In real world situations every model has some weaknesses and will make errors on training data. Given the fact that each model has certain limitations, the aim of ensemble learning is to supervise their strengths and weaknesses, leading to best possible decision in general. Ensemble based machine learning is a solution of minimizing risk in decision making. Bagging, boosting, stacked generalization and mixture of expert methods are the most popular techniques to construct ensemble systems. For the purpose of combining outputs of class labels, weighted majority voting, behaviour knowledge space and border count methods are used to construct independent classifiers and to achieve diversity among the classifiers which is important in ensemble learning. It was found that an ideal ensemble method should work on the principal of achieving six paramount characteristics of ensemble learning; accuracy, scalability, computational cost, usability, compactness and speed of classification. In addition, the ideal ensemble method would be able to handle large huge image size and long term historical data particularly of spatial and temporal. In this paper we reveal that ensemble models have obtained high acceptability in terms of accuracy than single models. Further, we present an analogy of various ensemble techniques, their applicability, measuring the solution diversity, challenges and proposed methods to overcome these challenges without diverting from the original concepts.

This study is concerned with computation methods for environmental data analysis in order to enable facilitate effective decision making when addressing air pollution problems. A number of environmental air pollution studies often simplify the problem but fail to consider the fact that air pollution is a spatio-temporal problem. This research addresses the air pollution problem as spatio-temporal problem by proposing a new decentralized computational technique named Online Scalable SVM Ensemble Learning Method (OSSELM). Special consideration is given to distributed ensemble in order to resolve the spatio-temporal data collection problem i.e., the data collected from multiple monitoring stations dispersed over a geographical location. Moreover, the air pollution problem is address systematically including computational detection, examination of possible causes, and air-quality prediction.

The idea of programmable networks has recently re-gained considerable momentum due to the emergence of the Software-Defined Networking (SDN) paradigm. In this context SDN is being looked upon as a promising paradigm that has the power to change the way networking is done. SDN, often referred to as a “radical new idea in networking”, promises to dramatically simplify network management and enable innovation through network programmability. This paper surveys the state-of-the-art in programmable networks with an emphasis on SDN. We provide a historic perspective of programmable networks from early ideas to recent developments. Then we present the SDN architecture and the OpenFlow standard in particular, discuss current alternatives for implementation and testing of SDN-based protocols and services, examine current and future SDN applications, and explore promising research directions based on the SDN paradigm. In this survey, we are giving the overview of the software defined networking architecture and OpenFlow.