International Journal of Collaborative Intelligence
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International Journal of Collaborative Intelligence (3 papers in press)
CCS Architectonic Design Refactoring as a potential solution to alienate the AE and Architectural scope creep A Case Study by Manoj Kumar M Abstract: Our earlier work (Manu A R, et. al, 2013, ManojKumar M et. al, 2014, V K Agrawal et. al, 2016, Nandakumar A.N et. al, 2012,) discussed the cloud structures and various security issues in crowd sourced multilateral cloud computing system such as architectural entropy, architectural smells, test debt, security debt, AD decay, AD degeneracy and technical debt etc. The discussion made in the earlier works [6-8] revealed that there is an inevitable demand for the identification of cloud security smells and various associated tasks so as to enable fallow-less and faultless cloud system design. Understanding stakeholders requirements and various mistakes made during the process design and optimization can be done where the focus can be made on alleviating the problems raised during transaction process. In this work a snippet of refactoring concept is provided. However, realizing the efficacy of refactoring towards Architecture entropy (AE) identification and removal in this work, a detailed discussion of refactoring and its implementation is presented. Realizing the probability of architectural entropy and smells, and it relation to architectural scope creep resulting adversaries particularly in terms of security breaches, in this work, with this phase it is intended to exploit the concept of refactoring and testing to assess architectural entropy and alleviate the same. Keywords: cloud computing Architecture entropy; cloud computing architecture degradation; cloud computing architecture erosion; cloud computing architecture decay; cloud computing architecture depravation;.
An Assessment of Classification with Hybrid Methodology for Neural Network Classifier against different classifier 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 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.