On the usage of the probability integral transform to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems

We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems. The proposed algorithm builds a fixed number of fuzzy sets for all variables and adjusts their shape and position to the real distribution of training data. A two-step process is applied : 1) transformation of the original distribution into a standard uniform distribution by means of the probability integral transform. Since the original distribution is generally unknown, the cumulative distribution function is approximated by computing the q-quantiles of the training set; 2) construction of a Ruspini strong fuzzy partition in the transformed attribute space using a fixed number of equally distributed triangular membership functions. Despite the aforementioned transformation, the definition of every fuzzy set in the original space can be recovered by applying the inverse cumulative distribution function (also known as quantile function). The experimental results reveal that the proposed methodology allows the state-of-the-art multi-way fuzzy decision tree (FMDT) induction algorithm to maintain classification accuracy with up to 6 million fewer leaves.Comment: Appeared in 2018 IEEE International Congress on Big Data (BigData Congress). arXiv admin note: text overlap with arXiv:1902.0935

An Overview of Vertical Handoff Decision Algorithms in NGWNs and a new Scheme for Providing Optimized Performance in Heterogeneous Wireless Networks

Because the increasingly development and use of wireless networks and mobile technologies, was implemented the idea that users of mobile terminals must have access in different wireless networks simultaneously. Therefore one of the main interest points of Next Generation Wireless Networks (NGWNs), refers to the ability to support wireless network access equipment to ensure a high rate of services between different wireless networks. To solve these problems it was necessary to have decision algorithms to decide for each user of mobile terminal, which is the best network at some point, for a service or a specific application that the user needs. Therefore to make these things, different algorithms use the vertical handoff technique. Below are presented a series of algorithms based on vertical handoff technique with a classification of the different existing vertical handoff decision strategies, which tries to solve these issues of wireless network selection at a given time for a specific application of an user. Based on our synthesis on vertical handoff decision strategies given below, we build our strategy based on solutions presented below, taking the most interesting aspect of each one.Vertical Handoff, Genetic Algorithms, Fuzzy Logic, Neural Networks, AHP

A cognitive approach for evaluating the usability of Storage as a Service in Cloud Computing Environment

Cloud computing is a style of computing which thrives users requirements by delivering scalable, on-demand and pay-per-use IT services. It offers different service models, out of which Storage as a Service (StaaS) is the fundamental block of Infrastructure cloud that fulfills user’s excess demand of elastic computing resources.  But considering the competitive business scenario choosing the best cloud storage provider is a difficult task. Thus, usability is considered to be the key performance indicator which evaluates the better cloud storage based on user’s satisfaction. This paper aims to focus on the usability evaluation of StaaS providers namely Google drive, Drop box and One drive. This paper proposed a fuzzy based AHP model for measuring user satisfaction. Usability evaluation is carried out based on user feedback through Interview and Questionnaire method. Analysis of user feedback is done based on the fuzzy approach in order to remove vaguness. Whereas, AHP model is used for measuring satisfaction degree of the different cloud storage services and it solves the problem of selecting best cloud storage

Vehicle level health assessment through integrated operational scalable prognostic reasoners

Today’s aircraft are very complex in design and need constant monitoring of the systems to establish the overall health status. Integrated Vehicle Health Management (IVHM) is a major component in a new future asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimising downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics and diagnostics engine and providing recommended maintenance actions. The data driven prognostics methods usually use a large amount of data to learn the degradation pattern (nominal model) and predict the future health. Usually the data which is run-to-failure used are accelerated data produced in lab environments, which is hardly the case in real life. Therefore, the nominal model is far from the present condition of the vehicle, hence the predictions will not be very accurate. The prediction model will try to follow the nominal models which mean more errors in the prediction, this is a major drawback of the data driven techniques. This research primarily presents the two novel techniques of adaptive data driven prognostics to capture the vehicle operational scalability degradation. Secondary the degradation information has been used as a Health index and in the Vehicle Level Reasoning System (VLRS). Novel VLRS are also presented in this research study. The research described here proposes a condition adaptive prognostics reasoning along with VLRS