sec. 33sec. ” denotes “improvement A Model for Generating Outputs 33 Table 3. The system reasoning results compared with the subjective selection results in moving distance Performan ce Index Idle 1 to 3 minutes Avg. MD Avg. Diff. Imp. rate Idle 1 to 10 minutes Avg. MD Avg. Diff. Imp. rate Idle 1 to 20 minutes Avg. MD Avg. Diff. Imp. ” denotes “improvement Fig 9. The comparison results of user waiting time Fig 10. The comparison results of user moving distance The system reasoning results can shorten the waiting time although the user is unable to get to the target object (parking space) with shortest distance.
