Data-Driven Ship Propulsion Modeling with Artificial Neural Networks

2021 ◽  
Author(s):  
Pavlos Karagiannidis ◽  
Nikolaos Themelis
Keyword(s):  
Neural Networks ◽  
Data Driven ◽  
Actual Condition ◽  
Data Set ◽  
Prediction Ability ◽  
Ship Propulsion ◽  
Preparation Stage ◽  
Shaft Power ◽  
Feed Forward Neural Networks ◽  
Reduction Of Emissions

The paper examines data-driven techniques for the modeling of ship propulsion that could support a strategy for the reduction of emissions and be utilized for the optimization of a fleet’s operations. A large, high-frequency and automated collected data set is exploited for producing models that estimate the required shaft power or main engine’s fuel consumption of a container ship sailing under arbitrary conditions. A variety of statistical calculations and algorithms for data processing are implemented and state-of-the-art techniques for training and optimizing Feed-Forward Neural Networks (FNNs) are applied. Emphasis is given in the pre-processing of the data and the results indicate that with a proper filtering and preparation stage it is possible to significantly increase the model’s accuracy. Thus, increase our prediction ability and our awareness regarding the ship's hull and propeller actual condition.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

scholarly journals Data-Driven Chemical Reaction Classification with Attention-Based Neural Networks

2019 ◽  
Author(s):  
Philippe Schwaller ◽  
Alain C. Vaucher ◽  
Vishnu H Nair ◽  
Teodoro Laino
Keyword(s):  
Neural Networks ◽  
Chemical Reactions ◽  
Chemical Reaction ◽  
Structural Rearrangement ◽  
Data Driven ◽  
Classification Task ◽  
Data Set ◽  
Atom Mapping ◽  
Multi Class Classification ◽  
Insight Into

<div><div><div><p>Organic reactions are usually clustered in classes that collect entities undergoing similar structural rearrangement. The classification process is a tedious task, requiring first an accurate mapping of the rearrangement (atom mapping) followed by the identification of the corresponding reaction class template. In this work, we present a transformer-based model that infers reaction classes from the SMILES representation of chemical reactions. The model reaches an accuracy of 93.8 % for a multi-class classification task involving several hundred different classes. The attention weights provided by the model give an insight into what parts of the SMILES strings are taken into account for classification, based solely on data. We study the incorrect predictions of our model and show that it uncovers different biases and mistakes in the underlying data set.</p></div></div></div>

Modeling highway runoff pollutant levels using a data driven model

2009 ◽  
Vol 60 (1) ◽  
pp. 19-28 ◽  
Author(s):  
T. Opher ◽  
A. Ostfeld ◽  
E. Friedler
Keyword(s):  
Management Strategies ◽  
Training Data ◽  
Data Driven ◽  
Environmental Research ◽  
Good Prediction ◽  
Runoff Water ◽  
Highway Runoff ◽  
Data Set ◽  
Prediction Ability ◽  
Road Pavement

Pollutants accumulated on road pavement during dry periods are washed off the surface with runoff water during rainfall events, presenting a potentially hazardous non-point source of pollution. Estimation of pollutant loads in these runoff waters is required for developing mitigation and management strategies, yet the numerous factors involved and their complex interconnected influences make straightforward assessment almost impossible. Data driven models (DDMs) have lately been used in water and environmental research and have shown very good prediction ability. The proposed methodology of a coupled MT-GA model provides an effective, accurate and easily calibrated predictive model for EMC of highway runoff pollutants. The models were trained and verified using a comprehensive data set of runoff events monitored in various highways in California, USA. EMCs of Cr, Pb, Zn, TOC and TSS were modeled, using different combinations of explanatory variables. The models' prediction ability in terms of correlation between predicted and actual values of both training and verification data was mostly higher than previously reported values. PbTotal was modeled with an outcome of R2 of 0.95 on training data and 0.43 on verification data. The developed model for TOC achieved R2 values of 0.91 and 0.49 on training and verification data respectively.

scholarly journals Least-Squares Monte Carlo for Proxy Modeling in Life Insurance: Neural Networks

Risks ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 116
Author(s):  
Anne-Sophie Krah ◽  
Zoran Nikolić ◽  
Ralf Korn
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monte Carlo ◽  
Least Squares ◽  
Capital Requirements ◽  
Sensitivity Analyses ◽  
Approximation Technique ◽  
Data Set ◽  
Least Squares Monte Carlo ◽  
Feed Forward Neural Networks

The least-squares Monte Carlo method has proved to be a suitable approximation technique for the calculation of a life insurer’s solvency capital requirements. We suggest to enhance it by the use of a neural network based approach to construct the proxy function that models the insurer’s loss with respect to the risk factors the insurance business is exposed to. After giving a mathematical introduction to feed forward neural networks and describing the involved hyperparameters, we apply this popular form of neural networks to a slightly disguised data set from a German life insurer. Thereby, we demonstrate all practical aspects, such as the hyperparameter choice, to obtain our candidate neural networks by bruteforce, the calibration (“training”) and validation (“testing”) of the neural networks and judging their approximation performance. Compared to adaptive OLS, GLM, GAM and FGLS regression approaches, an ensemble built of the 10 best derived neural networks shows an excellent performance. Through a comparison with the results obtained by every single neural network, we point out the significance of the ensemble-based approach. Lastly, we comment on the interpretability of neural networks compared to polynomials for sensitivity analyses.

scholarly journals Data-Driven Chemical Reaction Classification, Fingerprinting and Clustering using Attention-Based Neural Networks

2019 ◽  
Author(s):  
Philippe Schwaller ◽  
Daniel Probst ◽  
Alain C. Vaucher ◽  
Vishnu H Nair ◽  
Teodoro Laino ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reaction Center ◽  
Chemical Reactions ◽  
Chemical Reaction ◽  
Classification Accuracy ◽  
Similarity Search ◽  
Classification Model ◽  
Data Driven ◽  
Data Set ◽  
Atom Mapping

<p>Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. The classification process is a tedious task, requiring first an accurate mapping of the reaction (atom mapping) followed by the identification of the corresponding reaction class template. In this work, we present two transformer-based models that infer reaction classes from the SMILES representation of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We study the incorrect predictions of the models and show that they reveal different biases and mistakes in the underlying data set. Using the embeddings of our classification model, we introduce reaction fingerprints that do not require knowing the reaction center or distinguishing between reactants and reagents. This conversion from chemical reactions to feature vectors enables efficient clustering and similarity search in the reaction space. We compare the reaction clustering for combinations of self-supervised, supervised, and molecular shingle-based reaction representations.</p>

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98 ◽  
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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