38 citations · 95 across the 9 of their papers we have counts for
9 papers
Communication Trade-offs in Federated Learning of Spiking Neural Networks
Soumi Chaki, David Weinberg, Ayca Özcelikkale
Spiking Neural Networks (SNNs) are biologically inspired alternatives to conventional Artificial Neural Networks (ANNs). Despite promising preliminary results, the trade-offs in th…
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset
Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray +2
Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due t…
A Novel Framework based on SVDD to Classify Water Saturation from Seismic Attributes
Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray +2
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir…
A novel multiclassSVM based framework to classify lithology from well logs: a real-world application
Soumi Chaki, Aurobinda Routray, William K. Mohanty +1
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classif…
Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS
Soumi Chaki, Aurobinda Routray, William K. Mohanty +1
This paper presents the development of a hybrid learning system based on Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and domain knowledge to solve…
Well Tops Guided Prediction of Reservoir Properties using Modular Neural Network Concept A Case Study from Western Onshore, India
Soumi Chaki, Akhilesh K Verma, Aurobinda Routray +2
This paper proposes a complete framework consisting pre-processing, modeling, and post-processing stages to carry out well tops guided prediction of a reservoir property (sand frac…
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Akhilesh K Verma, Soumi Chaki, Aurobinda Routray +2
In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency u…
A Novel Pre-processing Scheme to Improve the Prediction of Sand Fraction from Seismic Attributes using Neural Networks
Soumi Chaki, Aurobinda Routray, William K. Mohanty
This paper presents a novel pre-processing scheme to improve the prediction of sand fraction from multiple seismic attributes such as seismic impedance, amplitude and frequency usi…
Reservoir Characterization: A Machine Learning Approach
Soumi Chaki
Reservoir Characterization (RC) can be defined as the act of building a reservoir model that incorporates all the characteristics of the reservoir that are pertinent to its ability…