In this paper a robust consensus-based ensemble assisted multi-feature learnt social media link prediction model is developed. Unlike classical methods, multi-level enhancement paradigm was considered where at first the focus made on extracting maximum possible features depicting inter-node relationship for high accuracy of prediction. Considering robustness different feature sets, we extracted...

Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI urban areas, which requires understanding spatiotemporal trends areas and driving forces influencing their pattern. Traditional GIS-based methods, used determine the greening potential of vacant land are incapable predicting future scenarios based on past trend. Therefore, we propo...

Computers have evolved over the years, and as evolution continues, we been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy, industry to communicate with each other. This is known Internet of Things (IoT). IoT several benefits a country’s economy’s health, transportation, agriculture sectors. These enormous benefits, coupled computati...

Soil nutrients are a vital part of soil fertility and other environmental factors. testing is an efficient tool used to evaluate the existing nutrient levels aid compute appropriate quantity depending upon level crop requirements. Since conventional models not feasible in real time applications, nutrient, potential hydrogen (pH) prediction essential improve overall productivity. In this aspect,...

Ensemble methods such as boosting combine multiple learners to obtain better prediction than could be obtained from any individual learner. Here we propose a principled framework for directly constructing ensemble learning methods from kernel methods. Unlike previous studies showing the equivalence between boosting and support vector machines (SVMs), which needs a translation procedure, we show...

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemb...

This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace...

Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are ...

It is widely recognized that knowledge discovery and data mining in the health domain are two techniques than scientists and researchers are always looking into areas for improvements and accurateness in prediction. In this paper, we present a multi-tier knowledge acquisition, amalgamation and learning info-structure for the learning of rules that have been generated from medical datasets compr...