scholarly journals A machine learning approach to Bayesian parameter estimation

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Samuel Nolan ◽  
Augusto Smerzi ◽  
Luca Pezzè
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Bayesian Estimation ◽  
Theoretical Study ◽  
Adaptive Protocols ◽  
Calibration Methods ◽  
Machine Learning Approach ◽  
Standard Calibration ◽  
Model Independent ◽  
The Given

AbstractBayesian estimation is a powerful theoretical paradigm for the operation of the approach to parameter estimation. However, the Bayesian method for statistical inference generally suffers from demanding calibration requirements that have so far restricted its use to systems that can be explicitly modeled. In this theoretical study, we formulate parameter estimation as a classification task and use artificial neural networks to efficiently perform Bayesian estimation. We show that the network’s posterior distribution is centered at the true (unknown) value of the parameter within an uncertainty given by the inverse Fisher information, representing the ultimate sensitivity limit for the given apparatus. When only a limited number of calibration measurements are available, our machine-learning-based procedure outperforms standard calibration methods. Our machine-learning-based procedure is model independent, and is thus well suited to “black-box sensors”, which lack simple explicit fitting models. Thus, our work paves the way for Bayesian quantum sensors that can take advantage of complex nonclassical quantum states and/or adaptive protocols. These capabilities can significantly enhance the sensitivity of future devices.

scholarly journals Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 101 ◽  
Author(s):  
Michael Schultz ◽  
Stefan Reitmann
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Learning Approach ◽  
Neural Network Approach ◽  
Machine Learning Approach ◽  
Long Short Term Memory ◽  
Status Data ◽  
Passenger Behavior ◽  
The Given ◽  
Operational Data

In this paper we address the prediction of aircraft boarding using a machine learning approach. Reliable process predictions of aircraft turnaround are an important element to further increase the punctuality of airline operations. In this context, aircraft turnaround is mainly controlled by operational experts, but the critical aircraft boarding is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. Thus, we used a developed complexity metric to evaluate the actual boarding progress and a machine learning approach to predict the final boarding time during running operations. A validated passenger boarding model is used to provide reliable aircraft status data, since no operational data are available today. These data are aggregated to a time-based complexity value and used as input for our recurrent neural network approach for predicting the boarding progress. In particular we use a Long Short-Term Memory model to learn the dynamical passenger behavior over time with regards to the given complexity metric.

scholarly journals Predicting Global Ship Demolition Using Machine Learning Approach

2021 ◽  
Author(s):  
SM Mizanur Rahman
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Asian Countries ◽  
Business Decisions ◽  
Leakage Effect ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
Vessel Count ◽  
Near Future ◽  
The Given

Abstract:Global ship demolition is mostly concentrated in south Asian countries, namely Bangladesh, India, Pakistan and China, since 1990’s, having competitive advantage for their high natural tide, and low environmental and social costs. Due to high social and environmental externalities, stakeholders increase monitoring of the externalities and continue to prescribe improvement towards sustainability, which put pressures on profitability and competitiveness. As a consequence, also seen in the past, a leakage effect may emerge, leading to shift of this activity to a region, with relatively less monitored and less stricter on social and environmental impacts. Unfortunately, the leakage effect is never predicted in shipbreaking in order to understand the level of push compatible in the given socio-economic contexts. In this study, we have attempted to predict the future ship demolition landscape, applying machine learning technique to 34,531 in-service vessels worldwide, larger than 500 gross tonnage (GT), which is run against a learning model based on 3500 demolished vessels from 2014. This study shows that redistribution may occur among the top recycling nations: India may emerge out to be a dominant player in shipbreaking, surpassing Bangladesh by a margin of two-fold, while Pakistan and China are in decreasing trend. In addition, the leakage effect is observed, in that Vietnam is predicted to be the fourth largest ship demolition country, while China and Pakistan recede from the third and fourth place to 6th and 8th. Turkey is predicted to advance from fifth position to third position by vessel count but stays same in term of total GT dismantled. Although it is not clear if any leakage is to be observed in the near future, this study may be a model for future predictive analytics and help stakeholders take evidence-based business decisions.

scholarly journals GUI Based Prediction of Diabetes Stages by Finding the Accuracy using Machine Learning Approach

2021 ◽  
pp. 40-43
Author(s):  
R. Shanthi ◽  
V.Vinoth Kumar
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Diabetic Patients ◽  
Model Parameters ◽  
Critical Tension ◽  
Machine Learning Approach ◽  
Few Data ◽  
Prediction Of Diabetes ◽  
The Given

In 2050, the world's diabetic patients will arrive at 642 million, which implies that one of the ten grown-ups later on is experiencing diabetes. Diabetes mellitus (DM) is characterized as a gathering of metabolic issues applying critical tension on human wellbeing around the world. DM is a persistent sickness portrayed by hyperglycemia and it might cause numerous inconveniences. To forestall this issue, to break down the given medical clinic dataset by directed AI technique(SMLT) with catch a few data resembles, variable ID, uni-variate examination, bi-variate and multi-variate investigation, missing worth therapies and dissect the information approval, information cleaning/getting ready and information perception will be done on the whole given dataset. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in prediction of diabetic patients by given attributes of dataset with evaluation of GUI based user interface diabetes attribute prediction. Additionally, it observes to lead an increase the highest accuracy in diabetic prediction of attributes by a significantly better classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and F1 Score.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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