A Novel Fuzzy Clustering Algorithm for Radial Basis Function Neural Network

A Fuzzy Radial basis function neural network (FRBFNN) classifier is proposed in the framework of Radial basis function neural network (RBFNN). This classifier is constructed using class-specific fuzzy clustering to form the clusters which represent the neurons i.e. fuzzy set hyperspheres (FSHs) in the hidden layer of FRBFNN. The creation of these FSHs is based on the maximum spread from inter-class information and intra-class fuzzy membership mechanism. The proposed approach is fast, independent of parameters, and shows good data visualization. The Least mean square training between the hidden layer to output layer in RBFNN is avoided, thus reduces the time complexity. The FRBFNN is trained quickly due to the fast converge of input data to form the FHSs in the hidden layer. The output is determined by the union operation of the FHSs outputs which are connected to the class nodes in the output layer. The performance of the proposed FRBFNN is compared with the other RBFNNs using ten benchmark datasets. The empirical findings demonstrate that the proposed FRBFNN is highly efficient classifier for pattern recognition

MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network

Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased ‘Threat’ of an ‘Attack’. Attackers are targeting these devices, as it may provide an easier ‘backdoor entry to the users’ network’.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method

Development and Validation of a Knowledge Management Capability Assessment Model

Although many assessment tools and methodologies for measuring knowledge management capabilities are becoming available in the practitioner world, none of them has been tested for validity. In this paper, we first present a knowledge management capability assessment (KMCA) methodology for determining the capability levels of an organization in various knowledge areas. The KMCA defines the knowledge capability areas and a five-level metric for assessing capabilities within each area. We then present the results of an empirical study conducted to validate the ability of the KMCA methodology to correctly ascertain capability levels within knowledge areas. The validation consists of two different tests: The first test, called the absolute test, validates the five-level metric within the KMCA by showing that a lower capability level is a prerequisite for achieving the next higher level. The second test, called the relative test, demonstrates the ability of the KMCA to compare relative capabilities (1) across knowledge areas within a single organization and (2) across multiple organizations for a given knowledge area. The KMCA was developed in concert with a leading manufacturing company in the semiconductor industry. The data for this study was collected from over 700 knowledge workers from multiple large organizational units within the company. The results show that the KMCA is robust, in that it is able to correctly estimate the capabilities of the knowledge areas it was designed to measure

Development and Validation of a BI Success Model

We propose and test a BI Success model, based on DeLone and McLean’s IS Success model, that incorporates comprehensive data that is needed for decision-making and computer systems that allow integration and analysis of that data as dimensions of BI success. Our model also includes organizational support structure for BI and the users’ involvement in the ongoing development of BI systems as contributing factors. Data collected from over 300 organizations across the world confirmed 7 of 9 hypothesized relationships. Notably, user involvement and the organizational support factors are seen to be associated with the BI capability factors which, in turn, are positively associated with users’ perception of net benefits and their satisfaction with BI practices. This is one of the first studies that evaluates the success of BI at organizational level and considers user involvement, characterized by on-going configuration / customization / improvement cycle, as a contributing factor in the classic IS Success model

Software Reuse Success Factors: A Qualitative Assessment of Developers\u27 Perception

Systematic reuse is becoming an increasingly accepted way to improve software development productivity and quality. The implementation of a software reuse methodology requires substantial investments for the company. The factors that contribute to the overall success of reuse for an organization have been examined in prior research. However, even in organizations that are successful in employing reuse, some projects fail to achieve the targeted amounts of reuse. This suggests that there are other factors beyond the overall organizational factors affecting the success of software reuse in projects. This research explores factors that affect reuse success of individual projects in software development. We assess the developers’ perception of the project level factors in an environment in which systematic software reuse is conducted successfully. We believe that an organization that can identify the factors affecting potential software reuse will be able to better target investments in the improvement of reuse methodology and thus influence the software productivity and quality