Forthcoming articles

International Journal of Collaborative Intelligence

International Journal of Collaborative Intelligence (IJCI)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Collaborative Intelligence (7 papers in press)

Regular Issues

  • An Assessment of Classification with Hybrid Methodology for Neural Network Classifier against different classifier   Order a copy of this article
    by Aakanksha Jain, Abhishek Kumar, Jyotir Moy Chatterjee, Pramod Rathore 
    Abstract: This research is an assessment of Classification with Neural Network Classifier (NNC) against various Classifiers centered on working effectiveness of various classifiers. We have taken Na
    Keywords: Naive Bayes classifier (NBC); Trees Classifier; Simple Cart; Rule Classification.

  • An efficient density-based clustering algorithm with circle-filtering strategy   Order a copy of this article
    by Xiao Xu 
    Abstract: Recently a density peaks clustering algorithm (DPC) was proposed to obtain arbitrary shapes of the clusters effectively. The cluster centers are discovered by finding density peaks according to the decision graph which drawn based on the density-distance. However, the computational complexity is extremely high for calculating the density-distance of each point, which limits the application of DPC for the large-scale data sets. To overcome this limitation, an efficient density-based clustering algorithm with circle-filtering strategy (CFC) is proposed. CFC algorithm removes useless points with sparse local density based on a circle-filtering strategy first, and then the cluster centers are selected only by the remaining points to achieve the correct clusters rapidly. Theoretical analysis and experimental results show that the novel CFC algorithm can reduce the computational complexity on the basis of ensuring the accuracy of clustering effectively, and CFC outperforms DPC.
    Keywords: density peaks clustering algorithm; circle-filtering strategy; large-scale data set; decision graph; computational complexity.

  • Energy-based Structural Least Square Twin Support Vector Machine   Order a copy of this article
    by Songhui Shi 
    Abstract: Twin support vector machine (TWSVM) is a machine learning algorithm improved on the basis of support vector machine (SVM). Because of its simple model, fast training speed and excellent classification performance, it has attracted extensive attention from scholars at home and abroad. It has been shown that the structural information of data as priori knowledge can improve the generalization ability of classifiers. In this paper, we propose an energy-based structural least square twin support vector machine (ES-LSTWSVM). First we perform cluster analysis on each class, then compute the covariance matrix of each cluster in the classes and introduce it into the objective function. In order to improve the generalization ability of the algorithm, we introduce an energy factor for each hyperplane, and convert the equality constraint into an energy-based form on the basis of least squares. Finally, we adopt the "all-versus-one" strategy to let the proposed algorithm solve the multi-classification problem. The experimental results show that ES-LSTWSVM has good classification performance.
    Keywords: TWSVM; Least square; Structural information; Energy-based model.

  • A Recurrent Neural Network Based on Attention Mechanism to Predict the Trend of Univariate Time Series   Order a copy of this article
    by Yunxin Liu 
    Abstract: For the time series with high acquisition frequency and high noise, it is very difficult to directly build a prediction model because of the similarity between its values and the large noise interference. If we simply take their average values, we will lose a lot of change trend information. Therefore, this article studies how to accurately obtain the change trend information of the time series, and builds an accurate time-series trend prediction model based on the obtained change trend information. We propose a time-series trend prediction model based on K-means clustering. The first step of the model is to obtain the change trend information of the original time series based on the K-means clustering idea, and the second step is to use the gated recurrent unit based on the input attention mechanism to establish a prediction model for the obtained change trend information, and then predict the future trend of the time series. Experiments on the Electromagnetic Radiation dataset, the AEP_hourly dataset, and the Wind Turbine Scada dataset show that the proposed K-means clustering method can effectively reduce noise interference and accurately obtain time-series change trend information. Comparative experiments on different prediction models show that our proposed gated recurrent unit prediction model based on the input attention mechanism has the best prediction accuracy.
    Keywords: time series; change trend prediction; K-means clustering; attention mechanism; gated recurrent unit.

  • Marketing Information and Intelligence and their Roles in Generating Customer Insights   Order a copy of this article
    by Pratap Chandra Mandal 
    Abstract: Businesses revolve around customers. Companies require knowing and understanding their customers well. To achieve this, companies require knowing customer requirements and having customer insights. Customer insights cannot be generated unless companies have processes in place to collect marketing information and marketing intelligence. The focus is to do a detailed discussion about the various aspects involved in collection of marketing information and marketing intelligence for generating customer insights. Companies require having an efficient marketing information system in place. Marketing information systems help in capturing and storing information about customers. The paper also focuses on competitive intelligence to have a better understanding of customers. Companies require following ethical practices in collecting and analyzing marketing intelligence. The paper focuses on this important aspect of business. Proper coordination and utilization of marketing information, marketing intelligence, and marketing information systems will help companies to generate better customer insights and develop customer relationships.
    Keywords: marketing information systems; internal data; analysis of information; privacy issues; customer requirements; marketing research; qualitative technique; customer relationship.

  • Coverage Dynamic Optimization for Video Sensor Networks Based on PSO Algorithm   Order a copy of this article
    by Chenglong Zhang 
    Abstract: Under the deterministic deployment of the video sensor nodes, the nodes are fixed in position and adjustable in direction. In order to improve the coverage of video sensor networks, a multi-group directional sensing model is designed, and a coverage dynamic optimization for video sensor networks based on particle swarm optimization algorithm is proposed. The designed algorithm adopts cosine adaptive inertia weight, and adaptively adjusts it from large to small with the number of iterations. It can improve the coverage rate of networks by optimizing the sensing model. The experimental results show that the proposed algorithm has better results of different sensing angles, and can improve coverage rate in the condition of deterministic deployment of nodes.
    Keywords: Coverage rate; Dynamic optimization; Video sensor networks; Particle Swarm Optimization.

  • Review of multi-view subspace clustering   Order a copy of this article
    by Jing Xia 
    Abstract: Multi-view subspace clustering is a type of subspace clustering which combines with multi-view learning.It can not only deal with the challenges of the big data and high dimensions,but also cluster multi-view data from multiple sources and observation angles according to some similarity measurement.Our paper is to introduce the theoretical basis and latest research progress of multi-view subspace clustering.The purpose of this paper is for beginnners to quickly know about the research status of multi-view subspace clustering and some ideas of typical algorithms.
    Keywords: multi-view subspace clustering; subspace clustering; self-representation; spectral clustering-based methods; data Mining.