King Fahd University of Petroleum and Minerals

Muhammad Imtiaz Hossain

Md Rafiul Hassan

Abdulazeez Abdulraheem

Lahouari Ghouti

Moustafa Elshafei

Amar Khoukhi

Tarek Ahmed Helmy El-Basuny

Syed Masiur Rahman

A hybrid Hidden Markov Model (HMM) and Machine Learning (ML) systems and apparatus for classification in the case of data instances with imbalanced class distribution, including a Hidden Markov Model for generating a log-likelihood score for each data instance. Implementations of the hybrid system and method detect fraudulent activity and classifies documents with accuracy that surpasses conventional classifiers. In one implementation, Hidden Markov Model (HMM) for generating a log-likelihood score based on an attribute value vector for a set of keyword features characterizing a Web page. In one implementation, the HMM generates a log-likelihood score based on an attribute value vector for page layout characterizing a document image. Resulting attribute value vectors are ranked and divided into bins grouped by log-likelihood scores within equal ranges. Various machine learning models are trained using the balanced vectors obtained by accumulating from all the bins of vectors.

Document image classifying system

System for detecting web page fraud based on wordlist categorization

Method for web page fraud activity

Hidden Markov model based data ranking for enhancement of classifier performance to classify imbalanced dataset

Systems, methods, and apparatuses are provided for permeability prediction. The method acquires data associated with one or more geological formations, calculates, using processing circuitry and a trained Hidden Markov model, log-likelihood values to group the data into a plurality of clusters, and trains an artificial neural network for each of the plurality of clusters when the mode of operation is training mode. Further, the method acquires one or more formation properties corresponding to a geological formation, determines using the trained Hidden Markov model, a log-likelihood score associated with the one or more formation properties, identifies a cluster associated with the one or more formation properties as a function of the log-likelihood score, and predicts a permeability based at least in part on the one or more formation properties and a trained artificial neural network associated with the identified cluster when the mode of operation is forecasting mode.

Geological formation permeability prediction system

Method for predicting permeability and oil content in a geological formation

Apparatuses, systems, and methodologies for permeability prediction

The method of predicting gas composition in a multistage separator includes solutions to the regression problem of gas composition prediction that are developed using an ensemble of hybrid computational intelligence (CI) models. Three separate homogeneous and one heterogeneous ensemble of hybrid computational intelligence (EHCI) models are developed using a parallel scheme. The homogeneous models have the same types of CI models used as base learners, and the heterogeneous model has of different types of CI models used as base learners. Various popular CI models, including multi-layer perceptron (MLP), support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), are used as base learners of ensemble models.

Method of predicting gas composition

Growing community of inventors

Dhahran, Saudi Arabia

Muhammad Imtiaz Hossain