Titlebook: Cognitive Computing for Big Data Systems Over IoT; Frameworks, Tools an Arun Kumar Sangaiah,Arunkumar Thangavelu,Venkatesa Book 2018 The Ed

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African Island Migration: I. Walkerster nodes is given to enhance the clone detection probability rate. Here, the location and residual energy is taken into account to identify the presence of cloned attack nodes within the network. Finally, Gravitational Search Algorithm with global search ability is investigated to identify the clo

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Tina Thiessen,Sören Plönnigs,Wolfgang Mathisdeo data analysis, wireless sensor network, etc. are used to somewhat solve the congestion in the traffic and to manage the traffic smartly. But in the above said methods having some demerits like much time to take for installation, maintenance cost is very high. Actually, our objective is to develo

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Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Thingring systems, smart watches, vending machines, smart meters, city traffic, building security systems and much more like this. It has steered the automation to a new high. But IoT alone has limited capability. To reap the actual benefit of IoT, it has to be intelligent. In this chapter, we have reaso

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Modelling and Analysis of Multi-objective Service Selection Scheme in IoT-Cloud Environment,omposed of thousands distributed entities and a number of multimedia smart devices. In recent years, due to the improvement of popularity and capability of smart mobile devices, Mobile Cloud Computing (MCC) gains a considerable attention in Internet of Things (IoT) environment. As there are variety

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,Cognitive Data Science Automatic Fraud Detection Solution, Based on Benford’S Law, Fuzzy Logic withntribute to very challenging area for data science method applying. Traditional approach of predictive modelling became insufficient, because relaying on few variables as a base of the fraud model are very fragile concept. Reason for that is fact that we are talking about portfolio with low cases of