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Hybrid Improved Document-level Embedding (HIDE)
In this work we propose HIDE a Hybrid Improved Document level Embedding which incorporates domain information, parts of speech information and sentiment information into existing word embeddings such as GloVe and Word2Vec.
Earned Benefit Maximization in Social Networks Under Budget Constraint
In this paper, we study this problem with a variation, where a set of nodes are designated as target nodes, each of them is assigned with a benefit value, that can be earned by influencing them, and our goal is to maximize the earned benefit by initially activating a set of nodes within the budget.
Social and Information Networks Data Structures and Algorithms Multiagent Systems
A Survey on Influence Maximization in a Social Network
Given a social network with diffusion probabilities as edge weights and an integer k, which k nodes should be chosen for initial injection of information to maximize influence in the network?
Social and Information Networks
A novel multiclassSVM based framework to classify lithology from well logs: a real-world application
Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one or one-against-all strategies.
Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS
The classification results have been further fine-tuned applying expert knowledge based on the relationship among predictor variables i. e. well logs and target variable - oil saturation.
A Novel Framework based on SVDD to Classify Water Saturation from Seismic Attributes
Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization.
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset
Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset.
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability.
Well Tops Guided Prediction of Reservoir Properties using Modular Neural Network Concept A Case Study from Western Onshore, India
The data set used in this study comprising three seismic attributes and well log data from eight wells, is acquired from a western onshore hydrocarbon field of India.
