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Publications (10 of 35) Show all publications
Wang, Z.-W. -., Wang, S.-K. -., Wan, B.-T. -. & Song, W. W. (2020). A novel multi-label classification algorithm based on K-nearest neighbor and random walk. International Journal of Distributed Sensor Networks, 16(3)
Open this publication in new window or tab >>A novel multi-label classification algorithm based on K-nearest neighbor and random walk
2020 (English)In: International Journal of Distributed Sensor Networks, ISSN 1550-1329, E-ISSN 1550-1477, Vol. 16, no 3Article in journal (Refereed) Published
National Category
Information Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-32343 (URN)10.1177/1550147720911892 (DOI)2-s2.0-85081297510 (Scopus ID)
Available from: 2020-03-23 Created: 2020-03-23 Last updated: 2020-03-23Bibliographically approved
Peng, X., Shu, W. & Song, W. W. (2019). Automatic classification of ECG signals in WBAN based on convolutional neural network and long-short term memory network. In: : . Paper presented at Proceedings - 2019 4th International Conference on Computational Intelligence and Applications, ICCIA 2019 (pp. 104-111).
Open this publication in new window or tab >>Automatic classification of ECG signals in WBAN based on convolutional neural network and long-short term memory network
2019 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-31317 (URN)10.1109/ICCIA.2019.00027 (DOI)2-s2.0-85075982749 (Scopus ID)
Conference
Proceedings - 2019 4th International Conference on Computational Intelligence and Applications, ICCIA 2019
Available from: 2019-12-18 Created: 2019-12-18 Last updated: 2019-12-18Bibliographically approved
Sarkheyli, A. & Song, W. W. (2019). Delone and McLean IS success model for evaluating knowledge sharing. In: : . Paper presented at QUAT 2018: Data Quality and Trust in Big Data, UAE November 12-15, 2018 (pp. 125-136). , 11235
Open this publication in new window or tab >>Delone and McLean IS success model for evaluating knowledge sharing
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

It is generally agreed upon that Knowledge Sharing (KS) is an effective process within organizational settings. It is also the corner-stone of many firm’s Knowledge Management (KM) Strategy. Despite the growing significance of KS for organization’s competitiveness and performance, analyzing the level of KS make it difficult for KM to achieve the optimum level of KS. Because of these causes, this study attempts to develop a conceptual model based on one of the IS Theories that is determined as the best model for evaluating the level of KS. In other words, it is Delone and McLean IS Success model that is presented according to the Communication Theory and it covers various perspectives of assessing Information Systems (IS). Hence, these dimensions cause it to be a multidimensional measuring model that could be a suitable model for realizing the level of KS.

Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Knowledge Sharing, Knowledge Managemen, t Knowledge Sharing Quality, Delone and McLean IS Success Model
National Category
Information Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30102 (URN)10.1007/978-3-030-19143-6_9 (DOI)000485167900009 ()2-s2.0-85065486267 (Scopus ID)978-3-030-19142-9 (ISBN)978-3-030-19143-6 (ISBN)
Conference
QUAT 2018: Data Quality and Trust in Big Data, UAE November 12-15, 2018
Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-09-27Bibliographically approved
Sarkheyli, A., Sarkheyli-Hägele, A. & Song, W. W. (2019). Development of Dynamic Intelligent Risk Management Approach. In: Proceedings of 3rd International Conference on Computational Intelligence and Applications: . Paper presented at The 3rd IEEE International Conference on Computational Intelligence and Applications, July 28-30, 2018, Hong Kong (pp. 128-132). IEEE, Article ID 8711543.
Open this publication in new window or tab >>Development of Dynamic Intelligent Risk Management Approach
2019 (English)In: Proceedings of 3rd International Conference on Computational Intelligence and Applications, IEEE, 2019, p. 128-132, article id 8711543Conference paper, Published paper (Refereed)
Abstract [en]

A dynamic Risk Management (RM) provides monitoring, recognition, assessment, and follow-up action to reduce the risk whenever it rises. The main problem with dynamic RM (when applied to plan for, how the unknown risk in unexpected conditions should be addressed in information systems) is to design an especial control to recover/avoid of risks/attacks that is proposed in this research. The methodology, called Dynamic Intelligent RM (DIRM) is comprised of four phases which are interactively linked; (1) Aggregation of data and information (2) Risk identification (3) RM using an optional control and (4) RM using an especial control. This study, therefore, investigated the use of artificial neural networks to improve risk identification via adaptive neural fuzzy interface systems and control specification using learning vector quantization. Further experimental investigations are needed to estimate the results of DIRM toward unexpected conditions in the real environment.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Information Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28465 (URN)10.1109/ICCIA.2018.00031 (DOI)000470235800024 ()978-0-7695-6528-6 (ISBN)
Conference
The 3rd IEEE International Conference on Computational Intelligence and Applications, July 28-30, 2018, Hong Kong
Available from: 2018-09-06 Created: 2018-09-06 Last updated: 2019-06-27Bibliographically approved
Wang, J., Liu, G. & Song, W. W. (2019). Firefly algorithm with proportional adjustment strategy. In: : . Paper presented at QUAT 2018: Data Quality and Trust in Big Data, UAE, 12-15 November, 2018 (pp. 78-93). , 11235
Open this publication in new window or tab >>Firefly algorithm with proportional adjustment strategy
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Firefly algorithm is a new heuristic intelligent optimization algorithm and has excellent performance in many optimization problems. However, in the face of some multimodal and high-dimensional problems, the algorithm is easy to fall into the local optimum. In order to avoid this phenomenon, this paper proposed an improved firefly algorithm with proportional adjustment strategy for alpha and beta. Thirteen well-known benchmark functions are used to verify the performance of our proposed algorithm, the computational results show that our proposed algorithm is more efficient than many other FA algorithms.

Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Firefly algorithm, Global optimization, Intelligent optimization algorithm, Meta-heuristic algorithm
National Category
Information Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30105 (URN)10.1007/978-3-030-19143-6_6 (DOI)000485167900006 ()2-s2.0-85065479081 (Scopus ID)978-3-030-19142-9 (ISBN)978-3-030-19143-6 (ISBN)
Conference
QUAT 2018: Data Quality and Trust in Big Data, UAE, 12-15 November, 2018
Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-09-27Bibliographically approved
Wan, B., Qin, Y. & Song, W. W. (2019). Path planning strategy of mobile nodes based on improved RRT algorithm. In: Proceedings of 3rd International Conference on Computational Intellligence and Applications: . Paper presented at The 3rd IEEE International Conference on Computational Intellligence and Applications, July 28-30, 2018, Hong Kong (pp. 228-234). IEEE, Article ID 8711533.
Open this publication in new window or tab >>Path planning strategy of mobile nodes based on improved RRT algorithm
2019 (English)In: Proceedings of 3rd International Conference on Computational Intellligence and Applications, IEEE, 2019, p. 228-234, article id 8711533Conference paper, Published paper (Refereed)
Abstract [en]

The RRT algorithm is widely used in the high-dimensional path planning in a dynamic environment, and well adapted to the dynamics of motion of the mobile node needs. However, in large scale wireless sensor networks (WSN), the RRT algorithm lacks stability and is easy to deviate from the optimal path. In this paper we proposes a path planning algorithm called E-RRT to improve the problems that RRT has. The method proposed includes the coverage density of obstacle for initialize searching area for the exploring random tree, and the gradually extended region used to ensure the path to be found. The method also adopts the greedy algorithm to delete the intermediate point in the point sequence of path for an optimal path, and the quadratic Bezier curve to smooth the path for the mobile sensor node. The path found can be the shortest, collision-free and smoothing, and therefore to satisfy the requirement of path planning for mobile sensor nodes. The simulation results show that the E-RRT algorithm outperforms the RRT algorithm.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Information Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28466 (URN)10.1109/ICCIA.2018.00051 (DOI)000470235800044 ()2-s2.0-85066330989 (Scopus ID)9780769565286 (ISBN)
Conference
The 3rd IEEE International Conference on Computational Intellligence and Applications, July 28-30, 2018, Hong Kong
Available from: 2018-09-06 Created: 2018-09-06 Last updated: 2019-06-27Bibliographically approved
Song, W. W., Lin, C., Forsman, A., Avdic, A. & Åkerblom, L. (2017). An Euclidean similarity measurement approach for hotel rating data analysis. In: Proceedings 2017 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017: . Paper presented at 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017 (pp. 293-298).
Open this publication in new window or tab >>An Euclidean similarity measurement approach for hotel rating data analysis
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2017 (English)In: Proceedings 2017 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017, 2017, p. 293-298Conference paper, Published paper (Refereed)
Abstract [en]

The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the 'cold start' problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.

Keywords
collaborative filtering, ranking systems, recommendation systems, ROC curves
National Category
Information Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-25650 (URN)10.1109/ICCCBDA.2017.7951927 (DOI)000414283700054 ()2-s2.0-85024390956 (Scopus ID)978-1-5090-4498-6 (ISBN)978-1-5090-4499-3 (ISBN)
Conference
2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017
Available from: 2017-07-31 Created: 2017-07-31 Last updated: 2018-01-13Bibliographically approved
Song, W. W., Zhang, S. & Ding, M. (2016). A combined prediction model of container volume based on residual correction. In: : . Paper presented at 25th International Conference on Information Systems Development (ISD 2016), Katowice, Poland, 24-26 August 2016.
Open this publication in new window or tab >>A combined prediction model of container volume based on residual correction
2016 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Semantic analysis is an important part of natural language processing, and it has a broadly application in the network information processing. This paper presents a semantic analysis framework based on the directed weighted graph. The paper uses a directed weighted graph for semantic classification. The methodology takes the information semantic analysis as the goal in network-oriented approach, particularly in E-commerce user reviews. It looks the formal semantics lexical as semantic body and denoted by nodes. It uses links to represent relationship between the nodes and calculated by weights. Directed links are used to connect to each other in nodes, which semantic vocabulary is interrelated between nodes. Then a directed weighted graph is constructed by semantic nodes and their interrelationships relations. The experimental results and analysis show that the proposed method in the paper can classify the semantics into different classification according to the path length threshold requirement.

Keywords
Directed Weighted Graph, Reviews, Semantic Classification
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-22831 (URN)
Conference
25th International Conference on Information Systems Development (ISD 2016), Katowice, Poland, 24-26 August 2016
Available from: 2016-08-22 Created: 2016-08-22 Last updated: 2018-01-10Bibliographically approved
Zhang, S., Song, W. W., Ding, M. & Hu, P. (2016). A multi-semantic classification model of reviews based on directed weighted graph. In: Wojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang (Ed.), Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part II. Paper presented at 17th International Conference, Shanghai, China, November 8-10, 2016 (pp. 424-435). , 10042
Open this publication in new window or tab >>A multi-semantic classification model of reviews based on directed weighted graph
2016 (English)In: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part II / [ed] Wojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang, 2016, Vol. 10042, p. 424-435Conference paper, Published paper (Refereed)
Abstract [en]

Semantic and sentimental analysis plays an important role in natural language processing, especially in textual analysis, and has a wide range of applications in web information processing and management. This paper intends to present a sentimental analysis framework based on the directed weighted graph method, which is used for semantic classification of the textual comments, i.e. user reviews, collected from the e-commerce websites. The directed weighted graph defines a formal semantics lexical as a semantic body, denoted to be a node in the graph. The directed links in the graph, representing the relationships between the nodes, are used to connect nodes to each other with their weights. Then a directed weighted graph is constructed with semantic nodes and their interrelationships relations. The experimental results show that the method proposed in the paper can classify the semantics into different classification based on the computation of the path lengths with a threshold. © Springer International Publishing AG 2016.

Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10042
Keywords
Directed weighted graph, Reviews, Semantic classification
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-23518 (URN)10.1007/978-3-319-48743-4_35 (DOI)000389505500035 ()2-s2.0-84995922775 (Scopus ID)978-3-319-48743-4 (ISBN)978-3-319-48742-7 (ISBN)
Conference
17th International Conference, Shanghai, China, November 8-10, 2016
Available from: 2016-12-02 Created: 2016-12-02 Last updated: 2018-01-13Bibliographically approved
Song, W. W., Lin, C., Avdic, A., Forsman, A. & Åkerblom, L. (2016). Collaborative Filtering with Data Classification: A Combined Approach to Hotel Recommendation Systems. In: 25th International Conference on Information Systems Development (ISD2016 Poland): . Paper presented at 25th International Conference on Information Systems Development (ISD2016 Poland), Katowice, Poland, August 24-26, 2016.
Open this publication in new window or tab >>Collaborative Filtering with Data Classification: A Combined Approach to Hotel Recommendation Systems
Show others...
2016 (English)In: 25th International Conference on Information Systems Development (ISD2016 Poland), 2016Conference paper, Published paper (Refereed)
Abstract [en]

Recommendation systems aim to help users make decisions more efficiently. The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the "cold start" problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.

Keywords
Recommendation systems, collaborative filtering, ranking systems, ROC curves
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-22830 (URN)
External cooperation:
Conference
25th International Conference on Information Systems Development (ISD2016 Poland), Katowice, Poland, August 24-26, 2016
Available from: 2016-08-22 Created: 2016-08-22 Last updated: 2018-01-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3681-8173

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