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Patent Agency Ranking
公开(公告)号：US20230128462A1
公开(公告)日：2023-04-27
申请号：US17510458
申请日：2021-10-26
Inventor： Md. Rafiul HASSAN , Muhammad Imtiaz HOSSAIN
Abstract: 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.
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公开(公告)号：US20240364719A1
公开(公告)日：2024-10-31
申请号：US18768090
申请日：2024-07-10
Inventor： Md. Rafiul HASSAN , Muhammad Imtiaz HOSSAIN
CPC classification number: H04L63/1416 , G06N20/00
Abstract: 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.
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公开(公告)号：US20240364718A1
公开(公告)日：2024-10-31
申请号：US18768082
申请日：2024-07-10
Inventor： Md. Rafiul HASSAN , Muhammad Imtiaz HOSSAIN
CPC classification number: H04L63/1416 , G06N20/00
Abstract: 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.
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公开(公告)号：US20240364717A1
公开(公告)日：2024-10-31
申请号：US18768079
申请日：2024-07-10
Inventor： Md. Rafiul HASSAN , Muhammad Imtiaz HOSSAIN
CPC classification number: H04L63/1416 , G06N20/00
Abstract: 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.
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公开(公告)号：US20230186126A1
公开(公告)日：2023-06-15
申请号：US16791571
申请日：2020-02-14
Inventor： Md Rafiul HASSAN , Muhammad Imtiaz HOSSAIN , Abdulazeez ABDULRAHEEM
CPC classification number: G06N7/01 , G06N3/086 , G01N15/08 , E21B49/00 , G01N33/246
Abstract: 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.
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