| 1. |
- Bai, Guohua, et al.
(författare)
-
Coping with System Sustainability : A Sociocybernetics Framework for Social-Economic System Architecture
- 2012
-
Ingår i: Systems research and behavioral science. - : Wiley Blackwell. - 1092-7026 .- 1099-1743. ; 29:3, s. 263-273
-
Tidskriftsartikel (refereegranskat)abstract
- This paper proposes an epistemological model based on cybernetic principles and activity theory to interpret two levels of problems that are intertwined in our social-economic system, namely the liveability and sustainability problems. In the first part of the paper, important principles and concepts from related fields of cybernetics and activity theory are introduced for later construction of a model. In the second part, a model is constructed based on the introduced concepts. To validate the proposed model, the current economic crisis is studied in the third part. An important contribution of the proposed model is a theoretical understanding of the two levels problems, and how to construct macro social-economical policies to avoid similar crisis in the future.
|
|
| 2. |
- Bakhtyar, Shoaib, et al.
(författare)
-
Freight transport prediction using electronic waybills and machine learning
- 2014
-
Ingår i: 2014 International Conference on Informative and Cybernetics for Computational Social Systems. - : IEEE Computer Society. - 9781479947539 ; , s. 128-133
-
Konferensbidrag (refereegranskat)abstract
- A waybill is a document that accompanies the freight during transportation. The document contains essential information such as, origin and destination of the freight, involved actors, and the type of freight being transported. We believe, the information from a waybill, when presented in an electronic format, can be utilized for building knowledge about the freight movement. The knowledge may be helpful for decision makers, e.g., freight transport companies and public authorities. In this paper, the results from a study of a Swedish transport company are presented using order data from a customer ordering database, which is, to a larger extent, similar to the information present in paper waybills. We have used the order data for predicting the type of freight moving between a particular origin and destination. Additionally, we have evaluated a number of different machine learning algorithms based on their prediction performances. The evaluation was based on their weighted average true-positive and false-positive rate, weighted average area under the curve, and weighted average recall values. We conclude, from the results, that the data from a waybill, when available in an electronic format, can be used to improve knowledge about freight transport. Additionally, we conclude that among the algorithms IBk, SMO, and LMT, IBk performed better by predicting the highest number of classes with higher weighted average values for true-positive and false-positive, and recall.
|
|
